We are going to use the cloud processing platform Copernicus Data Space Ecosystem to carry out our exercises. It’s a cloud platform hosted by the European Commission, ESA and Copernicus.The processing will happen there and you will get 1000 free credits. That’s enough to complete the course.
Here is a guide how to find your way around in the Coding Environment JupyterHub. You will see a button that forwards you there whenever there is a hands-on exercise to do.
In a first step we connect to the cloud platform. We can only see information and use functionality that is available to everybody.
+We can see for example which collections and processes are available, but we cannot process data. We will explore the platforms capabilities in an extra exercise in more depth later.
After we have connected to the platform and want more functionality, we need to login. This means we authenticate ourselfs to prove we are an registered user.
+After access is granted we can also process data. Every computation comes at a cost, this is why every user has an amount of credits (which usually have to be payed) for computing.
+Everytime you are going to use the cloud platform for processing you will have to login at the beginning of your workflow. We are going to learn how to create a workflow in a seperat exercise later on.
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Now let’s just log in… (this only works if you haver registered to CDSE as described in the lesson Introduction)
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conn.authenticate_oidc()
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… and check if the login has worked…
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conn
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… and let’s check our user information, which is possible since we have authenticated now.
This is all. We have verified that we can connect and login to the cloud platform. We will do this again later on when we’ll start with some hands on exercises.
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For now let’s return to EOCollege to get started with the lessons!
Traditional approaches for the analysis of Earth Observation (EO) data typically involve several steps, including data discovery, data download, data pre-processing, and data analysis. Especially when working with multiple datasets, handling data discovery, download, and access is a tremendous task, where users need to navigate through different interfaces, adhere to varying access requirements, and manage the heterogeneity of data formats. This approach is often time-consuming and requires significant effort to aggregate and harmonize datasets from different providers for comprehensive analysis.
In the field of Earth Observation, satellite missions like Sentinel-2 provide vast amounts of data that play a crucial role in various applications, including environmental monitoring, land cover mapping, and climate analysis. Understanding the volume of data involved in an analysis is critical for efficient data processing. EO datasets can span terabytes and petabytes, making it impractical to store, manage, and process them entirely on a local computer.
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The increasing availability of vast amounts of EO data from multiple satellites presents challenges in terms of the time required for data download and pre-processing on individual computers or infrastructures. Within the Copernicus program of the European Union, around 64 million products have been published, which sums up to more than 25 Petabyte of data volume. In the following slider we have collected some statistics about the amounts of EO data from the Sentinel satellites.
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Figure: EO data volume and the limits of your computer.
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The following interactive exercise assists in estimating the data volume associated with Sentinel-2 data. This calculator allows users to gain insights into the data volumes involved in specific regions and time ranges, further emphasizing the relevance of using EO platforms for scientific analyses. Not convinced of clouds yet? Try the volume calculator below to asses how much space you have to free up on your hard drive for your next project.
Cloud infrastructure and platforms have emerged as viable alternatives to the traditional approach of data analyses as described in Figure “EO research without cloud facilities”. These solutions combine data storage and compute resources, enabling users to conduct their data analysis in close proximity to the data itself. By leveraging cloud-based infrastructures, researchers and analysts can optimize their workflow by minimizing the time-consuming steps of data transfer and pre-processing, thereby allowing them to focus more efficiently on data analysis tasks.
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By utilizing cloud-based resources, users can harness the scalability and flexibility of these platforms to handle the extensive datasets generated by EO missions. Cloud-based EO platforms represent a paradigm shift in EO data analysis, offering a comprehensive ecosystem that seamlessly integrates storage, processing, analysis tools, collaboration, and visualization. These platforms empower users to overcome the challenges posed by large-scale EO data and accelerate scientific advancements in various fields, including environmental monitoring, climate studies, natural resource management, and disaster response.
Cloud-based EO infrastructures and platforms have emerged to meet the growing demand for efficient data processing, analysis, and visualization close to the data. We can distinguish between infrastructure providers and platform providers.
EO-based infrastructure providers focus on offering the underlying infrastructure necessary for processing, storage, and dissemination of EO data. They provide the computing resources, storage capacity, and networking capabilities required to handle large-scale EO data processing and analysis. These providers often build and maintain data centers and server clusters, ensuring reliable and scalable infrastructure for EO applications.
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In comparison to the traditional approach (Figure “EO research without cloud facilities”), this approach allow users to use computing resources close to the EO data (see Figure “EO cloud providers”). Users do not need to download and manage EO data on their own, they can simply use what is already available on the infrastructure. However, there are no EO-specific services for data discovery, access, visualization, and analysis.
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Figure: EO cloud providers.
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Examples of EO-based infrastructure providers
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Amazon Web Services (AWS): AWS offers a wide range of cloud services, including storage (Amazon S3) and computing (Amazon EC2), which can be leveraged for EO data processing and storage. Various open data are available on AWS (https://registry.opendata.aws/).
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Google Cloud Platform (GCP): GCP provides infrastructure services like Google Cloud Storage and Google Compute Engine, which can be utilized for EO data management and analysis. Various open data are available on GCP (https://cloud.google.com/datasets).
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Microsoft Azure: Azure offers cloud-based services such as Azure Storage, Azure Virtual Machines, and Azure Machine Learning, enabling EO applications and workflows.
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Open Telekom Cloud: Open Telekom Cloud is a cloud platform offered by Deutsche Telekom. It provides scalable infrastructure resources, including computing, storage, and networking capabilities, suitable for processing and storing large volumes of EO data.
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Cloudferro: Cloudferro is a cloud infrastructure provider specializing in geospatial data processing and analysis. They offer scalable and secure cloud resources optimized for EO applications. Cloudferro provides high-performance computing, storage, and networking services tailored for EO data processing workflows. Various open data are available on Cloudferro (https://cloudferro.com/en/eo-cloud/storage-big-data/).
Platform providers focus on delivering comprehensive EO platforms that combine infrastructure, tools, and services into a cohesive environment. These platforms typically offer a suite of integrated capabilities, including data storage, processing, analysis, visualization, and collaboration tools. They provide a user-friendly interface and simplify the EO data lifecycle, enabling users to access, process, and analyze EO data without managing the underlying infrastructure.
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On top of providing the infrastructure which allows users to do the computations close to the EO data , making available a platform additionally enables the use of specific Application Programming Interfaces (APIs) for the discovery, access, visualization, exploitation, and analysis of EO data. EO platforms are often made available on infrastructure providers to benefit from the EO data storage. Users of a platform can now use harmonized interfaces for all data, which is available on the platform.
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Figure: EO platform providers.
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Examples of cloud-based EO platform providers
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Google Earth Engine is a platform specifically designed for EO data analysis. It provides access to a vast amount of satellite imagery and geospatial datasets, along with powerful processing capabilities and built-in algorithms.
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Sinergise Sentinel-Hub is a platform focused on accessing and processing satellite data. It provides APIs and easy-to-use tools for accessing, processing, and visualizing EO data.
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Microsoft Planetary Computer is a platform that combines geospatial data and AI capabilities for Earth observation. It provides access to various global datasets, including satellite imagery, climate data, and environmental data. The platform aims to facilitate large-scale data analysis and support sustainable development and conservation efforts.
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Euro Data Cube is a platform on top of various cloud infrastructures to provide an interactive development environment with a standardized access to various EO data. It provides a JupyterLab environment for data exploration and analysis, as well as capabilities to run processing jobs.
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OpenEO Platform is a platform based on OpenEO, which aims to standardize and simplify the access and processing of EO data. It provides a unified API and common data model, enabling interoperability across multiple EO data providers and processing backends. The platform allows users to run EO workflows on various cloud-based infrastructure providers.
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Pangeo is a community platform for big data geoscience built on the Pangeo ecosystem. It aims to foster collaboration among researchers, engineers, research software engineers, and innovators within the open-source scientific Python ecosystem, focusing on Ocean, Atmosphere, Land, and Climate science. There is a strong focus on portability and interoperability, enabling deployments of the Pangeo platform on various infrastructure (laptops, cloud providers, etc.) and providing APIs that allow users to prototype on their laptops and easily scale to the cloud or High-Performance Computers with minimal changes to their code. The current deployment of the Pangeo platform in Europe, Pangeo@EOSC is hosted on the European Open Science Cloud (EOSC), providing a scalable and collaborative environment for big data analysis and research for all European researchers and their collaborators.
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Different EO platform providers may offer end-users access to specific tools and packages that can be highly beneficial for particular workflows. However, users should carefully evaluate whether the convenience of these tools justifies the potential trade-off of being locked into a particular platform. In some cases, opting for platforms that prioritize portability and openness, like OpenEO or Pangeo, might be more advantageous, especially for those who value flexibility and long-term interoperability across various environments and infrastructures.
In summary, EO-based infrastructure providers primarily focus on providing the underlying infrastructure and resources, while platform providers offer integrated environments with a wide range of tools and services to support EO data processing, analysis, and visualization. These two types of providers complement each other in the EO ecosystem, enabling users to access and leverage EO data effectively.
Cloud-based EO platforms have transformed the way researchers and scientists analyze and utilize EO data. These platforms often follow a three-layered design (often named “tiers”) comprising infrastructure, services, and exploitation interfaces. An example architecture based on this approach is the “Earth Observation Exploitation Platform Common Architecture” (EOEPCA) of ESA (https://eoepca.org). Leveraging the power of cloud computing, EO platforms provide a comprehensive ecosystem that seamlessly integrates storage, processing, analysis tools, collaboration, visualization, and data exploitation capabilities.
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The following overview will explore each layer of the three-layered design and provide examples to illustrate the functionalities and benefits of cloud-based EO platforms in real-world applications. They showcase the diverse range of tools, services, and interfaces (also named “building blocks”) available to store, process, analyze, collaborate, visualize, and exploit EO data effectively within the cloud environment.
“The Resource Tier represents the hosting infrastructure and provides the EO data, storage and compute upon which the exploitation platform is deployed.” (Source: EOEPCA Master System Design)
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Data Storage: The data storage component may include distributed file systems like distributed parallel file systems (e.g., GPFS, Hadoop) or object storage services (e.g., Amazon S3, Google Cloud Storage) to securely store and manage EO datasets.
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Computing Resources: The computing component can provide virtual machines (e.g., Amazon EC2, Google Compute Engine, OpenStack), container environments (e.g., Docker-Engine, Kubernetes) or batch-computing systems (e.g., High Performance Computing) for executing data processing and analysis tasks on EO datasets.
“The Platform Tier represents the Exploitation Platform and the services it offers to end-users.” (Source: EOEPCA Master System Design)
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The services of the platform tier can be grouped into data-related and processing-related services. The processing tools and services often rely on the data services to get discover and get access to data available on the platform.
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Data Services
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Data Catalog: Data available on the platform needs to be described with metadata to be findable by users. Often processing and analysis services, such as Open Data Cube or OpenEO, make use of the data catalog to ease the use of EO data. These services enable users to annotate, search, and discover EO datasets based on various metadata parameters.
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Data Access Service: This enables users to retrieve and access EO datasets. This may involve APIs, protocols, or data transfer mechanisms like Open Geospatial Consortium (OGC) Web Services or HTTP services for efficient and secure data access.
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Data Visualization Service: The visualization component provides standardized web services for the visualization of raster and vector data available on the platform. User interfaces like QGIS or web mapping tools can be used together with those services.
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Data Processing and Analysis Tools: This service component may include widely used processing tools like GDAL (Geospatial Data Abstraction Library), remote sensing software like SNAP (Sentinel Application Platform), data cube related tools (e.g., xarray & Dask) and APIs (e.g., OpenEO API) for performing advanced analysis on EO data.
“The Exploitation Tier represents the end-users who exploit the services of the platform to perform analysis, or using high-level applications built-in on top of the platform’s services.” (Source: EOEPCA Master System Design)
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User Interfaces: The exploitation interface component may include web-based interfaces like web portals (e.g., EO Browser from Sinergise), dashboards (e.g., Earth Observation Dashboard from NASA, ESA, JAXA or web development environments (e.g., JupyterLab). All of them provide interactive interfaces for users to explore and analyze EO data through a user-friendly interface.
When should you consider to use an EO cloud platform?
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[[x]] I have limited internet bandwidth for data downloading
+[[ ]] I have all my data locally on my own servers with lots of computing resources
+[[x]] I want to collaborate with other and external users
+[[x]] I want to easily make use of processing services
+[[x]] I don't want to care about system administration and operations
+[[ ]] I do not often have access to internet
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When you think about data, most likely tables come to your mind, with features organized in rows and columns. Data cubes overcome these constraints: they are a data structure that allows to represent data in more than only two dimensions. Generally a data cube is a multi-dimensional data structure. Even though it’s called a cube, it is n-dimensional. A 1-d data cube is an array. A 2-d data cube is a table. A 3-d data cube is a cube. A 4-d data cube is hard to visualize. A feature within a data cube is explained by multiple dimensions and has a certain value. An example would be: A company has sold multiple products (Shirts, Shoes, Pants), in different countries (Sweden, USA, Tunisia), in different years (2021, 2022, 2023).
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Dimensions: Products, Locations, Years
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Labels of the Dimensions:
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Products: Shrits, Shoes, Pants
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Countries: Sweden, USA, Tunisia
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Years: 2021, 2022, 2023
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Feature: Revenue
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Value: $
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This concept can be applied to many fields such as economics, medicine, biology, and also very well to EO data!
The concept of representing multidimensional data as data cubes fits ideally to the challenges of representing satellite data that is usually dealing with multiple dimensions such as: lat, lon, time, bands, etc. In the video below it becomes very clear what the strengths of data cubes in EO are.
Data can be represented as datacubes in EO, which are multi-dimensional arrays with additional information about their dimensionality. Datacubes can provide a nice and tidy interface for spatiotemporal data as well as for the operations you may want to execute on them. As they are arrays, it might be easiest to look at raster data as an example, even though datacubes can hold vector data as well. Our example data however consists of a 6x7 raster with 4 bands [blue, green, red, near-infrared] and 3 timesteps [2020-10-01, 2020-10-13, 2020-10-25], displayed here in an orderly, timeseries-like manner:
It is important to understand that datacubes are designed to make things easier for us, and are not literally a cube, meaning that the above plot is just as good a representation as any other. That is why we can switch the dimensions around and display them in whatever way we want, including the view below:
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Figure: Raster datacube flat representation: The 12 imagery tiles are now laid out flat as a 4 by 3 grid (bands by timesteps). All dimension labels are depicted (The timestamps, the band names and the x, y coordinates). Reference: openeo.org (2022). What are Data Cubes.
A dimension refers to a certain axis of a datacube. This includes all variables (e.g. bands), which are represented as dimensions. Our exemplary raster datacube has the spatial dimensions x and y, and the temporal dimension t. Furthermore, it has a bands dimension, extending into the realm of what kind of information is contained in the cube.
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The following properties are usually available for dimensions:
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labels (usually exposed through textual or numerical representations, in the metadata as nominal values and/or extents)
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reference system / projection
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resolution / step size
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unit (either explicitly specified or implicitly given by the reference system)
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additional information specific to the dimension type (e.g. the geometry types for a dimension containing geometries)
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Here is an overview of the dimensions contained in our example raster datacube above:
Dimension labels are usually either numerical or text (also known as “strings”), which also includes textual representations of timestamps or geometries for example.
+For example, temporal labels are usually encoded as ISO 8601 compatible dates and/or times and similarly geometries can be encoded as Well-known Text (WKT) or be represented by their IDs.
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Dimensions with a natural/inherent order (usually all temporal and spatial raster dimensions) are always sorted. Dimensions without inherent order (usually bands), retain the order in which they have been defined in metadata or processes (e.g. through filter_bands), with new labels simply being appended to the existing labels.
A geometry dimension is not included in the example raster datacube above and it is not used in the following examples, but to show how a vector dimension with two polygons could look like:
A dimension with geometries can consist of points, linestrings, polygons, multi points, multi linestrings, or multi polygons.
+It is not possible to mix geometry types, but the single geometry type with their corresponding multi type can be combined in a dimension (e.g. points and multi points).
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EO datacubes contain scalar values (e.g. strings, numbers or boolean values), with all other associated attributes stored in dimensions (e.g. coordinates or timestamps). Attributes such as the CRS or the sensor can also be turned into dimensions. Be advised that in such a case, the uniqueness of pixel coordinates may be affected. When usually, (x,y) refers to a unique location, that changes to (x,y,CRS) when (x,y) values are reused in other coordinate reference systems (e.g. two neighboring UTM zones).
In the example above, x and y dimension values have a unique relationship to world coordinates through their coordinate reference system (CRS). This implies that a single coordinate reference system is associated with these x and y dimensions. If we want to create a data cube from multiple tiles spanning different coordinate reference systems (e.g. Sentinel-2: different UTM zones), we would have to resample/warp those to a single coordinate reference system. In many cases, this is wanted because we want to be able to look at the result, meaning it is available in a single coordinate reference system.
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Resampling is however costly, involves (some) data loss, and is in general not reversible. Suppose that we want to work only on the spectral and temporal dimensions of a data cube, and do not want to do any resampling. In that case, one could create one data cube for each coordinate reference system. An alternative would be to create one single data cube containing all tiles that has an additional dimension with the coordinate reference system. In that data cube, x and y no longer point to a unique world coordinate, because identical x and y coordinate pairs occur in each UTM zone. Now, only the combination (x, y, crs) has a unique relationship to the world coordinates.
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On such a crs-dimensioned data cube, several operations make perfect sense, such as apply or reduce_dimension on spectral and/or temporal dimensions. A simple reduction over the crs dimension, using sum or mean would typically not make sense. The “reduction” (removal) of the crs dimension that is meaningful involves the resampling/warping of all sub-cubes for the crs dimension to a single, common target coordinate reference system.
The resolution of a dimension gives information about what interval lies between observations. This is most obvious with the temporal resolution, where the intervals depict how often observations were made. Spatial resolution gives information about the pixel spacing, meaning how many ‘real world meters’ are contained in a pixel. The number of bands and their wavelength intervals give information about the spectral resolution.
One important feature of an EO cloud platform is to host satellite data. Usually vast amounts of satellite data, for example the whole archive of Landsat and the Sentinels which adds up to Petabytes of data. Users want to access this data efficiently. Therefore the storage system of an EO cloud platform needs to be optimized towards user requests. Data Cubes play an important role here. Organizing the raw files into virtual data cubes solves many issues for EO cloud platforms.
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Data Management: Different Satellites have different formats and are structured differently. This can easily create confusion because the different sources have to be treated differently. After organizing these files into data cubes the interaction with all different satellite data follows the same rules.
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Separated Collections: Every satellite missions images form an own data cube, also called collection, with its according metadata. Ideally cloud processing tools on the platform allow for easy data fusion of different collections.
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Flexible Subsetting:Data Cube Management Systems are developed to facilitate subsetting operations along the different dimensions and return only the required subset. Without worrying about the different raw files that are involved (e.g. area of interest is crossing a tile border).
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:warning: Remember, a data cube is not an EO cloud platform. It’s only one part of it.
[( )] vector, a one dimensional array
+[( )] atomic feature, a single value
+[(x)] table, in EO a raster
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Check the typical dimensions of an EO data cube.
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[[x]] x
+[[ ]] rain
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+[[ ]] function
+[[x]] time
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What is the main puorpose of a geometry dimension?
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[(x)] storing geometries (e.g. polygons, points)
+[( )] it's holding the bounding box of the data cube
+[( )] defining the shape of the pixels
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Attach the resolutions to their dimensions
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[[x and y] [time] [band]
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[(X) ( ) ( ) ] meter, kilometer, degrees
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[( ) (X) ( ) ] days, hours, years
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[( ) ( ) (X) ] micrometers, color
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What are the advantages of data cubes in EO?
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[[x]] EO data is multidimensional data cubes allow to represent this
+[[ ]] Data cubes introduce an unnecessary layer of complexity that is not needed in EO
+[[ ]] The file size of EO data is drastically reduced
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What are the advantages of data cubes in cloud platforms?
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[[x]] The user is not confronted with multiple files and file types
+[[x]] Different satellite missions can be stored in different data cubes that can be combined for analysis on the platform
+[[x]] subsetting operations become very powerful - users only receive the extents they are interested in.
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+ Open Science and the FAIR Principles — Cubes & Clouds - Cloud Native Open Data Sciences for Earth Observation
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Space Agencies and International Organisations across the globe promote Open Science and support its practice by the scientific community through dedicated programmes. This course, for example, is part of the support to Earth Observation Open Science by the European Space Agency (ESA).
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ESA, just like other European organisations such as the European Commission, has a long standing commitment to Open Science. A prominent example is the provision of full free and open Earth Observation data from its science missions (i.e. the Earth Explorers) and from the Copernicus Programme (e.g. the Sentinel missions). However, Open Science goes much beyond open data. ESA’s vision for Open Science covers the full research cycle (see ESA vision figure). Simply put, a piece of research fully adheres to Open Science principles if:
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all the data, code, and documentation of the respective research are FAIR (see What is FAIR? section), Open and linked to one another
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the research is reproducible across various platforms (i.e. cloud platforms or computational systems)
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the research is maintained so that it is accessible to the community in the long-term
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This requires that the scientific community adheres to the same common (or compatible) practices when writing and documenting code, preparing and sharing data or publishing their research in journals.
Open Science offers new opportunities in dealing with scientific knowledge and represents a kind of cultural change. Through transparency and openness, Open Science increases the use and further development of knowledge as well as the potential for collaboration and the credibility of science. The focus is on free access to scientific processes and findings for everyone via the internet and the right to re-use this content. The beneficiaries of this concept are not only science, but also society and the economy.
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Open Science is realised through various strategies and procedures, such as free access to scientific publications (Open Access), computer programmes (Open Source Software), data (Open Data) and educational materials (Open Educational Resources).
Reliable. It is important to evaluate the research in two ways. First with respect to scientific principles and criteria like validity, second with respect to criteria out of the professional context. This will help ensure that your results are more reliable.
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Reproducible. Transparency is critical when doing research. Open Science allows you to clearly show what you’ve done to get the results you have. By being open about your methods, processes and decision making during your research, someone else doing the research again should get the same results.
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Reusable. By making research results reusable, you allow others to build upon the solid foundation your research has already created in a given subject. This is the same R that is also going to be mentioned in the FAIR principles.
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Relevant. Research quality describes the measurable influence of academic research on the academic community. Research impact includes environmental, cultural and societal impact, economic returns and societal benefits. By adhering to open science you increase the chances of your research to be relevant, because you give others the chance to interact with it.
Data. Data-driven research is fast becoming the norm in all disciplines. To support validation of your findings and allow others to build upon your work, you first need to make sure that others can find your data. This means giving them persistent and unique identifiers (such as DOIs); assigning appropiate metadata so that others can find and reuse your data; putting them into a repository that supports public searching; and being clear about what others can and can’t do with them by applying an appropriate license. In the Further Reading section you find links to courses about managing and sharing research data and licensing your outputs.
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Code. When sharing your software and code, be sure to make use of open source standards to support interoperability and their longer-term viability. Be sure to put your code somewhere where others can search for it and access it (e.g., Github). Additionally, you can give your code a DOI by registering your Github repo on Zenodo. You should also be clear about the license the code is being shared under. In the Further Reading section there’s a course about Open Source Software.
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Papers. Open Access (OA) to publications is a key component of Open Science. Free and instant access to publications improves the speed of innovation and leads better cooperation and progress in solving grand challenges. To publish openly, you’ll need to be able to source an appropriate OA journal or discipline-specific repository and navigate your way through their publishing agreements. You should also consider sharing preprints of your work as a means of getting early feedback and community validation of your approaches. In some cases, you’ll need to pay an Article Processing Charge to publish in an OA journal.
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Reviews. The peer review process is evolving. By making the peer review process more transparent, researchers have better access to peer feedback at an earlier stage in the lifecycle and consumers of research outputs can have greater confidence in their quality.
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But there is way more to discover about Open Science. The Open Science Taxonomy graphic shows the different terms behind Open Science.
In 2014, a group of researchers as well as employees of libraries, archives, publishers and funders established principles for the handling of research data in a workshop and published them on FORCE11 for reviews and comments. The so-called FAIR principles were born. They comprise four goals: the findability, accessibility, interoperability and re-usability of data. With the achievement of these goals, the sustainable re-usability of research data is meant to be guaranteed. FAIR is not binary: FAIR is a spectrum!
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The OpenAIRE definition of Metadata: Metadata is data providing information about data that makes findable, trackable and (re)usable. It can include information such as contact information, geographic locations, details about units of measure, abbreviations or codes used in the dataset, instrument and protocol information, survey tool details, provenance and version information and much more. In earth observation satellite data that could be the spatial and temporal extent of the data, the sensor, the bands and their wavelenghts, etc. Section 2.1 Data Discovery and 2.2 Data Properties deal with EO metadata in particular.
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Findable
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The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
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F1. (Meta)data are assigned a globally unique and persistent identifier
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F2. Data are described with rich metadata (defined by R1 below)
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F3. Metadata clearly and explicitly include the identifier of the data they describe
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F4. (Meta)data are registered or indexed in a searchable resource
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Persistent Identifier (PID): These are IDs that identify a data set, publication or software (any digital object) unambiguosly and persistently with a single link. It increases the findability of a resource drastically. A widely used identifier is the Digital Object Identifier (DOI).
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Accessible
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Once the users find the required data, they need to know how they can be accessed, possibly including authentication and authorisation.
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A1. (Meta)data are retrievable by their identifier using a standardised communications protocol
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A1.1 The protocol is open, free, and universally implementable
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A1.2 The protocol allows for an authentication and authorisation procedure, where necessary
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A2. Metadata are accessible, even when the data are no longer available
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Interoperable
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The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
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I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
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I2. (Meta)data use standards, formats and vocabularies that follow FAIR principles and allow it to be exchanged and combined across computer systems
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I3. (Meta)data include qualified references to other (meta)data
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Reusable
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The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings
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R1. (Meta)data are richly described with a plurality of accurate and relevant attributes
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R1.1. (Meta)data are released with a clear and accessible data usage license
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R1.2. (Meta)data are associated with detailed provenance
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R1.3. (Meta)data meet domain-relevant community standards
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Licenses: To make your data reusable the use of appropiate licenses is key. Here is a good starting point to learn about the widely used creative commons licenses.
GO FAIR: GO FAIR is a bottom-up, stakeholder-driven and self-governed initiative that aims to implement the FAIR data principles, making data Findable, Accessible, Interoperable and Reusable (FAIR). It offers an open and inclusive ecosystem for individuals, institutions and organisations working together through Implementation Networks (INs). The INs are active in three activity pillars: GO CHANGE, GO TRAIN and GO BUILD.
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Train-the-Trainer: Training material for “FAIR” in the train-the-trainer program for Research Data Management: Biernacka, et al. (2020). Train-the-Trainer Concept on Research Data Management (Version 3.0). Zenodo. http://doi.org/10.5281/zenodo.4071471 (p. 38)
Let’s test your theoretical knowledge on open science now. It’s important that you understand these concepts. We will apply them later on in the course!
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What do the 4 Rs in the context of Open Science stand for?
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[( )] Readable, refreshable, recognizable, and receivable
+[(X)] Reliable, reproducable, reusable, and relevant
+[( )] Recitable, renameable, replicatable, and repairable.
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Which statement is correct about Open Science?
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[( )] Open Science sounds good, but it is unfair because researchers with limited financial means cannot afford access to open research results.
+[(X)] Open Science promotes the transparency of science and the free reuse of existing research results by everyone.
+[( )] Open Science ensures better networking of researchers within the EU. A worldwide exchange is currently not possible.
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+
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What are the arguments in favour of Open Data?
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[( )] Generally, open data is not subject to a review process, so this type of data publication is always significantly faster.
+[( )] Lower costs and less time needed to prepare the data than with a closed access publication.
+[(X)] Accessibility of scientific data and metadata, source texts and digital reproductions.
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+
+
FAIR is an acronym that stands for…
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[(X)] Findable, Accessible, Interoperable and Reusable
+[( )] Fast Artificial Intelligence Research
+[( )] Fair, Accurate, Inclusive and Respectful Education
+[( )] Free, Available, Implemented and Ready
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+
+
Findable means…
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[[X]] (Meta)data are assigned a globally unique and persistent identifier
+[[ ]] (Meta)data are optimized to show up in the highest positions in google searches
+[[X]] (Meta)data are registered or indexed in a searchable resource
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Accessible means…
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[[ ]] (Meta)data must be open.
+[[X]] (Meta)data does not necessarily have to be open.
+[[X]] (Meta)data has access conditions and these are clear to both humans and machines.
+[[ ]] (Meta)data is open for access, but not for reuse.
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+
+
Interoperable means…
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[( )] (Meta)data is created and described by at least two researchers from differenct institutes
+[(X)] (Meta)data use standards, formats and vocabularies that allow it to be exchanged and combined across computer systems and between humans
+
+
+
Reusable means…
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[(X)] the data has clear usage licenses and is usable by both people and machines.
+[( )] that all data is usable.
+[( )] the data has clear usage licenses to be used by people.
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+ Open Data and Open Source Software — Cubes & Clouds - Cloud Native Open Data Sciences for Earth Observation
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Open data is data that anyone can access, use and share. Open data becomes usable when made available in a common, machine-readable format. Open data must be licensed. Its licence must permit people to use the data in any way they want, including transforming, combining and sharing it with others, even commercially. Any restrictions imposed on the use of open data will limit its potential for creating new value.
+
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Limitations: For data to be open, it should have no limitations that prevent it from being used in any particular way. Anyone should be free to use, modify, combine and share the data, even commercially
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Cost: Open data must be free to use, but this does not mean that it must be free to access. There is often a cost to creating, maintaining and publishing usable data. Ideally, any fee for accessing open data should be no more than the reasonable reproduction cost of the unit of data that is requested. This reproduction cost tends to be negligible for many datasets. Live data and big data can incur ongoing costs related to reliable service provision.
+
Reuse: Once the user has the data, they are free to use, reuse and redistribute it – even commercially. Open data is measured by what it can be used for, not by how it is made available. Aspects like format, structure and machine readability all make data more usable, and should all be carefully considered. However, these do not make the data more open.
+
FAIR vs Open Data: FAIR data is not the same as open data. For example, it is not always possible to grant free access to data for economic and legal reasons. Restrictions on access are compatible with FAIR principles, as long as the conditions and ways of access are evident.
Since we use material from the European Commissions [data.europa.eu e-learning programme], which is published under the Creative Commons Attribution-ShareAlike 4.0 International License we have to publish this section What is Open Data under This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Open Source does not simply mean that the source code of a project is available, which is only one element of an Open Source project. The Open Source Initiative (OSI) provides a commonly accepted definition of what constitutes Open Source. To summarize that, in order to be considered Open Source:
+
+
Open Source Software needs a license,
+
a work has to allow free redistribution,
+
the source code needs to be made available,
+
it must be possible to create further works based on it,
+
there must be no limitations of who may use the work or for what purpose (so something like “no commercial use” or “no military use” won’t work with Open Source),
+
the work must not require an additional license on top of the one it comes with,
+
and finally, the license must not depend on a specific distribution format, technology or presence of other works.
The creation of this course would not be possible without Open Source Software. Here are just a few examples of Open Source Software used in this course:
[(X)] Open Data means information that is freely accessible.
+[( )] With Open Data, only data that is related to a scientific interpretation can be considered.
+[( )] With Open Data, the availability and usability of data on the web is limited.
+
+
+
FAIR data is always open data.
+
[( )] True
+[(X)] False
+
+
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What is true about Open Source Software projects
+
[[X]] The source code is completely available to the public.
+[[ ]] You cannot contribute to Open Source Software Projects.
+[[X]] Open Source Software Projects are community driven.
+[[ ]] You can never use Open Source Software for commercial purposes.
+[[ ]] If software is published under a license, it is not open source.
+
+
+
What is GitHub?
+
[( )] GitHub is a cloud storage system specialized in storing big earth observation data sets.
+[(X)] GitHub is a code hosting platform for version control and collaboration. It lets you and others work together on projects from anywhere.
+
+
+
Find the following GitHub repositories and copy their link into the text box. Copy the complete link starting with https://
Finally let’s see how open science principles are applied in the field of geospatial, earth observation and EO cloud platforms. To begin we will have a look at the open science journey and a research project that has adapted openness and the FAIR principles very well. Then we will have a look at the role open science plays in today’s geospatial and EO world.
+
This drag and drop game asks you to connect the tasks to their respective step within the open science journey. If you hover over the icons, their description will pop up.
The ClirSnow Project is a great example of how the concepts of opennes and FAIR are applied to a real world research project.
+
+
+
Video content in collaboration with Michael Matiu (University of Trento).
+“It seems like a lot of work the first time you do it. And it is. But once you know how to do it, you will use it in every research project, because it actually makes research so much easier. And, it will boost your research impact and credibility. It is really worth it.”
+
+
+
+
+
The Role of Open Source Software in Geospatial - The example of GDAL#
+
Open Science plays an important role in geospatial. Open source software is a part of that and the Geographic Data Abstraction Library (GDAL) software is a great example of how important open source software is in the geospatial world.
+Paul Ramsey, the co-founder of the PostGIS extension, has described what GDAL is in a metaphoric way in a mapscaping.com podcast: “GDAL is data plumbing, a bit like an international electrical plug set for traveling — it’s got multiple different shaped plugs. Electricity is “just” electrons moving around. But they can move around as DC, AC, 120 volts or 240 volts. Plus, there are all these different ways you can plug and join electrical things. At the core, electricity is electrons vibrating, but it can be quite complex to get your hair dryer spinning.”
+Howard Butler, a director of the Open-Source Geospatial Foundation, said about the importance of GDAL: “[…] It’s open, it provides core functionality, I can’t understand how anybody gets anything done without it.“
+
+
+
Video content in collaboration with Even Rouault (Main Developer of GDAL).
Code: Workflows and Code can easily be shared on EO Cloud Platforms. There are openly available tutorial notebooks. Workflows can be shared as user defined processes and be reused by the community. There are user forums that share solutions and snippets. OpenEO, a standardized processing API for EO in the cloud, allows code to be portable between different cloud platforms. This increases reprodicibility, collaboration and prevents vendor locks.
+
+
To Do: Image Slider: openEO Platform Forum, Tutorial Notebooks Microsoft Planetary Computer, User Defined Processes openEO,
+
+
+
Results: There are multiple ways to share results created in EO cloud platforms. Ideally they can be ingested into the platform and be made available as collections for other users directly upon creation. If the result comes with appropiate metadata (e.g. according to the STAC specification) they can easily be registered in publicly avialable STAC Catalogues. Cloud Native Data Formats (described in more detail in lesson 2.4 Formats and Performance), like cloud optimized geotiff, are accessible via https requests. So instead of sharing a file, only a URL pointing to the file is shared.
+
+
To Do: Image Slider: Collection in a Platform, STAC Catalogue, Link to a COG
+
+
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Publication: If a publication is built on top of results produced in an EO cloud platform, the results and code can easily be linked to the publication in one of the forms described aboved. For example, you can publish your openEO process graph and link to it, and provide a link to a STAC Catalogue where the results are accessible.
+
+
To Do: Example of a Publication where the code is available on a cloud platform
+
+
+
FAIRness:
+
+
Findable: Data is usually presented through a data catalogue (e.g. STAC Catalogues are used in openEO platform and the Microsoft Planetary Computer) that is explicitly made for searching data. In many cases searching data works even without registration on the platform.
+
Accessible: Data access in cloud platforms is usually granted after registration and authentication. Since cloud computing resources can easily be misused a certain degree of access control is necessary.
+
Interoperable: Processing standards like openEO aim at making the code interoperable, which means it is transferable between platforms. Standardised metadata attached to the results,the use of cloud optimized formats and reingestion of the results into the platform guarantee easy uptake of the results right away. Different sources of satellite data are made interoperable by the cloud platform through the use of data cubes and processing on the fly - reprojections, regridding and temporal alignment are enabled on the fly.
+
Reusable: To make results reusable for others, they need to be accessible and have an open license. Ideally, a license of choice can be added to the metadata and the results are reingested into the platform as a public collection, available for everyone.
+
+
+
Analysis Ready Data (ARD): Analysis Ready Data are in the context of EO cloud platforms are usually satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets. This means for example that atmospheric correction and cloud masking has already been applied to optical data. Many collections on cloud platforms are analysis ready, so that users can directly start the analysis withouth the tedious and technically demanding preprocessing steps. Since ‘analysis ready’ can mean different things to different people, CEOS is working on standardizing what analysis ready data are.
Search a license from the Creative Commons License Chooser that adheres to the following: proper attribution/citation must be included when used, free for commercial use and Adaptions of the work can be shared, but only under the same or a compatible license.
+
[( )] CC BY 4.0
+[(X)] CC BY-SA 4.0
+[( )] CC BY-SA-ND 4.0
+
+
+
Was the license you have just chosen a software license or a data license?
+
[( )] Software License
+[(X)] Data License
+
+
+
Find the Open Research Data Set “Snow cover in the European Alps: Station observations of snow depth and depth of snowfall” on the catalogue OpenAIRE.
+
Share the DOI link to the data set version v1.3 in the format https://doi.org/10.5281/zenodo.XXXXXXX. Hint the last numbers are 74
Which license is used for the data set? Copy the URL to the license here.
+
[(X)] Creative Commons Attribution 4.0 International
+[( )] Creative Commons Attribution-NonCommercial 4.0 International
+[( )] Creative Commons Attribution-ShareAlike 4.0 International
+
+
+
Find the open access publication that is connected to the dataset. The one that has been published in “The Cryosphere”. Copy the DOI of the article here in the format https://doi.org/XX.XXXX/tc-XX-XXXX-XXXX. Hint: The DOI ends with 21
+
[[https://doi.org/10.5194/tc-15-1343-2021]]
+
+
+
Under which license is this course published. You can find this out on the courses GitHub page.
+
[( )] Massachusets Institute of Technology License (MIT License)
+[( )] Creative Commons Attribution-ShareAlike 4.0 International License
+[(X)] Creative Commons Attribution 4.0 International License
+
+
+
How is data FAIR in a cloud platform? Connect the subjects to the FAIR keywords.
+
[[Findable] [Accessible] [Interoperable] [Reusable]]
+[(X) ( ) ( ) ( ) ] STAC Metadata, Metadata Catalogue
+[( ) ( ) (X) ( ) ] Usage of abstract data cubes instead of different file formats
+[( ) (X) ( ) ( ) ] Authentication, Login, Free Trial Accounts
+[( ) ( ) ( ) (X) ] Data licenses attached to collections, provenance of the data is reported
+
Knowledge of various geospatial data types is essential for understanding, representing, and analyzing acquired data from various domains. Data carry not only spatial information but also measured values, derived data, or other targeted information. Based on their properties, they can be used for different purposes.
+
There are several common geospatial data types, each serving a specific purpose and having distinct characteristics.
+
+
+
Figure: Common data types in EO. Raster, Polygon, Point.
Raster data are represented as pixels and grid cells where every pixel has a value associated with it. Data stored in raster format can be discrete (representing distinct categories, eg. land cover types) or continuous (for example temperature or elevation).
+
Examples of raster data sources:
+
+
Satellite data - Satellite imagery data are a common example of raster data. There are many types of data acquired by satellites and their processing. The temporal aspect of images is very important for continuous analysis.
+
Aerial/drone orthophoto - Digital imagery obtained by aircrafts (e.g. drones) is usually high-resolution raster data usable for detailed analysis.
+
DEM - Digital Elevation Models are representing the surface of (usually) Earth and are essential for computations on a surface level.
+
Cloud mask - Cloud mask refers to the task of cloud detection in optical satellite data, where the common task is to detect (and remove) clouds to obtain better quality data.
Vector data are a fundamental type of geospatial data. Vector data represents spatial features using 3 basic classes - points, lines, and polygons and the attributes associated with them. With vector data, it is possible to capture and analyze complex spatial patterns and attributes with great accuracy, providing valuable insights into spatial phenomena and supporting decision-making processes. Vector data are commonly used in addition to raster data to highlight or accompany trends and results.
+
Examples of vector data sources:
+
+
Building footprints (Polygons) - Building footprints are usually represented as polygons and capture the perimeter of buildings on the Earth’s surface. These footprints can vary in complexity from simple rectangles for standard buildings to more intricate shapes that outline the exact contours of complex structures.
+
Street networks - Street networks are depicted as lines, representing the pathways through which vehicles and pedestrians can move. Each line segment can indicate a portion of a street, alley, highway, or path, connecting intersections and endpoints to map out the entire transportation infrastructure of an area.
+
Points of interest - Points of Interest (POIs) are example of point vector data. They represents specific locations that hold importance or interest within a geographic area or usecase. These can include landmarks, businesses, public facilities, and natural features, among others.
In-situ data are data collected directly at the place of interest and are connected to a special location. There are many reasons why these data are important and one of them is an enhancement of data collected by other means eg. weather measurements from satellites enhanced with local air quality measurements. There are countless examples of what can be measured, two common examples are:
Datacube refers to a multidimensional representation of data that incorporates spatial and temporal dimensions. It is a concept used to organize and analyze large volumes of geospatial information in a structured and efficient manner. Datacubes are combining the data types discussed above into one data structure. More about datacubes is in Lesson 1.2
For Earth observation data discovery, it is essential to know about various used catalog protocols that define standardized methods and formats for organizing, describing, and accessing Earth observation data catalogs.
+
Some commonly used protocols shared between diffent platforms include OpenSearch, OGC CSW, OGC API - Features, OGC API - Records, STAC, OData and more.
The OpenSearch specification was launched in 2005 by A9.com, an Amazon subsidiary, as a means for sharing search queries and search results in a standardized format.
+The specification was intended to allow syndication of search results that could then be aggregated by one large index.
+OpenSearch provides a simple to use description of the search interface, which is called an OpenSearch Description document (OSDD).
+A client (e.g., a browser) can use this description to check which response formats are supported and how a query/filter can be formulated.
+The OpenSearch based REST services are usually offered by existing EO data platforms for compatibility reasons as the protocol itself is stable and not extended anymore.
+The data models of most catalogs build on top of XML or GeoJSON and allow filtering on a set of simple but not standardized properties. The protocol supports both textual and geospatial search and filtering capabilities, making it suitable for a wide range of applications, including web search engines and geospatial data catalogs.
Catalogue Service for the Web (CSW) standardized by Open Geospatial Consortium (OGC), offers a framework for publishing, discovering, and accessing metadata records, allowing users to effectively search and retrieve geospatial data and related information. The catalogue is made up of records that describe geospatial data, linked geospatial services and related resources.
+CSW enables metadata query using metadata core (mandatory) elements.
+Catalogue services support the use of one of several identified query languages to find and return results using well-known content models (metadata schemas) and encodings.
OGC API - Features is a modern and flexible geospatial data access protocol developed by the OGC. It provides a standardized and RESTful approach for querying, retrieving, and manipulating geospatial feature data over the web. By leveraging the power of web technologies such as HTTP, JSON, and GeoJSON, OGC API - Features simplifies the process of accessing and working with geospatial data. It allows users to retrieve specific features based on spatial and attribute filters, perform spatial and attribute queries, and even modify feature data through standard HTTP methods.
OGC API - Records is a multi-part draft specification (built on top of OGC API - Features) that offers the capability to create, modify, and query metadata on the Web. The draft specification enables the discovery of geospatial resources by standardizing the way collections of descriptive information about the resources (metadata) are exposed. The specification also enables the discovery and sharing of related resources that may be referenced from geospatial resources or their metadata by standardizing the way all kinds of records are exposed and managed.
STAC stands for SpatioTemporal Asset Catalog. It is a community specification that provides a common way for describing and cataloging assets that have a connection to space and time, usually but not necessarily on the Earth. The STAC specification focuses on organizing and sharing geospatial data in a way that is accessible, interoperable, and scalable. The STAC Specification consists of 4 semi-independent specifications (Catalog, Collection, Item and API) which can work independently or be used together. All of them can be and are enriched by a variety of extensions.
+It is a relatively new specification but increasingly integrated by various data providers and seen as future of EO Data cataloguing and discovery. The data model in the dataspace is still evolving to comply fully with all standardized properties. Because of that, more attention is provided to STAC than other catalogue protocols in this tutorial.
The STAC specification is divided into three main parts:
+
+
STAC specification for static catalogs, which consists of three parts:
+
+
STAC Items
+
STAC Catalogs
+
STAC Collections
+
+
+
STAC API specification for dynamic catalogs.
+
STAC extensions (both for static STAC and the STAC API)
+
+
All these components are fairly independent, but all components work together and use links to express the relationship between them so that eventually clients can traverse through them. The links to the actual spatio-temporal data files that the STAC metadata describes are handled specifically and are called STAC Assets. Assets can be made available in Items and Collections.
A STAC Item is the foundational building block of STAC. It is a GeoJSON feature supplemented with additional metadata that enables clients to traverse through catalogs. Since an item is a GeoJSON, it can be easily read by any modern GIS or geospatial library. One item can describe one or more SpatioTemporal Asset(s). For example, a common practice of using STAC for imagery is that each band in a scene is its own STAC Asset and there is one STAC Item to represent all the bands in a single scene.
+
The STAC Item JSON specification uses standard GeoJSON fields as well as a few additional informational fields to describe the asset(s) more thoroughly.
+
STAC Item (and other components) have some required fields which must be always filled with information. In the example below, required fields like type, stac_version or id are filled. Properties are also required fields, but here also extended by many STAC extensions, in the format of extension_name:field_name: value. STAC extensions are also listed in the stac_extensions field. Complete STAC Item spec can be found on GitHub.
+
+ Example of a Sentinel 2 L2A STAC Item with one band asset.
+
A STAC Catalog is an entity that logically groups other Catalogs, Collections, and Items. A Catalog contains links to these other entities and can include additional metadata to describe the entities contained therein. A catalog is usually the starting point for navigating a STAC. More specifically, a catalog.json file contains links to some combination of other STAC Catalogs, Collections, and/or Items. We can think of it like a directory on a computer although it doesn’t necessarily need to mirror the local directory tree.
+
There are no restrictions on the way STAC Catalogs are organized. Therefore, the combination of STAC components within a STAC Catalog is quite variable and flexible. Many implementations use a set of ‘sub-catalog(s)’ that group the items in some sensible way, e.g. by years as a first level and months as a second level. It can be easily extended, for example, to include additional metadata to further describe its holdings, as the STAC Collection does.
A STAC Collection is similar to a STAC Catalog, but includes and partially requires additional metadata about a set of items that exist as part of the collection. It adds additional fields to enable the description of information like the spatial and temporal extent of the data, the license, keywords, providers, etc. Therefore, it can easily be extended with additional collection-level metadata that is common across all children. For example, it could summarize that all Items underneath hold data in either 10m or 30m spatial resolution.
+
+ Example of a Sentinel 2 L2A STAC Collection.
+
STAC API is a dynamic version of a static SpatioTemporal Asset Catalog and provides a RESTful endpoint that enables the search of STAC Items and STAC Collections. STAC Catalogs don’t play a big role in APIs as they are mostly used as an entity for grouping larger static catalogs into smaller chunks, which is usually not needed in the context of a dynamic API.
+
If the API implements the Filter or Query extension, additionally the user is allowed to search for specific content based on a set of available metadata fields. Additional extensions may support more interactive elements such as aggregations, or managing the metadata (updating it, creating new entities, or deleting some) through transactions.
Extensions to STAC are split into two parts: STAC extensions and STAC API extensions. They are both an important addition to the STAC specifications and can provide either additions to the data model (i.e. additional JSON properties such as eo:cloud_cover) or behavioural changes (e.g. additional types of links or a sorting functionality). Most tend to be about describing a particular domain or type of data.
OData (Open Data Protocol) specifies a variety of best practices for creating and using REST APIs that can be handled by a large set of client tools like common web browsers, download-managers. The OData protocol can be used for building URI for performing search queries and product downloads for example on the Copernicus Dataspace.
Earth observation data access is not limited to a single platform or a single entry point. What follows is a non-exhaustive list of some well-known Earth observation data catalogues usually based around the original agency providing the data:
+
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EarthData Search: A comprehensive data discovery and access tool provided by NASA’s Earth Observing System Data and Information System (EOSDIS). Contains wide range of NASA’s Earth science data.
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Copernicus Data Space: Currently in development platform aiming to provide immediate access to large amounts of open and free Earth observation data and scalable interfaces including both new and historical Sentinel images, commercial datasets, as well as Copernicus Contributing Missions.
+
USGS Earth Explorer: EarthExplorer (EE) provides online search, browse display, metadata export, and data download for earth science data from the archives of the U.S. Geological Survey (USGS). Usually of largest demand are data from the Landsat missions.
+
Open Science Catalog: A catalog of publicly available geoscience products, datasets and resources developed in the frame of scientific research Projects funded by ESA EO. Queriable by themes, projects, variables and products.
+
NCEI Catalog: NOAA’s National Centers for Environmental Information (NCEI) provides access to various environmental data, including satellite imagery, climate data, and other geospatial datasets. In particular it is composed of oceanic, atmospheric, and geophysical data.
+
STAC Index: A list of publicly available STAC APIs and Static Catalogs.
Large EO data portals usually allow two main different ways of access based on the technical proficiency of the target user group. User can either use graphical user interface of selected portal or use one the available APIs for search. Bellow are examples of way how to search for your data.
Many large portals provide a easy-to-use search and filtering GUI, which under the hood uses one of the provided catalog APIs. This allows less experienced users to perform queries usually for smaller subset of data with the help of an attached Web map client to orient themselves easily.
For batch operations or script access to the catalogs, it is envisioned that direct approach of the API is performed instead. This removes the need for direct user interaction to the web platform and can be used from user provided scripts or other programs and CLI tools.
+
Fore more information about performing filtering or queries, see Data Properties.
In order to access and browse any online STAC catalog or API, a rich web client application STAC Browser can be used on radiantearth.github.io/stac-browser. It does does allow a wide variety of filtering capabilities.
QGIS STAC API Browser provides a simple and user-friendly approach in searching and using STAC API resources in QGIS. It offers a comfortable way to filter and browse the STAC API items and ability to add the STAC API assets as map layers into the QGIS.
MetaSearch is a QGIS plugin to interact with metadata catalog services, supporting both the OGC API - Records and OGC Catalog Service for the Web (CSW) standards.
OWSLib is a Python package for client programming with Open Geospatial Consortium (OGC) web service (hence OWS) interface standards, and their related content models. The OWSLib client offers support for OGC CSW, OGC API - Records and OpenSearch based catalog services.
The STAC Python Client (pystac_client) is a Python package for working with STAC Catalogs and APIs that conform to the STAC and STAC API specs in a seamless way. PySTAC Client builds upon PySTAC through higher-level functionality and ability to leverage STAC API search endpoints.
Which one of the listed data access API’s can you use on Copernicus Dataspace? See list of API’s available
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[(x)] OData
+[(x)] STAC API
+[(x)] OGC API
+[( )] AstraDataServe
+[( )] OrbitQueryHub
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Use an online catalog browser of Copernicus Dataspace to search for Sentinel 2 L2A products over the island of Sardinia between 1.9.2023 and 30.9.2023 with cloud coverage less than or equal to 20% and get the number of returned products. Use enclosed geometry file and upload it to the browser as shown on image below.
Data properties and metadata are sets of characteristics of geospatial data that serve different purposes. Commonly data properties are seen as attributes describing the image itself whereas, on the other hand, metadata describes the properties of a product.
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Data Properties: Data properties refer to the characteristics and attributes of the geospatial data itself. These properties describe the actual content, structure, and values within the dataset. For raster data, properties can include information such as pixel values, cell size, spatial resolution, number of bands, and data format. For vector data, properties may include attributes associated with points, lines, or polygons, such as feature identifiers, names, classifications, or numerical values. Data properties are essential for understanding the data at a fundamental level and are used in the analysis, visualization, and processing tasks.
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Metadata: Metadata, on the other hand, provides descriptive information about the data product. It serves as documentation that complements the data properties and offers additional context and details about the data. Metadata describes the origin, and characteristics, and helps with usage of the data, allowing users to discover, evaluate, and effectively utilize the data. It typically includes information such as the data source, acquisition parameters, coordinate reference system, temporal coverage, quality measures, and data access policies. Metadata plays a crucial role in data management, data sharing, and data interoperability by providing the necessary documentation and context for understanding and utilizing the data.
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Both together provide a complete description of the data of our interest itself and how to work with them. Standardized ways how to describe metadata and data properties allow geospatial systems to work with data easily.
In previous lesson we learned where to find data. Now it is time to look at how we can select only the data we want. Properties and metadata are a great help when we are not interested in the whole collection, but only in certain regions, times or even bands of selected satellite products.
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In (many of) the data catalogs, we can filter by specific values for each satellite. Let’s talk more about some of them shortly
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Dataset name/identifier: Filtering directly by name of a product as a unique name or identifier is assigned to the dataset, allowing it to be easily identified and referenced.
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Time range: The temporal coverage or specific dates associated with the data acquisition. By this, we can easily select products from the same location with different dates of acquisition. This is particularly relevant for time-series or multi-temporal datasets.
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Bounding box or other area of interest: Spatial Extent or the geographic coverage of the raster data, typically defined by the bounding coordinates (longitude and latitude) that encompass the dataset. Usually you can select your own area or interest as a rectangle/geometry or use a map window for taking the current extent
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Mission: You can select by satellite used for data acquisition. In the case of Sentinel missions, you can select only Sentinel-1 as an example
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Processing levels: Typically you can also select by processing level you are interested in for your missions. Typically Level-0 means unprocessed, raw data, and with higher number represents more corrections were applied.
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Sensors or instrument: Selection by the output of a specific sensor or instrument.
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Cloud coverage/polarization: Based on a mission, a selection filter can be made by specific parameters. For multi-spectral data such as captured by Sentinel-2, we can typically filter by cloud coverage in percentage. This is used as we are interested in lower cloud coverage percent for analysis. Similarly, specific polarizations can be selected for SAR data such as captured by Sentinel-1.
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Orbit number: Typically integer number selects a specific orbit number user is interested in.
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Orbit direction: For the majority of data types the selection is either ascending or descending.
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Availability status/Timeliness: Some missions have different time availability for their data allowing them to select Near Real-Time acquisitions (approximately 3 hours after acquisition), raw data with a certain delay, or processed data later. It is also common that infrequently accessed data or older data than a certain threshold (years) can only be accessed ‘on demand’. Their ‘Availability status’ is usually set as ‘Archived’ or similar and they but must be ‘tasked to be brought online from archive storage’ by the user. This operation usually takes minutes to hours to complete. The user of the platform can later access the catalog again and hopefully the product have been brought online in the meantime.
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In the examples above it also became more clear why a standardized way of expressing the metadata is important. For example, how do you know whether the cloud cover is a percentage expressed in a range from 0 to 1 or from 0 to 100? How would you filter on this property, if both scales would be mixed?
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Video content in collaboration with Matthias Mohr (Major STAC contributor).
+“Having metadata in a standardized format makes your data available to others in a simple and unified way. Similarly, you can find and work with the data of others more easily. You can achieve these benefits with STAC.”
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Metadata and properties which are not typically used for filtering#
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Many metadata are contained within the product but are not used for filtering on the platforms or accessing hubs directly. Among these is for example Author of the dataset or Licence. You should be able to get all information about those in data metadata when you are accessing the data itself or on the general page with information.
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Usually it is unfortunatelly not possible to filter by or search by direct data properties, e.g. by values in data.
Dimensions are important description of data and their properties. More information about dimensions of data and datacubes was covered in the lecture about Datacubes
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x, y and sometimes z - Spatial dimension of data
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temporal/time dimension - capturing the time aspect for time series analysis
Once we have selected data products we are interested in, we can look directly into values to select what we are interested in. Common data types representing measured values are these:
Which of the following is not considered as Data Properties
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[(x)] Licence
+[( )] Pixel values
+[( )] Number of bands
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Which of these properties and metadata are commonly used for filtering when searching data?
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[(x)] Bounding Box
+[( )] E-Mail Adress
+[(x)] Time Range
+[(x)] Sensor
+[( )] Telephone Number
+[(x)] Cloud Coverage
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Which statements about the advantage of filtering data by metadata and properties are true?
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[( )] All images are considered for subsequent analysis.
+[(x)] Only images that are relevant are considered for subsequent analysis.
+[(x)] The amount of data is reduced before starting the actual analysis.
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Removed until exercise is accessible: Is the same number of products findable on all plafforms and in all ways?
+In the last chapter, there was exercise about finding Sentinel 2 L2A products over the island of Sardinia between 1.9.2023 and 30.9.2023 with cloud coverage less than or equal to 20% on Copernicus Dataspace via Web Browser. Correct answer was 59. Is the same number returned by programatically accessing catalog with code?
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[(x)] Yes
+[( )] No
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Removed until exercise is accessible: Following the exercise with gdalinfo, answer following questions about the raster used there:
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What is the NODATA value?
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[[0]]
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[[72d52'14.58"E, 32d31' 9.58"N]]
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[[4326]]
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+ Access EO Data from the Cloud — Cubes & Clouds - Cloud Native Open Data Sciences for Earth Observation
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Using a cloud provider for accessing data, and in this specific scenario Earth Observation data, could improve your productivity a lot. To get the most out of it, we will provide you some important insights.
In this lecture you will learn the usage and peculiarities of the main data operators commonly available on cloud platforms like:
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Data loading
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Filter
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Apply
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Reduce
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Resample
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Aggregate
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Finally, you will be able to create a simple workflow with openEO.
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The exercise will use the openEO Python Client Side Processing functionality, which allows to experiment using openEO without a connection to an openEO back-end.
In openEO, your process graph will always start with defining the data you want to load, which can be done mainly using the following processes:
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load_collection: loads a collection from the current back-end by its id and returns it as a processable data cube. The data that is added to the data cube can be restricted with the parameters spatial_extent, temporal_extent, bands and properties.
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load_stac: loads data from a static STAC catalog or a STAC API Collection and returns the data as a processable data cube. A batch job result can be loaded by providing a reference to it. If supported by the underlying metadata and file format, the data that is added to the data cube can be restricted with the parameters spatial_extent, temporal_extent and bands.
When filtering data (e.g. filter_spatial, filter_temporal, filter_bands), only the data that satisfies a condition is returned. For example, this condition could be a timestamp or interval, (a set of) coordinates, or specific bands. By applying filtering the datacube becomes smaller, according to the selected data.
In the image, the example datacube can be seen at the top with labeled dimensions. The filtering techniques are displayed separately below. On the left, the datacube is filtered temporally with the interval ["2020-10-15","2020-10-27"]. The result is a cube with only the rasters for the timestep that lies within that interval ("2020-10-25") and unchanged bands and spatial dimensions. Likewise, the original cube is filtered for a specific band ["nir"] in the middle and a specific spatial region [Polygon(...)] on the right.
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Figure: Datacube filtering: From the datacube 4 by 3 grid, arrows depict what happens if the grid is filtered. Temporal filtering results in data for one timestep with all four bands, filtering bands results in data with one band with all three timesteps, and spatial filtering results in all timesteps and bands being preserved, but all with a smaller area. Reference: openeo.org (2022). Processes on Datacubes.
The apply* functions (e.g. apply, apply_neighborhood, apply_dimension) employ a process on the datacube that calculates new pixel values for each pixel, based on n other pixels. Please note that several programming languages use the name map instead of apply, but they describe the same type of function.
For the case n=1 this is called a unary function and means that only the pixel itself is considered when calculating the new pixel value. A prominent example is the absolute() function, calculating the absolute value of the input pixel value.
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Figure: Datacube apply unary: 3 example tiles hold values below and above 0. after applying the process ‘absolute’, all values in the three example tiles have changed to their absolute values above 0. Reference: openeo.org (2022). Processes on Datacubes.
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If n is larger than 1, the function is called n-ary. In practice, this means that the pixel neighbourhood is taken into account to calculate the new pixel value. Such neighbourhoods can be of spatial and/or temporal nature. A spatial function works on a kernel that weights the surrounding pixels (e.g. smoothing values with nearby observations), a temporal function works on a time series at a certain pixel location (e.g. smoothing values over time). Combinations of types to n-dimensional neighbourhoods are also possible.
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In the example below, an example weighted kernel (shown in the middle) is applied to the cube (via apply_kernel). To avoid edge effects (affecting pixels on the edge of the image with less neighbours), a padding has been added in the background.
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Figure: Datacube apply spatial kernel: Three example tiles hold some values with a lot of variance. A spatial kernel (a cell plus it’s 4 direct neighbours) is applied to all pixels, and the result appears to be spatially smoothed, with less variance. Reference: openeo.org (2022). Processes on Datacubes.
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Of course this also works for temporal neighbourhoods (timeseries), considering neighbours before and after a pixel. To be able to show the effect, two timesteps were added in this example figure. A moving average of window size 3 is then applied, meaning that for each pixel the average is calculated out of the previous, the next, and the timestep in question (tn-1, tn and tn+1). No padding was added which is why we observe edge effects (NA values are returned for t1 and t5, because their temporal neighbourhood is missing input timesteps).
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Figure: Datacube apply temporal moving average: Smoothing is applied to 5 example tiles by calculating the mean of 3 timesteps of every single pixel. The resulting tiles for the timestamps look much more alike. Reference: openeo.org (2022). Processes on Datacubes.
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Alternatively, a process can also be applied along a dimension of the datacube, meaning the input is no longer a neighbourhood of some sort but all pixels along that dimension (n equals the complete dimension). If a process is applied along the time dimension (e.g. a breakpoint detection), the complete pixel timeseries are the input. If a process is applied along the spatial dimensions (e.g. a mean), all pixels of an image are the input. The process is then applied to all pixels along that dimension and the dimension continues to exist. This is in contrast to reduce, which drops the specified dimension of the data cube. In the image below, a mean is applied to the time dimension. An example pixel timeseries is highlighted by a green line and processed step-by-step.
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Figure: Datacube apply dimension time: The mean of all 5 timesteps is calculated for every single pixel. The resulting 5 tiles look exaclty the same, as they have been averaged. Reference: openeo.org (2022). Processes on Datacubes.
The reduce_dimension process collapses a whole dimension of the datacube. It does so by using some sort of reducer, which is a function that calculates a single result from an amount of values, as e.g. mean(), min() and max() are. For example we can reduce the time dimension (t) of a timeseries by calculating the mean value of all timesteps for each pixel. We are left with a cube that has no time dimension, because all values of that dimension are compressed into a single mean value. The same goes for e.g. the spatial dimensions: If we calculate the mean along the x and y dimensions, we are left without any spatial dimensions, but a mean value for each instance that previously was a raster is returned. In the image below, the dimensions that are reduced are crossed out in the result.
Think of it as a waste press that does math instead of using brute force. Given a representation of our example datacube, let’s see how it is affected.
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Figure: Datacube reduce: Three arrows depict what happens to the 12 example tiles, if they are reduced: Reducing timesteps leads to four tiles (one for each band), and the time dimension is deleted. Reducing bands lead to one tile per timestep, and the bands dimension is deleted. Reducing spatially leads to the original 4 by 3 bands by time layout, but the result has no spatial dimension and thus, the tiles have been turned into single values, per tile. Reference: openeo.org (2022). Processes on Datacubes.
In a resampling processes (e.g. resample_cube_spatial, resample_cube_temporal), the layout of a certain dimension is changed into another layout, most likely also changing the resolution of that dimension. This is done by mapping values of the source (old) datacube to the new layout of the target (new) datacube. During that process, resolutions can be upscaled or downscaled (also called upsampling and downsampling), depending on whether they have a finer or a coarser spacing afterwards. A function is then needed to translate the existing data into the new resolution. A prominent example is to reproject a datacube into the coordinate reference system of another datacube, for example in order to merge the two cubes.
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:warning: Simplified
+resample(🖼️,downscale)=>🟦
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resample(🌍,reproject)=>🗺️
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The first figure gives an overview of temporal resampling. How exactly the input timesteps are rescaled to the output timesteps depends on the resampling function.
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Figure: Datacube temporal resampling (up and down): Downsampling: To a timeline-representation of the example tiles, another timeline with only 2 steps at different dates is applied. The result has tiles only at those new timesteps. In Upsampling, the existing 3 timesteps are sampled into 5 result timesteps. Reference: openeo.org (2022). Processes on Datacubes.
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The second figure displays spatial resampling. Observe how in the upsampling process, the output datacube has not gained in information value. The resulting grid still carries the same pixel information, but in higher spatial resolution. Other upsampling methods may yield smoother results, e.g. by using interpolation.
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Figure: Datacube spatial resampling (up and down): Downsampling: The resulting tiles have a lower spatial resolution than the input tiles. Upsampling: The resulting tiles have a higher spatial resolution than the input tiles, but contain the same image than before (no information added). Reference: openeo.org (2022). Processes on Datacubes.
An aggregation of a datacube can be thought of as a grouped reduce. That means it consists of two steps:
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Grouping via a grouping variable, i.e. spatial geometries or temporal intervals
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Reducing these groups along the grouped dimension with a certain reducer function, e.g. calculating the mean pixel value per polygon or the maximum pixel values per month
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While the layout of the reduced dimension is changed, other dimensions keep their resolution and geometry. But in contrast to pure reduce, the dimensions along which the reducer function is applied still exist after the operation.
A temporal aggregation (e.g. aggregate_temporal) is similar to the downsampling process, as it can be seen in the according image above. Intervals for grouping can either be set manually, or periods can be chosen: monthly, yearly, etc. All timesteps in an interval are then collapsed via a reducer function (mean, max, etc.) and assigned to the given new labels.
+
A spatial aggregation (e.g. aggregate_spatial) works in a similar manner. Polygons, lines and points can be selected for grouping. Their spatial dimension is then reduced by a given process and thus, a vector cube is returned. The vector cube then has dimensions containing features, attributes and time. In the graphic below, the grouping is only shown for the first timestep.
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Figure: Datacube spatial aggregation: A line and a polygon are selected from the original example tiles. The pixels covered by these geometries are aggregated and the result consists no longer of imagery tiles but of an array with values for 2 geometries by 4 bands by 3 timesteps. Reference: openeo.org (2022). Processes on Datacubes.
openEO - A standardized API for EO cloud processing#
+
+
The need for a standarized API in EO cloud processing#
+
Earth Observation data are becoming too large to be downloaded locally for analysis. Also, the way they are organised (as tiles, or granules: files containing the imagery for a small part of the Earth and a single observation date) makes it unnecessary complicated to analyse them. The solution to this is to store these data in the cloud, on compute back-ends, process them there, and browse the results or download resulting figures or numbers. But how do we do that? openEO develops an open application programming interface (API) that connects clients like R, Python and JavaScript to big Earth observation cloud back-ends in a simple and unified way.
+With such an API,
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each client can work with every back-end, and
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it becomes possible to compare back-ends in terms of capacity, cost, and results (validation, reproducibility)
open: used here in the context of open source software; open source software is available in source code form, and can be freely modified and redistributed; the openEO project will create open source software, reusable under a liberal open source license (Apache 2.0)
+EO: Earth observation
+Jointly, the openEO targets the processing and analysis of Earth observation data. The main objectives of the project are the following concepts:
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Simplicity: nowadays, many end-users use Python or R to analyse data and JavaScript to develop web applications; analysing large amounts of EO imagery should be equally simple, and seamlessly integrate with existing workflows
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Unification: current EO cloud back-ends all have a different API (opens new window), making EO data analysis hard to validate and reproduce and back-ends difficult to compare in terms of capability and costs, or to combine them in a joint analysis across back-ends. A unified API can resolve many of these problems.
An API is an application programming interface. It defines a language that two computers (a client and a server) use to communicate.
+The following figure shows how many interfaces are needed to be able to compare back-ends from different clients, without an openEO API. And if you use the slider you will see how the situation becomes much clearer with a standardized API.
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Video content in collaboration with Edzer Pebesma (University of Münster).
+“For analysing Earth Observation data, don’t use a platform that locks you in.”
Loading: What is the difference between using an openEO remote back-end and openEO Python Client-Side-Processing? Tick what is correct. Answer in exercise: 23_data_access_lazy_loading.ipynb
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[[ ]] You need an account to authenticate in both cases
+[[x]] You don't need an account with Client-Side Processing
+[[x]] Client-side processing does not interact with an openEO back-end
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Loading: What are the dimension names of the loaded datacube? Answer in exercise: 23_data_access_lazy_loading.ipynb
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[( )] northing, easting, time, spectral
+[( )] lat, lon, t, bands
+[(x)] time, band, y, x
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Filtering: How many time steps are left after filtering temporally 2022-05-10, 2022-06-30? Answer in exercise: 23_data_access_filter.ipynb
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[( )] 11
+[(x)] 9
+[( )] 32
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Filtering: How many pixels are left along y after using filter_bbox with spatial_extent={"west":11.259613,"east":11.406212,"south":46.461019,"north":46.522237}
+? Answer in exercise: 23_data_access_filter.ipynb
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[( )] 1006
+[( )] 1145
+[(x)] 489
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Reducing Dimension: What would be a use case for reducing the time dimension? Tick what is correct.
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[[ ]] Get a full time series graph
+[[x]] Getting a cloudfree image for a certain time range
+[[x]] Get information about a whole sesaon
+[[ ]] Filling gaps in a time series
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Reducing Bands: How many pixels are left in the datacube after we use reduce_dimension over band? Answer in exercise: 23_data_access_reduce.ipynb
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[[21226010]]
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Reducing Dimension: What are the dimension names after running reduce_spatial? Answer in exercise: 23_data_access_reduce.ipynb
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[( )] x, y, band
+[( )] time, x, y
+[(x)] time, band
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We might be interested in aggregating our data over periods like week, month, year etc., defining what operation to use to combine the data available in the chosen period.
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Using aggregate_temporal_period we can achieve this easily:
The apply operator employ a process on the datacube that calculates new pixel values for each pixel, based on n other pixels.
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Let’s start again with the same sample data from the Sentinel-2 STAC Collection, applying the filters directly in the load_stac call, to reduce the amount of data.
Did not load machine learning processes due to missing dependencies: Install them like this: `pip install openeo-processes-dask[implementations, ml]`
+/home/749c4c89-469c-4d4a-9ce4-9feffea0504a/.local/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
+ times = pd.to_datetime(
+/home/749c4c89-469c-4d4a-9ce4-9feffea0504a/.local/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
+ times = pd.to_datetime(
+
/home/749c4c89-469c-4d4a-9ce4-9feffea0504a/.local/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
+ times = pd.to_datetime(
+Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
+
/home/749c4c89-469c-4d4a-9ce4-9feffea0504a/.local/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
+ times = pd.to_datetime(
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The exercise will use the openEO Python Client Side Processing functionality, which allows to experiment using openEO without a connection to an openEO back-end.
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Quiz hint: remeber this information for the final quiz!
When accessing data using an API, most of the time the data is lazily loaded.
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It means that only the metadata is loaded, so that it is possible to know about the data dimensions and their extents (spatial and temporal), the available bands and other additional information.
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Let’s start with a call to the openEO process load_stac for lazily loading some Sentinel-2 data from a public STAC Collection. Please note that not every STAC Collection or Item is currently supported.
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We need to specify an Area Of Interest (AOI) to get only part of the Collection, otherwise our code would try to load the metadata of all Sentinel-2 tiles available in the world!
Calling the .execute() method, the data will be lazily loaded and an xArray.DataArray object returned.
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Running the next cell will show the selected data content with the dimension names and their extent:
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datacube.execute()
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From the output of the previous cell you can notice something really interesting: the size of the selected data is more than 3 TB!
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But you should have noticed that it was too quick to download this huge amount of data.
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This is what lazy loading allows: getting all the information about the data in a quick manner without having to access and download all the available files.
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Quiz hint: look carefully at the dimensions of the loaded datacube!
When computing statistics over time or indices based on multiple bands, it is possible to use reduce operators.
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In openEO we can use the reduce_dimension process, which applies a reducer to a data cube dimension by collapsing all the values along the specified dimension into an output value computed by the reducer.
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Reduce the temporal dimension to a single value, the mean for instance:
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importopeneo
+fromopeneo.processesimportclip
+fromopeneo.localimportLocalConnection
+local_conn=LocalConnection('')
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+url="https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a"
+spatial_extent={"west":11.259613,"east":11.406212,"south":46.461019,"north":46.522237}
+temporal_extent=["2021-05-28T00:00:00Z","2021-06-30T00:00:00Z"]
+bands=["red","nir"]
+datacube=local_conn.load_stac(url=url,
+ spatial_extent=spatial_extent,
+ temporal_extent=temporal_extent,
+ bands=bands)
+datacube=datacube.apply(lambdax:clip(x,0,10000))# Get rid of possible negative values
+
+datacube_mean_time=datacube.reduce_dimension(dimension="time",reducer="mean")
+datacube_mean_time
+
+
+
+
+
Check what happens to the datacube inspecting the resulting xArray object:
+
+
+
datacube_mean_time.execute()
+
+
+
+
+
It is possible to reduce in the same way all the available dimensions of the datacube.
+
We can, for instance, reduce the band dimension similarly as we did for the temporal dimension:
Remember that calling .compute() on an xarray + dask based object will load into memory the data.
+In this case it will trigger the download of the data from the STAC Catalog and the processing defined as openEO process graph, computing the NDVI time series.
Cloud native formats or cloud-optimized formats, are file formats specifically designed to optimize the storage, access, and processing of geospatial data in cloud computing environments. These formats are tailored to leverage the scalability, flexibility, and parallel processing capabilities of cloud infrastructure, enabling efficient handling of large-scale datasets.
+
+
+
Video content in cooperation with Aimee Barciauskas (DevelopmentSeed) and Ryan Avery (DevelopmentSeed).
+“Cloud-optimised means organizing so subsets of data can be read. Ideally, the data is also compressed. Both of these factors minimize the amount of data that has to be transferred across a network.”
Cloud-optimized means mainly optimized “read” access with partial reads and also parallel reads. Main characteristics common for cloud-optimized formats:
+
+
Data Chunking: Cloud native formats employ a chunk-based organization, where the data is divided into smaller chunks or blocks. This enables parallel processing and efficient retrieval of specific portions of the data, reducing the need to access the entire dataset.
+
Internal Indexing: These formats incorporate internal indexing structures that facilitate fast spatial and attribute queries. This enables efficient data access and retrieval operations without the need for extensive scanning or processing of the entire dataset.
+
Metadata Optimization: Cloud native formats optimize metadata storage and indexing, allowing for efficient access and retrieval of metadata associated with the data at once. This supports faster discovery and interpretation of data properties and characteristics.
+
Compression and Tiling: Cloud native formats often employ advanced compression techniques to reduce storage requirements while maintaining data quality. Additionally, they utilize tiling strategies to divide the data into smaller, manageable tiles that can be independently accessed and processed.
+
+
HTTP Range Request allows clients to request only a specific portion or range of data instead of a complete dataset.
COG - Cloud-Optimized GeoTIFF (COG) is an optimized version of the GeoTIFF format. It organizes raster data into chunks, utilizes internal tiling and compression, and uses HTTP range requests for efficient data access in the cloud.
+
ZARR Zarr is a format specifically designed for storing and accessing multidimensional arrays. It supports chunking, compression, and parallel processing, making it suitable for large-scale geospatial datasets, for example, weather data. Metadata is stored externally in data files itself.
+
FlatGeoBuf Cloud optimized vector data format. It is a binary encoding format for geodata and holds a collection of Simple Features.
Mention resource usage on cloud platforms. Track resources. Every processing has it’s cost.
+
+
Scaling refers to the process of increasing or decreasing the capacity or size of a system to handle a larger or smaller workload or data volume. Scaling does not necessarily means only in the direction of larger and bigger but also saving unnecessary costs in times when there is no traffic. In our context, scaling involves managing the growth of data, traffic, or processing requirements to ensure optimal performance and availability.
Two classical approaches to scaling are horizontal and vertical:
+
+
Horizontal scaling: Also known as scaling out, horizontal scaling involves adding more machines or nodes to distribute the workload across a larger number of resources. This could mean adding more servers or instances to handle increased traffic or data processing demands. Horizontal scaling offers the advantage of improved fault tolerance and increased capacity, as the workload is spread across multiple resources.
+
Vertical scaling: Also known as scaling up, vertical scaling involves increasing the capacity of an individual machine or resource. This can be achieved by upgrading the hardware, such as adding more powerful processors, memory, or storage, to handle the growing demands of the geospatial application. Vertical scaling is often suitable for applications with single-node architectures or when the workload cannot be easily distributed across multiple machines.
+
+
Both horizontal and vertical scaling have their advantages and considerations. Horizontal scaling provides better scalability and fault tolerance, as it can handle increased traffic and processing by adding more resources. However, it may require additional effort to distribute and synchronize data across multiple nodes. Vertical scaling, on the other hand, simplifies data management as all resources are contained within a single node, but it may have limitations in terms of the maximum capacity a single machine can handle.
+
In common workflows, a combination of both approaches is used to ensure optimal speed and resource utilization while being able to keep the simplicity of a workflow.
Subscription: A subscription model involves a fixed, recurring payment made by the user to access and utilize the cloud platform’s services over a specific period, typically monthly or annually. Under a subscription model, users typically commit to a predetermined level of resources and pay a regular fee regardless of the actual usage during that period. This model often provides cost predictability and may offer discounts or benefits for long-term commitments. Users can usually choose from various options and combinations of resources (eg. GPU, CPU, disk storage combinations).
+
Advantages of the Subscription Model:
+
+
Cost Predictability: Users have a clear understanding of the ongoing costs as they pay a fixed fee.
+
Potential Cost Savings: Subscriptions may offer discounts or cost benefits for longer-term commitments.
+
Continuous Service Access: Users have continuous access to the subscribed services without the need for frequent renewal or payment management.
+
+
On-Demand Usage: In an on-demand model, users pay for the cloud platform’s services based on actual usage and consumption. Users are charged on a pay-as-you-go basis, where they pay for the resources or services they utilize in a given period. There are no long-term commitments or fixed fees. This model offers flexibility and scalability, allowing users to scale resources up or down as per their needs.
+
Advantages of On-Demand Usage:
+
+
Flexibility: Users have the flexibility to adjust resources based on their varying demands, scaling up or down as required.
+
Cost Efficiency: Users only pay for what they use, making it suitable for workloads with unpredictable or fluctuating resource needs.
+
No Long-Term Commitments: On-demand models do not require users to commit to a specific period or predefined resource levels, allowing for agility and quick adjustments.
+
+
Choosing between subscription and on-demand models depends on various factors, including the nature of your workloads, budget considerations, and usage patterns. Based on this (and data availability) users can choose a platform that suits their needs best. Reviewing the pricing details is an important part before selecting a working environment.
While scaling is providing many options and is essential for achieving results on a larger scale, there are some limitations to keep in mind and activities to even avoid.
+
Costs: One of the main characteristics to consider are costs of computing. Scaling resources dynamically can lead to increased costs, especially if not properly managed. It is essential to monitor resource usage and set appropriate maximum scaling policies to ensure cost optimization. Failure to do so may result in unnecessary provisioning of resources, leading to higher expenses. The purchase of many computational resources can be easy, but very costly. Code optimization is important to ensure there are no memory leaks, unnecessary data storage, and other expensive operations.
+
Data Access: In geospatial cloud workflows, one of the big challenges lies in data access and optimal data storage. The easy trap is in loading large portions of unnecessary data without applying correct filters ahead. Such data volumes can lead to more requirements on RAM or disk space resulting in higher costs of processing or longer times (and more computational time) just to load the data.
+
Accessing data as files: Geospatial data are stored in many formats as discussed in this lesson and some are more appropriate to access in the cloud. The opportunity of first evaluating metadata before loading the whole dataset is great for saving time and money.
+
Latency and Data Transfer: In distributed and scaled-out architectures, managing data transfer and minimizing latency can be crucial. Moving data between services or instances across different locations can introduce network overhead and impact application performance. Efficient data caching, or data partitioning strategies can help mitigate these challenges.
+
Scaling Limits in the platform: While cloud platforms offer high scalability, there are still practical limits to consider. Every service or resource has its scalability limits, such as maximum instance count, storage capacity, or network throughput. It is important to understand these limitations and design your programs and applications accordingly.
+
To mitigate these challenges and limitations, it’s advisable to thoroughly plan and architect your application for scalability, leverage cloud-native tools and services, monitor resource usage and costs, and regularly test and optimize your scaling strategies. Additionally, staying updated with the latest advancements in cloud technologies and best practices will help you navigate the complexities of cloud-native scaling more effectively.
+
+
Animated Content: Tiling and application (drag and drop)#
[(X)] Cloud native data formats in EO should be compatible to cloud services (APIs, http requests, cloud storage), enable fast viewing and access to spatial sub regions.
+[( )] Cloud native data formats in EO are exclusively designed to be compressed as much as possible, so that the least amount of storage space is necessary.
+[( )] Cloud native data formats in EO have to be human readable when you open them in a text editor.
+
Consider a cloud provider, that should decide if compressing the data on its storage would let it spare money.
+
If the compression process of a COG takes 0.05 CPU hour every 1 GB of data, the total amount of COGs on the storage takes 1200 TB and one CPU hour costs 0.05€ for the first 10000 CPU hour and then 0.03€ for the rest, how much would the compression process cost?
The amount of data on the storage is 1200 TB * 1000 GB/TB = 1200000 GB
+
If to compress 1 GB of data takes 0.05 CPU hour, to compress 1200000 GB it will take: 1200000 GB * 0.05 CPU hour / GB = 60000 CPU hour
+
The first 10000 CPU hour cost 10000 CPU hour * 0.05 €/CPU hour = 500€
+
The rest is 50000 CPU hour and it will cost 50000 CPU hour * 0.03 €/CPU hour = 1500 €
+
The total is 500 € + 1500 € = 2000 €
+
+
In the same scenario, now we also consider the storage maintanance cost. If each TB of storage has a maintenance cost of 0.5 € every month, how much time would it take before the compression would let the cloud provider spare money? Consider a compression rate of 70%.
Without compression, every month the storage would cost: 1200 TB * 0.5 €/TB = 600 €
+
With a compression rate of 70%, the data would use 1200 TB * 0.7 = 840 TB of disk space
+
With compression, every month the storage would cost: 840 TB * 0.5 €/TB = 420 €
+
With compression, each month it’s possible to spare 180 €.
+
Since the compression process costs 2000 €, and each month costs 180 € less with it, it would take 2000 € / 180 €/month ~= 11 months to start to spare money.
+
+
Calculate the volume of data needed for the snow workflow according to the collections, time steps, bands, resolutions, spatial extent selected in the previous chapter, value types, format, compression estimate, etc. (this only if we define snow workflow already here) - Don’t use snow workflow -> Set that information
In this lecture we are going to combine the knowledge and hands-on experience we have gathered so far to create a full EO workflow on a cloud platform.
+We will
+
+
define a research question,
+
choose and load the necessary data sources,
+
define the data cube to our needs,
+
use functions to process the data,
+
visualize the result
+
and track the resources we are consuming on the platform.
Video content in cooperation with Matteo Dall’Amico (MobyGIS - Waterjade) and Federico Di Paolo (MobyGIS - Waterjade).
+“How much snow is stored in that mountain, when will it melt?” We answered this question using EO cloud platforms! Have a look: EO4Alps Snow
Snow serves as a water reservoir and is thus important for any hydrological management activity, such as irrigation planning, drink water supply or hydro power generation. Knowing precisely, when and where snow is present is a critical source of information for these acitivities. Satellite earth observation plays an important role in describing the snow cover, both globally and in local mountain ranges. This is due to it’s ability to sense information throughout space (complete coverage of the globe) and time (repeated measurements). Our goal is to create a time series of the snow covered area of the catchment of interest. We will use this time series to compare it to the run off at the main outlet of the catchment. And study the relationship between snow dynamics and runoff.
In this exercise we are going to derive the snow cover in an alpine catchment using Sentinel-2 data. Sentinel-2 carries an optical sensor, it is measuring the reflected light of the earths surface in differenct wavelenghts. At a 20 m spatial resolution and at a 6 day repeat rate. We are using the Green and SWIR bands to calculate the Normalized Difference Snow Index (NDSI). It is calculated as follows:
Snow typically has very high visible (VIS) reflectance and very low reflectance in the shortwave infrared (SWIR), a characteristic used to detect snow by distinguishing between snow and most cloud types. The NDSI expresses this phenomenon as a formula. It results in a value between -1 and 1. The higher the value is, the more probable it is that the surface is covered with snow. In order to create a binary snow map we apply a threshold of \(NDSI < 0.4\). This is a commonly used value for discriminating snowcovered and snow free areas. Then we spatially aggregate the snow free and snow covered pixels in the catchment area by summing them up. In order to get the snow covered area of the catchment we multiply the number of snow covered pixels by the pixel resolution. Additionally, we have to deal with cloud cover. We use the Sentinel-2 cloud mask that is provided with the data and exclude all images that have a cloud cover over 25 % in our study area. Ideally we should fill the gaps the clouds generate, since they are introducing uncertainty. Nevertheless, for a first try our approach should be good enough to get a general idea about the snow cover in our area of interest. In the end we receive a time series with the snow covered area in the catchment.
+The approach we are using is very basic. There are many assumptions and simplifications involved. A critical analysis of the workflow and possible improvements follows in Section 3.3 Validation.
What is the city at the outlet of the catchment? Answer in the exercise: 31_data_processing.ipynb section ‘Region of Interest’
+
[(x)] Meran
+[( )] Innsbruck
+[( )] Grenoble
+
+
+
How many images are available in the time range (“2018-02-01” and “2018-06-30”)? Answer in the exercise: 31_data_processing.ipynb section ‘Calculate Catchment Statistics’, might require you to run some additional code blocks
+
[( )] 0-20
+[( )] 21-40
+[(x)] 41-60
+
+
+
How many pixels are in the spatially aggregated data cube? (time * x * y * bands) Answer in the exercise: 31_data_processing.ipynb section ‘Calculate Catchment Statistics’
How many resources did the computation of the data cube take? Answer in the exercise: 31_data_processing.ipynb section ‘Calculate Catchment Statistics’
+
[(x)] 0 - 20
+[( )] 21 - 100
+[( )] 101 - 200
+
+
+
How many snow covered pixels are there across all time steps? Answer in the exercise: 31_data_processing.ipynb section ‘Calculate Catchment Statistics’
In this exercise we will build a complete EO workflow on a cloud platform; from data access to obtaining the result. In this example we will analyse snow cover in the Alps.
+
We are going to follow these steps in our analysis:
+
+
Load satellite collections
+
Specify the spatial, temporal extents and the features we are interested in
+
Process the satellite data to retrieve snow cover information
+
aggregate information to get catchment statistics over time
We want to use the Sentinel-2 L2A product. It’s name is 'SENTINEL2_L2A'.
+
We get the metadata for this collection as follows. This is an important step to familiarize yourself with the data collection (e.g. learn the band names).
We define all extents of our data cube. We use the catchment as spatial extent. As a time range we will focus on the snow melting season 2018, in particular from Febraury to June 2018. We are only interested in the green and short wave infrared band, band 3 and 11. And we directly remove time slices with a cloud cover >= 90 %.
The Normalized Difference Snow Index (NDSI) is computed as:
+
+\[ NDSI = \frac {GREEN - SWIR} {GREEN +SWIR} \]
+
We have created a Sentinel-2 data cube with bands B03 (green) and B11 (SWIR). We will use the green and SWIR band to calculate a the NDSI. This process is reducing the band dimension of the data cube to generate new information, the NDSI.
So far we have a time series map of NDSI values. We are intereseted in the presence of snow though. Ideally in a binary classification: snow and no snow.
+To achieve this we are setting a threshold of 0.4 on the NDSI. This gives us a binary snow map.
+
+
+
snowmap=(ndsi>0.4)*1.0# the addition of "* 1.00" is a workaround for a backend specific implementation problem. Once solved on the CDSE openEO backend it could be removed
+snowmap
+
We are going to use the Scene Classification of Sentinel-2, called the “SCL” band, for creating a cloud mask and then applying it to the NDSI. The values we are interested in are: 8=cloudmediumprobability, 9=cloudhighprobability, 3=cloudshadow
We have a cloud masked snow map data cube now. In order to keep only pixels within the exact chatchment boundaries we mask to the outline of the catchment. Values outside of the boundaries are set to NA.
Let’s have a first look at a time slice of our snow map. So far we have not computed anything. We have only defined a set of functions that are going to be applied in sequence. This makes up our workflow or processing graph.
+To reduce the data volume which we are going to download we are only selecting one time step of our data cube.
+
In order to start the processing we have to tell the cloud platform specifically that we want to execute our workflow. In this case we want to start the processing directly without registering a job on the backend. This solution is good for small amounts of data. For larger processing tasks batch jobs are preferred (we’ll do that later).
Once the processing is done on the cloud and the data is downloaded we can load the file into our working environment and plot it!
+
The area of interest is spread across two S2 tiles. This is visibile in the northern part of the plot because we chose one specific acquisition date where there is not data available for the northern tile.
We are looking at the snow cover of a region over time and want to extract aggregated catchment statistics on snow cover and cloud cover. We do this by counting all the pixels in the catchment, counting the pixels that are covered by snow and the pixels covered by clouds.
+
Ultimately we are interested in the snow covered area (SCA) within the catchment. We count all snow covered pixels within the catchment for each time step. Multiplied by the pixel size that would be the snow covered area. The snow pixel count divided by the total number of pixels in the catchment is the percentage of pixels covered with snow. We will use this number.
+
We need to make sure that the information content meets our expected quality. Therefore, we calculate the cloud percentage for the catchment for each timestep. We use this information to filter the timeseries. All timesteps that have a cloud coverage of over 25% will be discarded.
+
We are going to
+
+
Get number of pixels in the catchment: total, clouds, snow.
+
Combine the three aggregated pixel counts into one data cube.
+
Calculate cloud and snow percentages
+
Filter cloudy time steps with the cloud percentage
+
Plot the resulting time series
+
+
Quiz hint: remember the pixel counts here for the final exercise
+
+
Count pixels and aggregate spatially to the catchment#
+
+
+
# number of all pixels
+n_catchment=((snowmap_cloudfree>-1)*1.0).add_dimension(name="bands",type="bands",label="n_catchment")
+
+# number of cloud pixels (no function needed, mask already created before)
+n_cloud=cloud_mask.add_dimension(name="bands",type="bands",label="n_cloud")
+
+# number of snow pixels
+n_snow=((snowmap_cloudfree==1)*1.0).add_dimension(name="bands",type="bands",label="n_snow")
+
+# combine the binary data cubes into one data cube
+n_catchment_cloud_snow=n_catchment.merge_cubes(n_cloud).merge_cubes(n_snow)
+
+# aggregate to catchment
+n_pixels=n_catchment_cloud_snow.aggregate_spatial(geometries=catchment_outline['geometry'][0],reducer='sum')
+n_pixels
+
We are starting the processing now with a batch job. This registers our job on the backend in our user space and assigns further information to the job, such as an ID, the job status, the process graph and further metadata. First we specifiy the end of our process graph with save_result() and specifiy the format (since we aggregated over the spatial dimension we will receive three arrays of data. So JSON is a suitable format). Then we create the batch job and start it.
+
+
+
# Define the end of the process graph and the output format
+n_pixels_json=n_pixels.save_result(format="JSON")
+# Create a batch job
+job=n_pixels_json.create_job(title="n_pixels_json")
+# start the job and wait till it finishes
+job.start_and_wait()
+
Quick hint: what is the length of the time series JSON?
+len(n_pixels_json)
+
Now we do some data wrangling to get a structured data frame.
+
+
+
# Create a Pandas DataFrame to hold the values
+dates=[kforkinn_pixels_json]
+n_catchment_vals=[n_pixels_json[k][0][0]forkinn_pixels_json]
+n_cloud_vals=[n_pixels_json[k][0][1]forkinn_pixels_json]
+n_snow_vals=[n_pixels_json[k][0][2]forkinn_pixels_json]
+
+data={
+ "time":pd.to_datetime(dates),
+ "n_catchment_vals":n_catchment_vals,
+ "n_cloud_vals":n_cloud_vals,
+ "n_snow_vals":n_snow_vals
+ }
+df=pd.DataFrame(data=data).set_index("time")
+# Sort the values by date
+df=df.sort_values(axis=0,by="time")
+df[:3]
+
+
+
+
+
+
+
+
+
+
+
n_catchment_vals
+
n_cloud_vals
+
n_snow_vals
+
+
+
time
+
+
+
+
+
+
+
+
2018-02-06 00:00:00+00:00
+
1489702.0
+
253542.0
+
619519.0
+
+
+
2018-02-08 00:00:00+00:00
+
4201607.0
+
183099.0
+
2907006.0
+
+
+
2018-02-11 00:00:00+00:00
+
4201607.0
+
60947.0
+
2943057.0
+
+
+
+
+
+
+
+
df.n_snow_vals.sum()
+
+
+
+
+
55525116.0
+
+
+
+
+
+
+
Calculate the cloud percentage for filtering time steps#
+
Divide the number of cloudy pixels by the number of total pixels = cloud percentage
Quick hint: The sum of the n_catchment_vals should give an overall idea of the total number of pixels in the datacube for the whole time-seriesdf.n_catchment_vals.sum()
+
Quick hint: a filter of the snow values can give an idea of when the maximum snow cover occurreddf.where(df.n_snow_vals==df.n_snow_vals.max())
+
Quick hint: a simplified approach for converting from pixel count to square kilometres is to use this simplified formula::
In this exercise we will build a complete EO workflow on a cloud platform; from data access to obtaining the result. In this example we will analyse snow cover in the Alps.
+
We are going to follow these steps in our analysis:
+
+
Load satellite collections
+
Specify the spatial, temporal extents and the features we are interested in
+
Process the satellite data to retrieve snow cover information
We define all extents of our data cube. We use the catchment as spatial extent. As a time range we will focus on the snow melting season 2018, in particular from Febraury to June 2018.
The Normalized Difference Snow Index (NDSI) is computed as:
+
+\[ NDSI = \frac {GREEN - SWIR} {GREEN +SWIR} \]
+
We have created a Sentinel-2 data cube with bands B03 (green), B11 (SWIR) and the cloud mask (CLM). We will use the green and SWIR band to calculate a the NDSI. This process is reducing the band dimension of the data cube to generate new information, the NDSI.
So far we have a timeseries of NDSI values. We are intereseted in the presence of snow though. Ideally in a binary classification: snow and no snow.
+To achieve this we are setting a threshold of 0.42 on the NDSI. This gives us a binary snow map.
We are going to use “SCL” band for creating a cloud mask and then applying it to the NDSI.
+8=cloudmediumprobability, 9=cloudhighprobability, 3=cloudshadow
We are looking at a region over time. We need to make sure that the information content meets our expected quality. Therefore, we calculate the cloud percentage for the catchment for each timestep. We use this information to filter the timeseries. All timesteps that have a cloud coverage of over 25% will be discarded.
+
Ultimately we are interested in the snow covered area (SCA) within the catchment. We count all snow covered pixels within the catchment for each time step. Multiplied by the pixel size that would be the snow covered area. Divided the pixel count by the total number of pixels in the catchment is the percentage of pixels covered with snow. We will use this number.
+
Get number of pixels in catchment: total, clouds, snow.
+
+
+
# number of all pixels
+n_catchment=((snowmap_cloudfree>-1)*1.0).add_dimension(name="bands",type="bands",label="n_catchment")
+
+# number of cloud pixels (no function needed, mask already created before)
+n_cloud=cloud_mask.add_dimension(name="bands",type="bands",label="n_cloud")
+
+# number of snow pixels
+n_snow=((snowmap_cloudfree==1)*1.0).add_dimension(name="bands",type="bands",label="n_snow")
+
+# combine the binary data cubes into one data cube
+n_catchment_cloud_snow=n_catchment.merge_cubes(n_cloud).merge_cubes(n_snow)
+
+# aggregate to catchment
+n_pixels=n_catchment_cloud_snow.aggregate_spatial(geometries=catchment_outline['geometry'][0],reducer='sum')
+n_pixels
+
+
+
+
+
+
+
+
+
+
+
+
Create batch job to start processing on the backend.
+
+
+
# Create a batch job
+n_pixels_json=n_pixels.save_result(format="JSON")
+job=n_pixels_json.create_job(title="n_pixels_json")
+job.start_job()
+
In this exercise we will build a complete EO workflow using cloud provided data (STAC Catalogue), processing it locally; from data access to obtaining the result. In this example we will analyse snow cover in the Alps.
+
We are going to follow these steps in our analysis:
+
+
Load satellite collections
+
Specify the spatial, temporal extents and the features we are interested in
+
Process the satellite data to retrieve snow cover information
Did not load machine learning processes due to missing dependencies: Install them like this: `pip install openeo-processes-dask[implementations, ml]`
+
We define all extents of our data cube. We use the catchment as spatial extent. As a time range we will focus on the snow melting season 2018, in particular from Febraury to June 2018.
We know that the catchment area is almost fully covered by the Sentinel-2 32TPS tile and therefore we use this information in the properties filter, along with a first filter on the cloud coverage.
The Normalized Difference Snow Index (NDSI) is computed as:
+
+\[ NDSI = \frac {GREEN - SWIR} {GREEN +SWIR} \]
+
We have created a Sentinel-2 data cube with bands B03 (green), B11 (SWIR) and the scene classification mask (SCL). We will use the green and SWIR band to calculate a the NDSI. This process is reducing the band dimension of the data cube to generate new information, the NDSI.
So far we have a timeseries of NDSI values. We are intereseted in the presence of snow though. Ideally in a binary classification: snow and no snow.
+To achieve this we are setting a threshold of 0.42 on the NDSI. This gives us a binary snow map.
We are going to use “SCL” band for creating a cloud mask and then applying it to the NDSI.
+8=cloudmediumprobability, 9=cloudhighprobability, 3=cloudshadow
We are looking at a region over time. We need to make sure that the information content meets our expected quality. Therefore, we calculate the cloud percentage for the catchment for each timestep. We use this information to filter the timeseries. All timesteps that have a cloud coverage of over 25% will be discarded.
+
Ultimately we are interested in the snow covered area (SCA) within the catchment. We count all snow covered pixels within the catchment for each time step. Multiplied by the pixel size that would be the snow covered area. Divided the pixel count by the total number of pixels in the catchment is the percentage of pixels covered with snow. We will use this number.
+
Get number of pixels in catchment: total, clouds, snow.
Did not load machine learning processes due to missing dependencies: Install them like this: `pip install openeo-processes-dask[implementations, ml]`
+
<xvec.index.GeometryIndex object at 0x7f8a7817a650>
crs :
epsg:32632
reduced_dimensions_min_values :
{'band': 'B03'}
+
+
Plot the timeseries and the cloud threshold of 25%. If the cloud cover is higher the timestep will be excluded later on.
+
Plot the cloud percentage with the threshold.s
+
+
+
cloud_percent=(snow_cloud_map_timeseries_xr.loc[dict(band="cloud_mask")]/snow_cloud_map_timeseries_xr.loc[dict(band="valid_px")])*100
+cloud_percent.plot(marker='o')
+# plot the cloud percentage and a threshold
+plt.axhline(y=25,color="r",linestyle="-")
+plt.show()
+
Science is much more impactful once it’s shared. Therefore, we are going to learn how to
+open up our scientific output from a cloud platform, so that is openly available - and
+has the chance to make the impact it should.
importmath
+
+# Function to create a bounding box around a point
+defcreate_bounding_box(latitude,longitude,distance_km):
+ # Radius of the Earth in kilometers
+ earth_radius_km=6371
+
+ # Convert latitude and longitude from degrees to radians
+ lat_rad=math.radians(latitude)
+ lon_rad=math.radians(longitude)
+
+ # Calculate the angular distance covered by the given distance_km
+ angular_distance=distance_km/earth_radius_km
+
+ # Calculate the latitude and longitude offsets
+ lat_offset=math.degrees(angular_distance)
+ lon_offset=math.degrees(angular_distance/math.cos(lat_rad))
+
+ # Calculate the coordinates of the southwest and northeast corners
+ sw_lat=latitude-lat_offset
+ sw_lon=longitude-lon_offset
+ ne_lat=latitude+lat_offset
+ ne_lon=longitude+lon_offset
+
+ # Return the bounding box as a tuple (sw_lat, sw_lon, ne_lat, ne_lon)
+ return(sw_lat,sw_lon,ne_lat,ne_lon)
+
In this exercise, we focus on the validation of the results we have produced. In general, the accuracy of a satellite derived product is expressed by comparing it to in-situ measurements. Furthermore, we will compare the resuling snow cover time series to the runoff of the catchment to check the plausibility of the observed relationship.
+
The steps involved in this analysis:
+
+
Generate Datacube time-series of snowmap,
+
Load in-situ datasets: snow depth station measurements,
+
Pre-process and filter in-situ datasets to match area of interest,
/tmp/ipykernel_1286/2944796695.py:4: DeprecationWarning:
+Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
+(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
+but was not found to be installed on your system.
+If this would cause problems for you,
+please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466
+
+ import pandas as pd
+
We have prepared the workflow to generate the snow map as a python function calculate_sca(). The calculate_sca() is from _32_cubes_utilities and is used to reproduce the snow map process graph in openEO
Load the in-situ datasets, snow depth station measurements. They have been compiled in the ClirSnow project and are available here: Snow Cover in the European Alps with stations in our area of interest.
+
We have made the data available for you already. We can load it directly.
+
+
+
# load snow station datasets from zenodo:: https://zenodo.org/record/5109574
+station_df=pd.read_csv("32_data/data_daily_IT_BZ.csv")
+station_df=station_df.assign(Date=station_df.apply(format_date,axis=1))
+# the format_date function, from _32_cubes_utilities was used to stringify each Datetime object in the dataframe
+# station_df.head()
+
+
+
+
+
+
+
# load additional metadata for acessing the station geometries
+station_df_meta=pd.read_csv("32_data/meta_all.csv")
+station_df_meta.head()
+
+
+
+
+
+
+
+
+
+
+
Provider
+
Name
+
Longitude
+
Latitude
+
Elevation
+
HN_year_start
+
HN_year_end
+
HS_year_start
+
HS_year_end
+
+
+
+
+
0
+
AT_HZB
+
Absdorf
+
15.976667
+
48.401667
+
182.0
+
1970.0
+
2016.0
+
1970.0
+
2016.0
+
+
+
1
+
AT_HZB
+
Ach_Burghausen
+
12.846389
+
48.148889
+
473.0
+
1990.0
+
2016.0
+
1990.0
+
2016.0
+
+
+
2
+
AT_HZB
+
Admont
+
14.457222
+
47.567778
+
700.0
+
1970.0
+
2016.0
+
1970.0
+
2016.0
+
+
+
3
+
AT_HZB
+
Afritz
+
13.795556
+
46.727500
+
715.0
+
1970.0
+
2016.0
+
1970.0
+
2016.0
+
+
+
4
+
AT_HZB
+
Alberschwende
+
9.849167
+
47.458333
+
717.0
+
1982.0
+
2016.0
+
1982.0
+
2016.0
+
+
+
+
+
+
+
+
Pre-process and filter in-situ snow station measurements#
Filter the in-situ datasets to match the snow-map time series using the function station_temporal_filter() from cubes_utilities.py, which merges the station dataframe with additional metadata needed for the Lat/Long information and convert them to geometries
The stations are measuring snow depth. We only need the binary information on the presence of snow (yes, no). We use the binarize_snow() function from cubes_utilities.py to assign 0 for now snow and 1 for snow in the snow_presence column.
We exgtract the SCA value of our data cube at the buffered station locations. Therefore we use the process aggregate_spatial() with the aggregation method median(). This gives us the most common value in the buffer (snow or snowfree).
Combine station measurements and the extracted SCA from our data cube#
+
The station measurements are daily and all of the stations are combined in one csv file.
+The extracted SCA values are in the best case six-daily (Sentinel-2 repeat rate) and also all stations are in one json file.
+We will need to join the the extracted SCA with the station measurements by station (and time (selecting the corresponding time steps)
+
+
Extract snow values from SCA extracted at the station location#
Validate the SCA results with the snow station measurements#
+
Now that we have combined the SCA results with the snow station measurements we can start the actual validation. A confusion matrix compares the classes of the station data to the classes of the SCA result. The numbers can be used to calculate the accuracy (correctly classified cases / all cases).
The accuracy of the snow estimate from the satellite image computation for each station is shown below:
+
+
+
On-site snow station
+
Accuracy
+
+
+
+
San Martino in Passiria Osservatore
+
100.00%
+
+
Rifiano Beobachter
+
100.00%
+
+
Plata Osservatore
+
82.61%
+
+
San Leonardo in Passiria Osservatore
+
NaN
+
+
Scena Osservatore
+
96.15%
+
+
+
+
+
The fifth and last station San Leonardo in Passiria Osservatore recorded NaNs for snow depths for our selected dates, which could potentially be as a results of malfunctioning on-site equipments. Hence, we are not able to verify for it. But overall, the validation shows a 100% accuracy for stations San Martino in Passiria Osservatore and Rifiano Beobachter, while station Plata Osservatore has a lot more False Positive (4) than the other stations.This shows a good match between estimated snow values from satellite datasets and on-the ground measurements of the presence of snow.
In addition to computing metrics for validating the data, we also check the plausibility of our results. We compare our results with another measure with a known relationship. In this case, we compare the snow cover area time series with the discharge time-series at the main outlet of the catchment. We suspect that after snow melting starts, with a temporal lag, the runoff will increase. Let’s see if this holds true.
Load the SCA time series we have generated in a previous exercise. It’s the time series of the aggregated snow cover area percentage for the whole catchment.
Let’s plot the relationship between the snow covered area and the discharge in the catchment.
+
+
+
start_date=date(2018,2,1)
+end_date=date(2018,6,30)
+# filter discharge data to start and end dates
+discharge_ds=discharge_ds.loc[start_date:end_date]
+
+ax1=discharge_ds.discharge_m3_s.plot(label='Discharge',xlabel='',ylabel='Discharge (m$^3$/s)')
+ax2=snow_perc_df["perc_snow"].plot(marker='o',secondary_y=True,label='SCA',xlabel='',ylabel='Snow cover area (%)')
+ax1.legend(loc='center left',bbox_to_anchor=(0,0.6))
+ax2.legend(loc='center left',bbox_to_anchor=(0,0.5))
+plt.show()
+
+
+
+
+
+
+
+
The relationship looks as expected! Once the snow cover decreases the runoff increases!
The validation process typically involves comparing a model or a developed Earth Observation (EO) product with reference data, and the level of agreement is assessed using validation metrics. Validating EO products is crucial to prevent misinterpretation or error propagation when utilizing the data for purposes such as area quantification, subsequent modeling, or planning, particularly in contexts like nature conservation or risk assessment. However, validating EO products poses significant challenges.
+
In this tutorial, we will explain how to derive validation metrics and how to interpret them. Our primary focus will center on the difficulties and limitations inherent in the accuracy assessment process, especially in the context of large-scale (global) mapping products based on Earth Observation.
Identify sources of uncertainty in the applied workflow
+
Process graph with pop-ups of sources of uncertainties
+
Strategies of how to improve
+
+
Now that we have carried out a very basic approach to solve our research question we should take some time to identify possible sources of uncertainty and think about how to improve them:
+
+
Optical earth observation has some inherent drawbacks, most importantly: clouds. Especially in mountain regions.
+
+
We are excluding images where a certain cloud coverage is exceeded. There would still be some information available.
+
We are not filling in the gaps that clouds generate. This leaves us with some uncertainty.
+
+
+
Use data fusion techniques and include SAR data, that can penetrate the clouds.
+
+
Sentinel-2 has a 6 day repeat rate. This means we do not know what happens with the snow cover in between two acquisitions.
+
Use data fusion techniques and include other optical sensors and SAR data
+
+
+
Use physical snow models or heuristics to estimate the snow cover in between
+
+
We are using a threshold for discriminating between snow and no snow. Changing this arbitrary value will influence our results.
+
There are better, more complex ways to identify snow.
+
+
+
Snow Cover does not represent the amount of snow.
+
+
Therefore we would need to calculate the snow depth.
Reference data for EO are commonly obtained through field surveys or visual expert assessments of the underlying EO data. These reference datasets play a dual role, serving not only for validation purposes but also as essential components for training models, particularly when the EO product relies on predictions from a data-driven model. Consequently, when referring to reference data, a broad distinction is made between training and test data. The training dataset is employed in the model development phase, while the test dataset is crucial for evaluating the quality of the resulting product, specifically assessing the accuracy of predictions generated by the model.
Many EO products are generated using data-driven models, which can range from simple rule-based models to more complex machine learning models. In the process of creating such EO products, two distinct validation steps are crucial: model validation and map validation.
+During model validation, we evaluate the model’s ability to predict the target variable (e.g., snow cover) based on EO data (e.g., optical satellite imagery). This evaluation often involves cross-validation, where the training data are divided into multiple folds. Iteratively, one fold is withheld during model training, and these reserved data are used to test the model’s performance in predicting unseen data. Cross-validation typically includes tuning the model (adjusting hyperparameters, selecting variables) to identify the optimal model for predicting the held-back data.
+If (and only if) the training data and the derived cross-validation folds are representative for the prediction area (see discussion later), the cross-validation performance may be used as an indicator for the map accuracy.
+
To properly measure the map accuracy, a probability sample of the prediction area is required. This might be a random sample of the entire area that is used to describe the fit between the prediction (i.e. the map) and the reference.
+However, in numerous scientific publications, this essential step is often omitted, and model performance alone is presented as the sole indicator for map accuracy. The following section outlines the risks associated with this practice.
Validation metrics summarize the fit between predictions and reference. For continuous variables (e.g. snow depth), Root Mean Square Error or Coefficient of Determination are commonly used validation metrics. For categorical variables (e.g. land cover), Accuracy or F1 score are frequently used summary statistics. For binary classifications, the area under the ROC curve may be used. However, there are many more validation metrics expressing the fit between prediction and reference by focusing on different aspects.
+
+
+
+
Validation strategies in the absence of a probability sample#
+
When reference data are randomly distributed across the prediction area, validation metrics can be computed by comparing predictions and reference through a randomly selected subset of the entire reference dataset used as test data. Alternatively, in cross-validation, the training data may be randomly partitioned into multiple folds. However, the availability of design-based samples is infrequent, particularly in large-scale mapping endeavors like global applications.
+Typically, reference data are sourced from extensive databases, such as soil profiles or vegetation surveys, resulting in high clustering within areas that have been extensively studied or are easily accessible. Conversely, certain areas may entirely lack reference data, as illustrated in the accompanying figure.
+
+
+
Figure 1: Comparison between 1000 randomly sampled reference data (left) and a highly clustered sample of the same size (right) that is typical for many environmental data sets. Reference: https://doi.org/10.1038/s41467-022-29838-9
+
+
When such data are randomly split into training and test sets or cross-validation folds, a significant issue arises: the lack of independence between training and test data. This stems from the fact that both sets originate from the same geographic areas, whereas the trained model is deployed to make predictions for much larger areas without available reference data. Ploton et al., 2020, illustrate the consequences: overly optimistic validation statistics that deviate from the actual quality of the map.
+
To address this challenge, various spatial data splitting methods have been proposed. These methods involve splitting reference data based on spatial units, spatial blocks, or by considering spatial autocorrelation, all with the aim of ensuring independence between training and test data (e.g. Brenning 2012, Roberts et al., 2017, Valavi et al., 2019) or representativeness for the prediction task (Mila 2022, Linnenbrink 2023).
Employing validation strategies tailored for spatial data enables us to offer the most accurate estimates of map accuracy, albeit with certain limitations. Reflecting on the reference data illustrated in Figure 1, large areas lack coverage from any reference data. While it is technically feasible to generate predictions for these areas, the question arises: is this a reasonable approach?
+
Assuming that new geographical spaces often goes along with new environmental conditions, it becomes likely that our models may not be applicable to these environments due to non-applicable relationships. For instance, consider reference data for vegetation traits sampled in low elevations; it raises questions about the model’s applicability to high elevations where the traits might be influenced by different factors. This challenge is particularly pronounced when employing machine learning models, as their extrapolation abilities are often limited. When making predictions, the model is compelled to extend its predictions into unknown areas, making predictions for regions beyond the trained data range highly uncertain.
+This, however, is not reflected by the validation statistics that were calculated based on the reference data, hence knowledge on the performance in the data-poor regions is not included. As a result, the statistics fail to reflect the accuracy of the model in these areas, and predictions for regions outside the original data range should be approached with caution due to their inherent uncertainty.
+
It is therefore important to limit predictions to the area where the model was trained and validated for. Meyer and Pebesma 2021 provide one suggestion to derive the “area of applicability” of prediction models that is based on distances to reference data in the predictor space. Other suggestion limit predictions to the geographic proximity of reference data (Sabatini et al., 2022).
As outlined above, the step of accuracy assessment involves considerable considerations on the data used for evaluation and requires awareness on the area these statistics are considered valid for. This challenge becomes particularly crucial when reference data fail to represent a comprehensive sample of the entire prediction area, a common scenario in many geoscience applications - to avoid overly optimistic performance estimates and hence false conclusions on the map accuracy. The validation procedure hence needs to be carefully communicated alongside the predictions. The resulting EO product (i.e. the prediction map) should be limited to the area for which the model was enabled to learn about relationships and for which performance estimates can be reliably provided for. This can be done by either masking the map or by providing an additional quality layer.
+
+
+
Video content in cooperation with Hannah Meyer (University of Münster).
+“Validation isn’t optional. It’s a must.”
What are common problems in creating and validating global maps?
+
[[x]] The spatial distribution of reference data: There are usually areas in the world where reference data is clustered and areas where there is hardly any data available.
+[[ ]] None: We have cloud computing that can scale to produce global maps and machine learning models can automatically account for data sparse regions.
+[[x]] The availablility of reference data: Some biophysical indicators are not measured frequently in space and time in the field (e.g. leaf area index, snow water equivalent)
+
+
+
What is the Area of Applicability?
+
[( )] It's the topic the map covers (e.g. vegetation cover)
+[( )] It's the extent of the map.
+[(x)] It's the area of the map where the values are representable.
+
Which is the station where the mapped snow cover has the lowest accuracy? Answer in the exercise: 33_validation.ipynb section ‘validate the SCA results with the snow station measurements’
+
[( )] Rifiano Beobachter
+[( )] Saint Leonardo in Passiria Osservatore
+[(x)] Plata Osservatore
+
+
+
When is the date with the maximum runoff/discharge? Answer in the exercise: 33_validation.ipynb section ‘compare to discharge data’
How is the relation between snow cover and runoff/discharge? Answer in the exercise: 33_validation.ipynb section ‘compare to discharge data’
+
[( )] When the snow cover is high, also the runoff is high.
+[(x)] Snow melt is followed by increased runoff.
+[( )] Snow melt is followed by reduced runoff.
+
Meyer, H., Pebesma, E. Machine learning-based global maps of ecological variables and the challenge of assessing them. Nat Commun 13, 2208 (2022). https://doi.org/10.1038/s41467-022-29838-9
+
Meyer, H., & Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12, 1620–1633. https://doi.org/10.1111/2041-210X.13650
We have reached the last chapter of the course. You know about data cubes, cloud platforms and open science. Now it’s time to prove it! We will apply everything we have learned so far and complete our own EO workflow on a cloud platform adhering to the open science principles.
+We have carried out a full EO workflow to produce snow cover information in an alpine catchment. To make our results impactful we need to make them openly available to other researchers and the general public. Therefore we are going to learn how to share our data set (and code) properly - following the FAIR principles. We have learned about the concepts of open science in lecture 1.3 open science. Now we are going to apply them! We are going to create a snow cover area map of the alps together with all the participants of the course. Everyone adds their contribution to a shared map. With every participant another small patch of the alps gets mapped! The map is openly available so that everybody can track our progress, the data is openly available and you can point to the patch you have provided!
+
+
+
Video content in cooperation with Leandro Parente (OpenGeoHub).
+“Connect - Create - Share - Repeat”
+Links to OpenGeoHub’s open science projects mentioned in the video:
Time to start your own open science journey. Produce a snow cover area map for a region that hasn’t been mapped yet. FAIRify your results and make them publicly available!
Science is much more impactful once it’s shared. Therefore, we are going to learn how to
+open up our scientific output from a cloud platform, so that is openly available - and
+has the chance to make the impact it should.
+
+
Reuse the workflow we have used before for creating the snow covered area
+
Select AOI,
+
Recreate process graph,
+
Download results for one time-step
+
+
A Snow Cover Area map in the COG format
+
A STAC metadata item that is provided with the result from openEO at CDSE
+
+
+
Adapt the STAC item
+
Upload the results and make them available openly via a STAC browser and web map
Start by selecting a center point of the area you would like to analyse from the map shown below. The starting extent is the full alps. Zoom in to an area and choose a region that has not been mapped yet. Make sure not to overlap too much with already mapped areas by having a look at the STAC Collection. It’s a community mapping project :)
+Create a 1 km bounding box around it. This will be the area you are calculating the snow covered area for.
+
Attention:
+Execute the cell below to show the map. Zoom to a location you want to analyze. Use the location symbol to select a point. A marker appears on the map. This is the center of your area of interest
Attention:
+Now this cell will get the coordinates of the marker you have placed. This will create a 1 km bounding box around the chosen location. And visualize it in the map above. The marker moves to the center when you zoom in
+
+
+
feat=m.draw_features
+geom=feat[0]['geometry']['coordinates']
+
+# set distance of 1 km around bbox
+distance_km=1
+
+# Create a bounding box around the point
+bbox=create_bounding_box(geom[0],geom[1],distance_km)
+visualize_bbox(m,bbox)
+
+
+
+
+
Now we’ll select the time frame. We’ll start with the winter months of 2023.
+
+
+
temporal_extent=["2023-02-01","2023-06-01"]
+
+
+
+
+
+
+
Reuse the process graph of the snow covered area data cube#
+
We’ve saved the python code that we had used to create the snow cover area data cube into a python function calculate_sca(). It’s stored in cubes_utilities.py. It creates a 4 dimensional data cube with the dimensions: x, y, time, bands.
+As parameters we have exposed the bounding box and temporal extent. We will update them with the choices we have made above.
We want to calculate the SCA for the winter period of a given year. Therefore, we need to reduce the values along the time dimension. We’ll use the process reduce_dimension() with a median() to accomplish this. We are directly continuing to build on our process graph that we have loaded above.
We register the job as a batch job on the backend and start the processing. Depending on the traffic on the backend, this usually takes between 1 to 5 minutes.
Add statistics to the dataset via gdal, such as a summary of the values within the dataset and also some metadata, i.e. the legend (TIFFTAGS). And we reduce the datatype to the lowest possible datatype supported by COG uint8, since only have three values to represent (0, 1, 2). If you’re interested you can check what happened via !gdalinfo33_results/openEO_uint8.tif
Now, we check if the nodata value can be determined directly from the COG metadata
+
+
+
snowmap.rio.nodata
+
+
+
+
+
2
+
+
+
+
+
Now, we make a plot of the snowmap keeping in mind that 0=nosnow, 1=snow, and 2=clouds(nodatavalue)
+
+
+
snowmap.plot(levels=[0,1,2])
+plt.title("Spatial distribution of snow, no snow and cloudy pixels")
+plt.ylabel("Latitude")
+plt.xlabel("Longitude")
+plt.tight_layout()
+
+
+
+
+
+
+
+
Let’s have a look at the histogram to understand the distribution of the values in our map
+
+
+
data=snowmap.values.flatten()
+snowmap.plot.hist(xticks=[0,1,2],weights=np.ones(len(data))/len(data))
+
+plt.gca().yaxis.set_major_formatter(PercentFormatter(1))
+plt.title("Distribution of snow, no snow and cloud pixels")
+plt.show()
+
Extract bbox information and temporal extent from the STAC collection that was delivered with the result from OpenEO. We are reusing it to create our STAC item. We have prepared these function for you extract_metadata_geometry and extract_metadata_time
Since we calculated the statistics and renamed the file, we have to add this new file name to the STAC item.
+
+
+
filename="openEO_uint8.tif"
+
+
+
+
+
Let’s create the actual STAC item describing your data! As talked about in previous lessons, STAC item has various required fields which need to be present and filled correctly. For the field ID we assign the fixed name snowcover and the initials of your name. That will be visible on the STAC browser once you have submitted the result!
And now by executing the cell below, update of the STAC browser will start. By this, you are uploading your results to the openly available STAC browser. This might take some minutes.
You can now browse your results together with all the other submissions at the publicly available STAC Catalog! You can check your snow cover map, that you are correctly listed as the author and that your contribution has the correct name. The license on the STAC Collection “Cubes and Clouds: Snow Cover” is CC-BY-4.0. The STAC Collection also has it’s own DOI.
+
Congratulations you have just contributed to a community mapping project that is completely open source, open data and FAIR! Make sure to show it also to your friends, colleagues or potential employers :)
If you would like to redo your submission, you can still update your files in submissions folder and once ready, run again the code in the cell above.
+
Attention: If you have previously opened the STAC browser, your old submission will be cached and not directly displayed. To circumvent this, open a private window from your browser.
/tmp/ipykernel_72/1928884253.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.
+
+ m = folium.Map(location=[catchment_outline.centroid.y, catchment_outline.centroid.x])
+/tmp/ipykernel_72/1928884253.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.
+
+ m = folium.Map(location=[catchment_outline.centroid.y, catchment_outline.centroid.x])
+
+
+
Make this Notebook Trusted to load map: File -> Trust Notebook
As a first step we will create a RGB data cube from Sentinel-2, including a cloud cover map (CLM). This can be useful to visualize the Sentinel-2 images for some specific date. The spatial resolution set for this cube will be 0.00018 degree, which correspond roughly to the native 10 meter resolution of the Sentinel-2 visible bands (B02: blue, B03: green, B04: red)
/home/conda/master-asi-conae/315434deb1b1e05eacacc41e1b0529b1837100f46402a32041d77ba54ae6c5f3-20230213-125825-013239-74-edc-2022.10-14/lib/python3.9/site-packages/xcube_sh/config.py:248: FutureWarning: Units 'M', 'Y' and 'y' do not represent unambiguous timedelta values and will be removed in a future version.
+ time_tolerance = pd.to_timedelta(time_tolerance)
+
After cloud masking, snow map can be obtained by thresholding the Normalized Difference Snow Index (NDSI) which is computed as:
+
+\[ NDSI = \frac {GREEN - SWIR} {GREEN +SWIR} \]
+
We will therefore create a Sentinel-2 data cube with bands B03 (green), B11 (SWIR) and the cloud mask (CLM). To speed up the computation of the SCA we will set the resolution equal to 0.0018 degrees, which correspond roughly to 100 m. This resolution is generally enough to get reliable estimate of snow cover area over a catchment.
/home/conda/master-asi-conae/315434deb1b1e05eacacc41e1b0529b1837100f46402a32041d77ba54ae6c5f3-20230213-125825-013239-74-edc-2022.10-14/lib/python3.9/site-packages/xcube_sh/config.py:248: FutureWarning: Units 'M', 'Y' and 'y' do not represent unambiguous timedelta values and will be removed in a future version.
+ time_tolerance = pd.to_timedelta(time_tolerance)
+
In alpine catchment snow represent a major contribution to the runoff during the melting season. As a qualitative analysis we will plot the runoff time series for the selected catchment toghether with the SCA time series we derived from Sentinel-2. As soon as the SCA decreases due to melting, the snow melt contribution implies an increase of the runoff.
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Load the runoff time series stored in the csv file:
Read the shapefile on which to compute the snow cover map and plot it
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+
# Read the file with geopandas and convert it to EPSG:4326
+aoi_df=gpd.read_file('ADO_DSC_ITH1_0025.geojson')
+aoi_df=aoi_df.to_crs(4326)
+
+# Create a basemap with folium
+m=folium.Map(
+ location=[aoi_df.to_crs(4326).iloc[0].geometry.centroid.y,aoi_df.to_crs(4326).iloc[0].geometry.centroid.x],
+ zoom_start=11)
+
+# Add the polygon to the map
+geo_j=folium.GeoJson(data=aoi_df.to_json())
+geo_j.add_to(m)
+
+# Show the map
+m
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+
Make this Notebook Trusted to load map: File -> Trust Notebook
/home/conda/master-asi-conae/315434deb1b1e05eacacc41e1b0529b1837100f46402a32041d77ba54ae6c5f3-20230213-125825-013239-74-edc-2022.10-14/lib/python3.9/site-packages/xcube_sh/config.py:248: FutureWarning: Units 'M', 'Y' and 'y' do not represent unambiguous timedelta values and will be removed in a future version.
+ time_tolerance = pd.to_timedelta(time_tolerance)
+
/opt/conda/envs/edc-default-2022.10-14/lib/python3.9/site-packages/xcube_sh/config.py:248: FutureWarning: Units 'M', 'Y' and 'y' do not represent unambiguous timedelta values and will be removed in a future version.
+ time_tolerance = pd.to_timedelta(time_tolerance)
+
Markdown File: The main markdown file compiling all the information that will be displayed in the course.
+
Assets: A folder called assests. It holds all media used in the lecture, e.g. images, videos, interactive graphics, code snippets, etc.
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Exercises: If there are coding exercises in a lesson, the according notebooks and data are stored here.
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diff --git a/_sources/0_introduction/0_introduction.md b/_sources/0_introduction/0_introduction.md
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+# Introduction
+This short sections will familiarize you with some free external services that are going to be used throughout the MOOC.
+- How to sign up for the Copernicus Data Space Ecosystem (this is where the cloud processing will happen)
+- First steps in the Coding Environment
+
+## Sign up for EO College
+In case you haven't already done it. Sign up for the MOOC **'Cubes & Clouds - Cloud Native Open Data Sciences for Earth Observation'**
+- https://eo-college.org/courses/cubes-and-clouds
+
+## Sign up for Copernicus Data Space Ecosystem
+We are going to use the cloud processing platform **Copernicus Data Space Ecosystem** to carry out our exercises. It's a cloud platform hosted by the European Commission, ESA and Copernicus.The processing will happen there and you will get 1000 free credits. That's enough to complete the course.
+- https://documentation.dataspace.copernicus.eu/Registration.html
+
+## Your first steps in the Coding Environment
+Here is a guide how to find your way around in the Coding Environment JupyterHub. You will see a button that forwards you there whenever there is a hands-on exercise to do.
+
+[](https://www.youtube.com/watch?v=GBXd4aQLMb0)
+
+
+Register on EOXHub
+- https://cubes-and-clouds.hub.eox.at/
+
+After registering on EOX you are now ready to start your first exercise!
+
+[Exercise 0 Introduction](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/0_introduction/exercises/0_login.ipynb)
+
diff --git a/_sources/0_introduction/exercises/0_login.ipynb b/_sources/0_introduction/exercises/0_login.ipynb
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+++ b/_sources/0_introduction/exercises/0_login.ipynb
@@ -0,0 +1,174 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0c6aed2f-1493-4636-a124-03c81b28bc52",
+ "metadata": {},
+ "source": [
+ "# Login to openEO\n",
+ "In order to access a cloud platfrom you need to login. In this notebook we will login to the Copernicus Data Space Ecosystem for using openEO. \n",
+ "- Import the libraries we need to interact with the cloud platform\n",
+ "- Make sure we have the login credentials \n",
+ "- Connect to the cloud platform\n",
+ "- Login to the cloud platform\n",
+ "- Check that the login worked"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d3400da4-b7c0-43be-abc9-e3100bf8c9f4",
+ "metadata": {},
+ "source": [
+ "## Libraries\n",
+ "We will import the openeo python client library. It is preinstalled in the jupyter workspace on EOX. \n",
+ "- `openeo`: The openeo python client has all the functions available that we need to interact with the cloud platform using the openEO API.\n",
+ "\n",
+ "Here is more information on the openeo python client: \n",
+ "- https://open-eo.github.io/openeo-python-client/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "1abf270c-d73e-4109-854c-3cf36a1b36d7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import openeo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4acd87c3-537f-4f9e-9b2a-4378bb9c8abb",
+ "metadata": {},
+ "source": [
+ "## Connect to the cloud platform\n",
+ "In a first step we connect to the cloud platform. We can only see information and use functionality that is available to everybody. \n",
+ "We can see for example which collections and processes are available, but we cannot process data. We will explore the platforms capabilities in an extra exercise in more depth later.\n",
+ "\n",
+ "Now let's just connect to the platform..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "86afd551-857e-4129-a2ee-39a933255f34",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "conn = openeo.connect('https://openeo.dataspace.copernicus.eu/')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "312b377d",
+ "metadata": {},
+ "source": [
+ "... and check if the connection has worked. You should see that you are connected, but not logged in."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1107f9f8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "conn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42084427-fce5-429b-8707-d950c9e80fa7",
+ "metadata": {},
+ "source": [
+ "## Login to the cloud platform\n",
+ "After we have connected to the platform and want more functionality, we need to login. This means we authenticate ourselfs to prove we are an registered user. \n",
+ "After access is granted we can also process data. Every computation comes at a cost, this is why every user has an amount of credits (which usually have to be payed) for computing.\n",
+ "Everytime you are going to use the cloud platform for processing you will have to login at the beginning of your workflow. We are going to learn how to create a workflow in a seperat exercise later on.\n",
+ "\n",
+ "Now let's just log in... *(this only works if you haver registered to CDSE as described in the lesson Introduction)*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "de26fd5c-71da-4d28-b895-f5108a97a1d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "conn.authenticate_oidc()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "92fb1bf2",
+ "metadata": {},
+ "source": [
+ "... and check if the login has worked..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "050b91c0-60d8-464e-9fd3-0ab62638aafd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "conn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6c5858ed",
+ "metadata": {},
+ "source": [
+ "... and let's check our user information, which is possible since we have authenticated now."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "592cfb2a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "conn.describe_account()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7c6de05a",
+ "metadata": {},
+ "source": [
+ "## Return to EOCOllege\n",
+ "This is all. We have verified that we can connect and login to the cloud platform. We will do this again later on when we'll start with some hands on exercises. \n",
+ "\n",
+ "For now let's return to EOCollege to get started with the lessons!\n",
+ "\n",
+ "[Return to Cubes and Clouds on EO College](https://eo-college.org/courses/cubes-and-clouds)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "users-edc-2023.03-02",
+ "language": "python",
+ "name": "conda-env-users-edc-2023.03-02-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/1.1_intro_platform/1.1_intro_platform.md b/_sources/1.1_intro_platform/1.1_intro_platform.md
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@@ -0,0 +1,169 @@
+# Earth Observation cloud platforms
+
+## Learning Objectives
+
+- Understand why using a platform is useful
+- Differentiate platform offerings
+- Get to know the components and building blocks of a platform
+
+## Why do we need EO cloud platforms?
+
+[](https://www.youtube.com/watch?v=S6wfn1mR3qk)
+> Video content in cooperation with [Jeroen Dries](https://remotesensing.vito.be/team/jeroen-dries) (VITO).
+> Numbers based on [ESA Annual Sentinel Data Access Report 2022](https://scihub.copernicus.eu/twiki/pub/SciHubWebPortal/AnnualReport2022/COPE-SERCO-RP-23-1493_SentinelDataAccessAnnual_Report_2022.pdf)
+
+Traditional approaches for the analysis of Earth Observation (EO) data typically involve several steps, including data discovery, data download, data pre-processing, and data analysis. Especially when working with multiple datasets, handling data discovery, download, and access is a tremendous task, where users need to navigate through different interfaces, adhere to varying access requirements, and manage the heterogeneity of data formats. This approach is often time-consuming and requires significant effort to aggregate and harmonize datasets from different providers for comprehensive analysis.
+
+
+
+> Figure: EO research withouth cloud facilities.
+
+### EO data volume and the limits of your computer
+
+In the field of Earth Observation, satellite missions like Sentinel-2 provide vast amounts of data that play a crucial role in various applications, including environmental monitoring, land cover mapping, and climate analysis. Understanding the volume of data involved in an analysis is critical for efficient data processing. EO datasets can span terabytes and petabytes, making it impractical to store, manage, and process them entirely on a local computer.
+
+The increasing availability of vast amounts of EO data from multiple satellites presents challenges in terms of the time required for data download and pre-processing on individual computers or infrastructures. Within the Copernicus program of the European Union, around 64 million products have been published, which sums up to more than 25 Petabyte of data volume. In the following slider we have collected some statistics about the amounts of EO data from the Sentinel satellites.
+
+
+
+> Figure: EO data volume and the limits of your computer.
+
+The following interactive exercise assists in estimating the data volume associated with Sentinel-2 data. This calculator allows users to gain insights into the data volumes involved in specific regions and time ranges, further emphasizing the relevance of using EO platforms for scientific analyses. Not convinced of clouds yet? Try the volume calculator below to asses how much space you have to free up on your hard drive for your next project.
+
+
+
+### How can we handle such volumes of data?
+
+Cloud infrastructure and platforms have emerged as viable alternatives to the traditional approach of data analyses as described in Figure "EO research without cloud facilities". These solutions combine data storage and compute resources, enabling users to conduct their data analysis in close proximity to the data itself. By leveraging cloud-based infrastructures, researchers and analysts can optimize their workflow by minimizing the time-consuming steps of data transfer and pre-processing, thereby allowing them to focus more efficiently on data analysis tasks.
+
+By utilizing cloud-based resources, users can harness the scalability and flexibility of these platforms to handle the extensive datasets generated by EO missions. Cloud-based EO platforms represent a paradigm shift in EO data analysis, offering a comprehensive ecosystem that seamlessly integrates storage, processing, analysis tools, collaboration, and visualization. These platforms empower users to overcome the challenges posed by large-scale EO data and accelerate scientific advancements in various fields, including environmental monitoring, climate studies, natural resource management, and disaster response.
+
+## Types of platforms
+
+Cloud-based EO infrastructures and platforms have emerged to meet the growing demand for efficient data processing, analysis, and visualization close to the data. We can distinguish between infrastructure providers and platform providers.
+
+### Infrastructure providers
+
+EO-based infrastructure providers focus on offering the underlying infrastructure necessary for processing, storage, and dissemination of EO data. They provide the computing resources, storage capacity, and networking capabilities required to handle large-scale EO data processing and analysis. These providers often build and maintain data centers and server clusters, ensuring reliable and scalable infrastructure for EO applications.
+
+In comparison to the traditional approach (Figure "EO research without cloud facilities"), this approach allow users to use computing resources close to the EO data (see Figure "EO cloud providers"). Users do not need to download and manage EO data on their own, they can simply use what is already available on the infrastructure. However, there are no EO-specific services for data discovery, access, visualization, and analysis.
+
+
+
+> Figure: EO cloud providers.
+
+**Examples of EO-based infrastructure providers**
+
+1. [Amazon Web Services (AWS)](https://aws.amazon.com/): AWS offers a wide range of cloud services, including storage (Amazon S3) and computing (Amazon EC2), which can be leveraged for EO data processing and storage. Various open data are available on AWS ().
+2. [Google Cloud Platform (GCP)](https://cloud.google.com/): GCP provides infrastructure services like Google Cloud Storage and Google Compute Engine, which can be utilized for EO data management and analysis. Various open data are available on GCP ().
+3. [Microsoft Azure](https://azure.microsoft.com): Azure offers cloud-based services such as Azure Storage, Azure Virtual Machines, and Azure Machine Learning, enabling EO applications and workflows.
+4. [Open Telekom Cloud](https://open-telekom-cloud.com): Open Telekom Cloud is a cloud platform offered by Deutsche Telekom. It provides scalable infrastructure resources, including computing, storage, and networking capabilities, suitable for processing and storing large volumes of EO data.
+5. [Cloudferro](https://cloudferro.com): Cloudferro is a cloud infrastructure provider specializing in geospatial data processing and analysis. They offer scalable and secure cloud resources optimized for EO applications. Cloudferro provides high-performance computing, storage, and networking services tailored for EO data processing workflows. Various open data are available on Cloudferro ().
+
+### Platform providers
+
+Platform providers focus on delivering comprehensive EO platforms that combine infrastructure, tools, and services into a cohesive environment. These platforms typically offer a suite of integrated capabilities, including data storage, processing, analysis, visualization, and collaboration tools. They provide a user-friendly interface and simplify the EO data lifecycle, enabling users to access, process, and analyze EO data without managing the underlying infrastructure.
+
+On top of providing the infrastructure which allows users to do the computations close to the EO data , making available a platform additionally enables the use of specific Application Programming Interfaces (APIs) for the discovery, access, visualization, exploitation, and analysis of EO data. EO platforms are often made available on infrastructure providers to benefit from the EO data storage. Users of a platform can now use harmonized interfaces for all data, which is available on the platform.
+
+
+
+> Figure: EO platform providers.
+
+**Examples of cloud-based EO platform providers**
+
+1. [Google Earth Engine](https://earthengine.google.org) is a platform specifically designed for EO data analysis. It provides access to a vast amount of satellite imagery and geospatial datasets, along with powerful processing capabilities and built-in algorithms.
+2. [Sinergise Sentinel-Hub](https://www.sentinel-hub.com) is a platform focused on accessing and processing satellite data. It provides APIs and easy-to-use tools for accessing, processing, and visualizing EO data.
+3. [Microsoft Planetary Computer](https://planetarycomputer.microsoft.com) is a platform that combines geospatial data and AI capabilities for Earth observation. It provides access to various global datasets, including satellite imagery, climate data, and environmental data. The platform aims to facilitate large-scale data analysis and support sustainable development and conservation efforts.
+4. [Euro Data Cube](https://eurodatacube.com) is a platform on top of various cloud infrastructures to provide an interactive development environment with a standardized access to various EO data. It provides a JupyterLab environment for data exploration and analysis, as well as capabilities to run processing jobs.
+5. [OpenEO Platform](https://openeo.cloud) is a platform based on OpenEO, which aims to standardize and simplify the access and processing of EO data. It provides a unified API and common data model, enabling interoperability across multiple EO data providers and processing backends. The platform allows users to run EO workflows on various cloud-based infrastructure providers.
+6. [Pangeo](https://pangeo.io) is a community platform for big data geoscience built on the Pangeo ecosystem. It aims to foster collaboration among researchers, engineers, research software engineers, and innovators within the open-source scientific Python ecosystem, focusing on Ocean, Atmosphere, Land, and Climate science. There is a strong focus on portability and interoperability, enabling deployments of the Pangeo platform on various infrastructure (laptops, cloud providers, etc.) and providing APIs that allow users to prototype on their laptops and easily scale to the cloud or High-Performance Computers with minimal changes to their code. The current deployment of the Pangeo platform in Europe, [Pangeo@EOSC](https://pangeo-eosc.vm.fedcloud.eu/) is hosted on the [European Open Science Cloud](https://eosc.eu) (EOSC), providing a scalable and collaborative environment for big data analysis and research for all European researchers and their collaborators.
+
+Different EO platform providers may offer end-users access to specific tools and packages that can be highly beneficial for particular workflows. However, users should carefully evaluate whether the convenience of these tools justifies the potential trade-off of being locked into a particular platform. In some cases, opting for platforms that prioritize portability and openness, like OpenEO or Pangeo, might be more advantageous, especially for those who value flexibility and long-term interoperability across various environments and infrastructures.
+
+### Summary
+
+In summary, EO-based infrastructure providers primarily focus on providing the underlying infrastructure and resources, while platform providers offer integrated environments with a wide range of tools and services to support EO data processing, analysis, and visualization. These two types of providers complement each other in the EO ecosystem, enabling users to access and leverage EO data effectively.
+
+## Components of platforms
+
+Cloud-based EO platforms have transformed the way researchers and scientists analyze and utilize EO data. These platforms often follow a three-layered design (often named "tiers") comprising infrastructure, services, and exploitation interfaces. An example architecture based on this approach is the "Earth Observation Exploitation Platform Common Architecture" (EOEPCA) of ESA (). Leveraging the power of cloud computing, EO platforms provide a comprehensive ecosystem that seamlessly integrates storage, processing, analysis tools, collaboration, visualization, and data exploitation capabilities.
+
+The following overview will explore each layer of the three-layered design and provide examples to illustrate the functionalities and benefits of cloud-based EO platforms in real-world applications. They showcase the diverse range of tools, services, and interfaces (also named "building blocks") available to store, process, analyze, collaborate, visualize, and exploit EO data effectively within the cloud environment.
+
+
+
+### Infrastructure & Resource Tier
+>
+> "The Resource Tier represents the hosting infrastructure and provides the EO data, storage and compute upon which the exploitation platform is deployed." (Source: [EOEPCA Master System Design](https://eoepca.github.io/master-system-design/current/#_architecture_layers))
+
+1. **Data Storage:** The data storage component may include distributed file systems like distributed parallel file systems (e.g., GPFS, Hadoop) or object storage services (e.g., Amazon S3, Google Cloud Storage) to securely store and manage EO datasets.
+
+2. **Computing Resources:** The computing component can provide virtual machines (e.g., Amazon EC2, Google Compute Engine, OpenStack), container environments (e.g., Docker-Engine, Kubernetes) or batch-computing systems (e.g., High Performance Computing) for executing data processing and analysis tasks on EO datasets.
+
+### Platform Tier
+>
+> "The Platform Tier represents the Exploitation Platform and the services it offers to end-users." (Source: [EOEPCA Master System Design](https://eoepca.github.io/master-system-design/current/#_architecture_layers))
+
+The services of the platform tier can be grouped into data-related and processing-related services. The processing tools and services often rely on the data services to get discover and get access to data available on the platform.
+
+1. **Data Services**
+ - **Data Catalog:** Data available on the platform needs to be described with metadata to be findable by users. Often processing and analysis services, such as Open Data Cube or OpenEO, make use of the data catalog to ease the use of EO data. These services enable users to annotate, search, and discover EO datasets based on various metadata parameters.
+ - **Data Access Service:** This enables users to retrieve and access EO datasets. This may involve APIs, protocols, or data transfer mechanisms like Open Geospatial Consortium (OGC) Web Services or HTTP services for efficient and secure data access.
+ - **Data Visualization Service:** The visualization component provides standardized web services for the visualization of raster and vector data available on the platform. User interfaces like QGIS or web mapping tools can be used together with those services.
+
+2. **Data Processing and Analysis Tools:** This service component may include widely used processing tools like [GDAL](https://gdal.org) (Geospatial Data Abstraction Library), remote sensing software like SNAP (Sentinel Application Platform), data cube related tools (e.g., [xarray](https://xarray.dev/) & [Dask](https://www.dask.org/)) and APIs (e.g., [OpenEO API](https://openeo.org)) for performing advanced analysis on EO data.
+
+### Exploitation Tier
+>
+>"The Exploitation Tier represents the end-users who exploit the services of the platform to perform analysis, or using high-level applications built-in on top of the platform’s services." (Source: [EOEPCA Master System Design](https://eoepca.github.io/master-system-design/current/#_architecture_layers))
+
+1. **User Interfaces:** The exploitation interface component may include web-based interfaces like web portals (e.g., [EO Browser from Sinergise](https://apps.sentinel-hub.com/eo-browser/)), dashboards (e.g., [Earth Observation Dashboard from NASA, ESA, JAXA](https://eodashboard.org/) or web development environments (e.g., [JupyterLab](https://jupyter.org/)). All of them provide interactive interfaces for users to explore and analyze EO data through a user-friendly interface.
+
+### Exercise: Build a platform
+
+Now it is time for you: Please Drag and drop the building blocks of a platform into a correct diagram.
+
+
+
+
+## Quiz
+
+What types of "layers" or "tiers" are there in a platform architecture?
+
+ [[x]] Infrastructure & Resource Tier
+ [[ ]] Software Tier
+ [[x]] Platform Tier
+ [[ ]] Data Cube Tier
+ [[x]] Exploitation Tier
+
+What does an infrastructure provider offer?
+
+ [[x]] Virtual Machines
+ [[ ]] Data discovery
+ [[ ]] Data cubes
+ [[ ]] Data visualization
+ [[x]] Data storage
+
+What kind of provider is the Euro Data Cube?
+
+ [[ ]] Infrastructure provider
+ [[x]] Platform provider
+
+When should you consider to use an EO cloud platform?
+
+ [[x]] I have limited internet bandwidth for data downloading
+ [[ ]] I have all my data locally on my own servers with lots of computing resources
+ [[x]] I want to collaborate with other and external users
+ [[x]] I want to easily make use of processing services
+ [[x]] I don't want to care about system administration and operations
+ [[ ]] I do not often have access to internet
+
+## Further reading
+
+- Earth Observation Cloud Platform Concept Development Study Report:
+- Big Earth data: disruptive changes in Earth observation data management and analysis?
+- Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges:
+- An Overview of Platforms for Big Earth Observation Data Management and Analysis:
+- The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities:
+- ESA Earth Observation Exploitation Platform Common Architecture (EOEPCA):
diff --git a/_sources/1.2_data_cube/1.2_data_cube.md b/_sources/1.2_data_cube/1.2_data_cube.md
new file mode 100644
index 0000000..cca22e7
--- /dev/null
+++ b/_sources/1.2_data_cube/1.2_data_cube.md
@@ -0,0 +1,174 @@
+# What is a data cube?
+
+## Learning objectives
+
+- Understand what data cubes are and why they are needed
+- Learn what dimensions are
+- Understand the role of data cubes in EO cloud platforms
+
+## Data Cubes in General
+
+When you think about data, most likely tables come to your mind, with features organized in rows and columns. Data cubes overcome these constraints: they are a data structure that allows to represent data in more than only two dimensions. Generally a data cube is a multi-dimensional data structure. Even though it's called a cube, it is n-dimensional. A 1-d data cube is an array. A 2-d data cube is a table. A 3-d data cube is a cube. A 4-d data cube is hard to visualize. A feature within a data cube is explained by multiple dimensions and has a certain value. An example would be: A company has sold multiple products (Shirts, Shoes, Pants), in different countries (Sweden, USA, Tunisia), in different years (2021, 2022, 2023).
+
+- Dimensions: Products, Locations, Years
+- Labels of the Dimensions:
+ - Products: Shrits, Shoes, Pants
+ - Countries: Sweden, USA, Tunisia
+ - Years: 2021, 2022, 2023
+- Feature: Revenue
+- Value: $
+
+
+
+This concept can be applied to many fields such as economics, medicine, biology, and also very well to EO data!
+
+## Data Cubes in EO
+
+The concept of representing multidimensional data as data cubes fits ideally to the challenges of representing satellite data that is usually dealing with multiple dimensions such as: lat, lon, time, bands, etc. In the video below it becomes very clear what the strengths of data cubes in EO are.
+
+
+
+### An example data cube
+
+Data can be represented as datacubes in EO, which are multi-dimensional arrays with additional information about their dimensionality. Datacubes can provide a nice and tidy interface for spatiotemporal data as well as for the operations you may want to execute on them. As they are arrays, it might be easiest to look at raster data as an example, even though datacubes can hold vector data as well. Our example data however consists of a 6x7 raster with 4 bands [`blue`, `green`, `red`, `near-infrared`] and 3 timesteps [`2020-10-01`, `2020-10-13`, `2020-10-25`], displayed here in an orderly, timeseries-like manner:
+
+
+> Figure: An examplary raster datacube with 4 dimensions: x, y, bands and time. Reference: [openeo.org (2022). What are Data Cubes.](https://openeo.org/documentation/1.0/datacubes.html#what-are-datacubes)
+
+It is important to understand that datacubes are designed to make things easier for us, and are not literally a cube, meaning that the above plot is just as good a representation as any other. That is why we can switch the dimensions around and display them in whatever way we want, including the view below:
+
+
+> Figure: Raster datacube flat representation: The 12 imagery tiles are now laid out flat as a 4 by 3 grid (bands by timesteps). All dimension labels are depicted (The timestamps, the band names and the x, y coordinates). Reference: [openeo.org (2022). What are Data Cubes.](https://openeo.org/documentation/1.0/datacubes.html#what-are-datacubes)
+
+### Dimensions
+
+A dimension refers to a certain axis of a datacube. This includes all variables (e.g. bands), which are represented as dimensions. Our exemplary raster datacube has the spatial dimensions `x` and `y`, and the temporal dimension `t`. Furthermore, it has a `bands` dimension, extending into the realm of _what kind of information_ is contained in the cube.
+
+The following properties are usually available for dimensions:
+
+- name
+- type (potential types include: spatial (raster or vector data), temporal and other data such as bands)
+- axis (for spatial dimensions) / number
+- labels (usually exposed through textual or numerical representations, in the metadata as nominal values and/or extents)
+- reference system / projection
+- resolution / step size
+- unit (either explicitly specified or implicitly given by the reference system)
+- additional information specific to the dimension type (e.g. the geometry types for a dimension containing geometries)
+
+Here is an overview of the dimensions contained in our example raster datacube above:
+
+| # | name | type | labels | resolution | reference system |
+| - | ------- | -------- | --------------------------------------------------------------------------- | ---------- | ----------------------------------- |
+| 1 | `x` | spatial | `466380`, `466580`, `466780`, `466980`, `467180`, `467380` | 200m | [EPSG:32627](https://epsg.io/32627) |
+| 2 | `y` | spatial | `7167130`, `7166930`, `7166730`, `7166530`, `7166330`, `7166130`, `7165930` | 200m | [EPSG:32627](https://epsg.io/32627) |
+| 3 | `bands` | bands | `blue`, `green`, `red`, `nir` | 4 bands | - |
+| 4 | `t` | temporal | `2020-10-01`, `2020-10-13`, `2020-10-25` | 12 days | Gregorian calendar / UTC |
+
+> Table: Overview of the dimensions contained in our example raster datacube above. Reference: [openeo.org (2022). What are Data Cubes.](https://openeo.org/documentation/1.0/datacubes.html#what-are-datacubes)
+
+Dimension labels are usually either numerical or text (also known as "strings"), which also includes textual representations of timestamps or geometries for example.
+For example, temporal labels are usually encoded as [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) compatible dates and/or times and similarly geometries can be encoded as [Well-known Text (WKT)](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry) or be represented by their IDs.
+
+Dimensions with a natural/inherent order (usually all temporal and spatial raster dimensions) are always sorted. Dimensions without inherent order (usually `bands`), retain the order in which they have been defined in metadata or processes (e.g. through [`filter_bands`](https://processes.openeo.org/#filter_bands)), with new labels simply being appended to the existing labels.
+
+### Geometry as a Dimension
+
+A geometry dimension is not included in the example raster datacube above and it is not used in the following examples, but to show how a vector dimension with two polygons could look like:
+
+| name | type | labels | reference system |
+| ---------- | ------ | ------ | ---------------- |
+| `geometry` | vector | `POLYGON((-122.4 37.6,-122.35 37.6,-122.35 37.64,-122.4 37.64,-122.4 37.6))`, `POLYGON((-122.51 37.5,-122.48 37.5,-122.48 37.52,-122.51 37.52,-122.51 37.5))` | [EPSG:4326](https://epsg.io/4326) |
+
+> Table: Geometry as a Dimension. Reference: [openeo.org (2022). What are Data Cubes.](https://openeo.org/documentation/1.0/datacubes.html#what-are-datacubes)
+
+A dimension with geometries can consist of points, linestrings, polygons, multi points, multi linestrings, or multi polygons.
+It is not possible to mix geometry types, but the single geometry type with their corresponding multi type can be combined in a dimension (e.g. points and multi points).
+
+EO datacubes contain scalar values (e.g. strings, numbers or boolean values), with all other associated attributes stored in dimensions (e.g. coordinates or timestamps). Attributes such as the CRS or the sensor can also be turned into dimensions. Be advised that in such a case, the uniqueness of pixel coordinates may be affected. When usually, `(x, y)` refers to a unique location, that changes to `(x, y, CRS)` when `(x, y)` values are reused in other coordinate reference systems (e.g. two neighboring UTM zones).
+
+### Coordinate Reference System as a Dimension
+
+In the example above, _x_ and _y_ dimension values have a _unique_ relationship to world coordinates through their coordinate reference system (CRS). This implies that a single coordinate reference system is associated with these _x_ and _y_ dimensions. If we want to create a data cube from multiple tiles spanning different coordinate reference systems (e.g. Sentinel-2: different UTM zones), we would _have_ to resample/warp those to a single coordinate reference system. In many cases, this is wanted because we want to be able to _look_ at the result, meaning it is available in a single coordinate reference system.
+
+Resampling is however costly, involves (some) data loss, and is in general not reversible. Suppose that we want to work only on the spectral and temporal dimensions of a data cube, and do not want to do any resampling. In that case, one could create one data cube for each coordinate reference system. An alternative would be to create one _single_ data cube containing all tiles that has an _additional dimension_ with the coordinate reference system. In that data cube, _x_ and _y_ no longer point to a unique world coordinate, because identical _x_ and _y_ coordinate pairs occur in each UTM zone. Now, only the combination (_x_, _y_, _crs_) has a unique relationship to the world coordinates.
+
+On such a _crs-dimensioned data cube_, several operations make perfect sense, such as `apply` or `reduce_dimension` on spectral and/or temporal dimensions. A simple reduction over the `crs` dimension, using _sum_ or _mean_ would typically not make sense. The "reduction" (removal) of the `crs` dimension that is meaningful involves the resampling/warping of all sub-cubes for the `crs` dimension to a single, common target coordinate reference system.
+
+### Resolution
+
+The resolution of a dimension gives information about what interval lies between observations. This is most obvious with the temporal resolution, where the intervals depict how often observations were made. Spatial resolution gives information about the pixel spacing, meaning how many 'real world meters' are contained in a pixel. The number of bands and their wavelength intervals give information about the spectral resolution.
+
+
+
+> Images generated by LexCube - [Leipzig Explorer of Earth Data Cubes](https://www.lexcube.org/) by [Maximilian Söchting](https://rsc4earth.de/authors/msoechting/)
+
+
+[](https://www.youtube.com/watch?v=I6anJ5xaM8E)
+> Video content in cooperation with [Gunnar Brandt](https://www.brockmann-consult.de/about-us/) (Brockmann Consult), [Pontus Lurcock](https://www.brockmann-consult.de/about-us/) (Brockmann Consult) and [Miguel Mahecha](https://www.uni-leipzig.de/en/profile/mitarbeiter/prof-dr-miguel-mahecha) (University of Leipzig).
+
+### Data Cubes in Cloud Platforms
+
+One important feature of an EO cloud platform is to host satellite data. Usually vast amounts of satellite data, for example the whole archive of Landsat and the Sentinels which adds up to Petabytes of data. Users want to access this data efficiently. Therefore the storage system of an EO cloud platform needs to be optimized towards user requests. Data Cubes play an important role here. Organizing the raw files into virtual data cubes solves many issues for EO cloud platforms.
+
+- **Data Management:** Different Satellites have different formats and are structured differently. This can easily create confusion because the different sources have to be treated differently. After organizing these files into data cubes the interaction with all different satellite data follows the same rules.
+- **Separated Collections:** Every satellite missions images form an own data cube, also called collection, with its according metadata. Ideally cloud processing tools on the platform allow for easy data fusion of different collections.
+- **Flexible Subsetting:** [Data Cube Management Systems](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/1.2_data_cube/1.2_data_cube.md#further-reading) are developed to facilitate subsetting operations along the different dimensions and return only the required subset. Without worrying about the different raw files that are involved (e.g. area of interest is crossing a tile border).
+
+> :warning: Remember, a data cube is not an EO cloud platform. It's only one part of it.
+
+### Further Reading
+
+- [David Montero Loaiza et al (2023). Data Cubes for Earth System Research: Challenges Ahead. Earth ArXiv (Preprint). https://doi.org/10.31223/X58M2V](https://doi.org/10.31223/X58M2V)
+- Popular Data Cube Management Systems:
+ - [Open Data Cube](https://www.opendatacube.org/)
+ - [Rasdaman](https://www.rasdaman.com/):
+ - [GDAL Cubes](https://gdalcubes.github.io/)
+
+### References
+
+- [openeo.org (2022). What are Data Cubes.](https://openeo.org/documentation/1.0/datacubes.html#what-are-datacubes)
+- [ESA (2018). Earth System Data Lab.](https://www.esa.int/ESA_Multimedia/Videos/2018/07/Earth_System_Data_Lab)
+- [Maximilian Söchting (2022). LexCube - Leipzig Explorer of Earth Data Cubes](https://www.lexcube.org/)
+
+## Quiz
+
+What is a two dimensional data cube?
+
+ [( )] vector, a one dimensional array
+ [( )] atomic feature, a single value
+ [(x)] table, in EO a raster
+
+Check the typical dimensions of an EO data cube.
+
+ [[x]] x
+ [[ ]] rain
+ [[x]] y
+ [[ ]] function
+ [[x]] time
+ [[ ]] world
+ [[x]] bands
+
+What is the main puorpose of a geometry dimension?
+
+ [(x)] storing geometries (e.g. polygons, points)
+ [( )] it's holding the bounding box of the data cube
+ [( )] defining the shape of the pixels
+
+Attach the resolutions to their dimensions
+
+- [[x and y] [time] [band]
+- [(X) ( ) ( ) ] meter, kilometer, degrees
+- [( ) (X) ( ) ] days, hours, years
+- [( ) ( ) (X) ] micrometers, color
+
+What are the advantages of data cubes in EO?
+
+ [[x]] EO data is multidimensional data cubes allow to represent this
+ [[ ]] Data cubes introduce an unnecessary layer of complexity that is not needed in EO
+ [[ ]] The file size of EO data is drastically reduced
+
+What are the advantages of data cubes in cloud platforms?
+
+ [[x]] The user is not confronted with multiple files and file types
+ [[x]] Different satellite missions can be stored in different data cubes that can be combined for analysis on the platform
+ [[x]] subsetting operations become very powerful - users only receive the extents they are interested in.
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+# Open Science and the FAIR Principles
+
+## Learning Objectives
+
+- Understand what Open Science is and why we need it
+- Get an overview of what belongs to Open Science
+- Get to know the FAIR principles
+
+## Why do we need Open Science
+
+[](https://www.youtube.com/watch?v=VSXLe2rNO_c)
+> Video content in collaboration with [Anca Anghelea](https://www.linkedin.com/in/ancaandreeapopescu/?originalSubdomain=it) (ESA) and [Chelle Gentemann](https://www.linkedin.com/in/chelle-gentemann/) (NASA).
+
+Space Agencies and International Organisations across the globe promote Open Science and support its practice by the scientific community through dedicated programmes. This course, for example, is part of the support to Earth Observation Open Science by the European Space Agency (ESA).
+
+ESA, just like other European organisations such as the European Commission, has a long standing commitment to Open Science. A prominent example is the provision of full free and open Earth Observation data from its science missions (i.e. the Earth Explorers) and from the Copernicus Programme (e.g. the Sentinel missions). However, Open Science goes much beyond open data. ESA's vision for Open Science covers the full research cycle (see ESA vision figure). Simply put, a piece of research fully adheres to Open Science principles if:
+
+- all the data, code, and documentation of the respective research are FAIR (see [What is FAIR?](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/1.3_openscience/1.3.1_openscienceandfair.md#what-is-fair) section), Open and linked to one another
+- the research is reproducible across various platforms (i.e. cloud platforms or computational systems)
+- the research is maintained so that it is accessible to the community in the long-term
+
+This requires that the scientific community adheres to the same common (or compatible) practices when writing and documenting code, preparing and sharing data or publishing their research in journals.
+
+- [Role of Open Science in ESA's Agenda 2025](https://www.esa.int/About_Us/ESA_Publications/Agenda_2025)
+- [NASA Transform to Open Science (TOPS)](https://science.nasa.gov/open-science/transform-to-open-science) within [NASA’s Open-Source Science Initiative (OSSI)](https://science.nasa.gov/researchers/open-science/)
+- [American Geoscience Union's vision of open science](https://www.agu.org/open-science)
+
+
+
+[](https://www.youtube.com/watch?v=BIHuPGg0YT0)
+
+## What is Open Science?
+
+### Open Science
+
+Open Science offers new opportunities in dealing with scientific knowledge and represents a kind of cultural change. Through transparency and openness, Open Science increases the use and further development of knowledge as well as the potential for collaboration and the credibility of science. The focus is on free access to scientific processes and findings for everyone via the internet and the right to re-use this content. The beneficiaries of this concept are not only science, but also society and the economy.
+
+Open Science is realised through various strategies and procedures, such as free access to scientific publications (Open Access), computer programmes (Open Source Software), data (Open Data) and educational materials (Open Educational Resources).
+
+[](https://www.youtube.com/watch?v=3m6p6w8oOw4)
+
+### The 4 Rs of Open Science
+
+When opening your science think about the 4 Rs:
+
+- **Reliable.** It is important to evaluate the research in two ways. First with respect to scientific principles and criteria like validity, second with respect to criteria out of the professional context. This will help ensure that your results are more reliable.
+- **Reproducible.** Transparency is critical when doing research. Open Science allows you to clearly show what you've done to get the results you have. By being open about your methods, processes and decision making during your research, someone else doing the research again should get the same results.
+- **Reusable.** By making research results reusable, you allow others to build upon the solid foundation your research has already created in a given subject. This is the same R that is also going to be mentioned in the FAIR principles.
+- **Relevant.** Research quality describes the measurable influence of academic research on the academic community. Research impact includes environmental, cultural and societal impact, economic returns and societal benefits. By adhering to open science you increase the chances of your research to be relevant, because you give others the chance to interact with it.
+
+### The four pillars of Open Science
+
+The four pillars of Open Science are:
+
+- **Data.** Data-driven research is fast becoming the norm in all disciplines. To support validation of your findings and allow others to build upon your work, you first need to make sure that others can find your data. This means giving them persistent and unique identifiers (such as DOIs); assigning appropiate metadata so that others can find and reuse your data; putting them into a repository that supports public searching; and being clear about what others can and can't do with them by applying an appropriate license. In the Further Reading section you find links to courses about managing and sharing research data and licensing your outputs.
+- **Code.** When sharing your software and code, be sure to make use of open source standards to support interoperability and their longer-term viability. Be sure to put your code somewhere where others can search for it and access it (e.g., Github). Additionally, you can give your code a DOI by registering your Github repo on Zenodo. You should also be clear about the license the code is being shared under. In the Further Reading section there's a course about Open Source Software.
+- **Papers.** Open Access (OA) to publications is a key component of Open Science. Free and instant access to publications improves the speed of innovation and leads better cooperation and progress in solving grand challenges. To publish openly, you'll need to be able to source an appropriate OA journal or discipline-specific repository and navigate your way through their publishing agreements. You should also consider sharing preprints of your work as a means of getting early feedback and community validation of your approaches. In some cases, you'll need to pay an Article Processing Charge to publish in an OA journal.
+- **Reviews.** The peer review process is evolving. By making the peer review process more transparent, researchers have better access to peer feedback at an earlier stage in the lifecycle and consumers of research outputs can have greater confidence in their quality.
+
+But there is way more to discover about Open Science. The Open [Science Taxonomy graphic](https://doi.org/10.6084/m9.figshare.1508606.v3 "Knoth, Petr; Pontika, Nancy (2015): Open Science Taxonomy. figshare. Figure. https://doi.org/10.6084/m9.figshare.1508606.v3") shows the different terms behind Open Science.
+
+
+
+### Further Reading
+
+- [FOSTER Open Science (2022). Managing and Sharing Research Data.](https://www.fosteropenscience.eu/learning/managing-and-sharing-research-data/#/id/5b2ccc7d7ce0b17553f69063)
+- [FOSTER Open Science (2022). Open Licensing.](https://www.fosteropenscience.eu/learning/open-licensing/#/id/5b618775ddd75cab7db42b31)
+- [FOSTER Open Science (2022). Open Source Software and Workflows.](https://www.fosteropenscience.eu/learning/open-source-software-and-workflows/#/id/5abf67d9dd1827131b90e6bd)
+- [ESA Earth System Data Lab](https://www.earthsystemdatalab.net/)
+- [ESA Open Science Persisten Demonstrator](https://www.ogc.org/initiatives/open-science/)
+- [NASA Common Metadata Repository](https://www.earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/cmr)
+- [NASA VEDA Platform](https://www.earthdata.nasa.gov/esds/veda)
+
+### References
+
+- [Fürst, Elena, Gänsdorfer, Nikos, Kalová, Tereza, Macher, Therese, Schranzhofer, Hermann, Stork, Christiane, & Thöricht, Heike (2022). Open Educational Resources Research Data Management. DOI: 10.5281/zenodo.6923397](https://github.com/schranzhofer/OER_for_RDM/blob/master/OER_for_RDM_English.md#open-science "FAIR Data Austria (2021). Open Educational Resources Research Data Management. DOI: 10.5281/zenodo.6923397")
+- [FOSTER Open Science (2022). What is Open Science.](https://www.fosteropenscience.eu/learning/what-is-open-science/#/id/5ab8ea32dd1827131b90e3ac)
+- [Loek Brinkman, Elly Dijk, Hans de Jonge, Nicole Loorbach, & Daan Rutten. (2023). Open Science: A Practical Guide for Early-Career Researchers (1.0). Zenodo. https://doi.org/10.5281/zenodo.7716153](https://doi.org/10.5281/zenodo.7716153)
+
+## What is FAIR?
+
+### FAIR
+
+In 2014, a group of researchers as well as employees of libraries, archives, publishers and funders established principles for the handling of research data in a workshop and published them on FORCE11 for reviews and comments. The so-called FAIR principles were born. They comprise four goals: the findability, accessibility, interoperability and re-usability of data. With the achievement of these goals, the sustainable re-usability of research data is meant to be guaranteed. FAIR is not binary: FAIR is a spectrum!
+
+> The OpenAIRE definition of Metadata: Metadata is data providing information about data that makes findable, trackable and (re)usable. It can include information such as contact information, geographic locations, details about units of measure, abbreviations or codes used in the dataset, instrument and protocol information, survey tool details, provenance and version information and much more. In earth observation satellite data that could be the spatial and temporal extent of the data, the sensor, the bands and their wavelenghts, etc. Section [2.1 Data Discovery](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/2.1_data_discovery/2.1_data_discovery.md) and [2.2 Data Properties](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/2.2_data_properties/2.2_data_properties.md) deal with EO metadata in particular.
+
+- **Findable**
+
+ - The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
+
+ - F1. (Meta)data are assigned a globally unique and persistent identifier
+ - F2. Data are described with rich metadata (defined by R1 below)
+ - F3. Metadata clearly and explicitly include the identifier of the data they describe
+ - F4. (Meta)data are registered or indexed in a searchable resource
+
+ - Persistent Identifier (PID): These are IDs that identify a data set, publication or software (any digital object) unambiguosly and persistently with a single link. It increases the findability of a resource drastically. A widely used identifier is the [Digital Object Identifier (DOI)](https://www.doi.org/).
+
+- **Accessible**
+
+ - Once the users find the required data, they need to know how they can be accessed, possibly including authentication and authorisation.
+
+ - A1. (Meta)data are retrievable by their identifier using a standardised communications protocol
+ - A1.1 The protocol is open, free, and universally implementable
+ - A1.2 The protocol allows for an authentication and authorisation procedure, where necessary
+ - A2. Metadata are accessible, even when the data are no longer available
+
+- **Interoperable**
+
+ - The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
+
+ - I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
+ - I2. (Meta)data use standards, formats and vocabularies that follow FAIR principles and allow it to be exchanged and combined across computer systems
+ - I3. (Meta)data include qualified references to other (meta)data
+
+- **Reusable**
+
+ - The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings
+
+ - R1. (Meta)data are richly described with a plurality of accurate and relevant attributes
+ - R1.1. (Meta)data are released with a clear and accessible data usage license
+ - R1.2. (Meta)data are associated with detailed provenance
+ - R1.3. (Meta)data meet domain-relevant community standards
+
+ - Licenses: To make your data reusable the use of appropiate licenses is key. Here is a good starting point to learn about the widely used [creative commons licenses](https://creativecommons.org/licenses/?lang=en).
+
+[](https://www.youtube.com/watch?v=2uZxFu9SFi8)
+
+#### Animated Content: FAIR (drag and drop)
+
+
+
+### Further Reading
+
+- **Nature:** A comment regarding FAIR Principles: [Wilkinson, M. D. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018](http://doi.org/10.1038/sdata.2016.18)
+- **European Commission:** EC FAIR-Principles: [European Commission. Action Plan for FAIR data recommendations](https://ec.europa.eu/info/sites/default/files/turning_fair_into_reality_1.pdf), [The EC expert group on FAIR data](http://ec.europa.eu/transparency/regexpert/index.cfm?do=groupDetail.groupDetail&groupID=3464&NewSearch=1&NewSearch=1), [EC/H2020 – Guidelines on FAIR Data Management in Horizon 2020](http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf)
+- **GO FAIR**: GO FAIR is a bottom-up, stakeholder-driven and self-governed initiative that aims to implement the [FAIR data principles](https://www.go-fair.org/fair-principles/), making data Findable, Accessible, Interoperable and Reusable (FAIR). It offers an open and inclusive ecosystem for individuals, institutions and organisations working together through [Implementation Networks](https://www.go-fair.org/implementation-networks/) (INs). The INs are active in three activity pillars: [GO CHANGE](https://www.go-fair.org/fields-of-action/go-change/), [GO TRAIN](https://www.go-fair.org/fields-of-action/go-train/) and [GO BUILD](https://www.go-fair.org/fields-of-action/go-build/).
+- **Train-the-Trainer:** Training material for “FAIR” in the train-the-trainer program for Research Data Management: Biernacka, et al. (2020). Train-the-Trainer Concept on Research Data Management (Version 3.0). Zenodo. (p. 38)
+- **OPENAIRE:** A network of Open Access repositories, archives and journals that support Open Access policies. The OpenAIRE Consortium is a [Horizon 2020](https://en.wikipedia.org/wiki/Framework_Programmes_for_Research_and_Technological_Development#Horizon_2020) (FP8) project, aimed to support the implementation of the [EC](https://en.wikipedia.org/wiki/European_Council) and [ERC Open Access](https://en.wikipedia.org/wiki/Open_Access) policies.
+- **Codata:** FAIR-Principles and the Committee on Data for Science and Technology (Codata): The [Committee on Data for Science and Technology](https://codata.org/) (CODATA) is a Paris-based organization with the aim of improving the quality, reliability and accessibility of interesting data from all fields of science and technology. [Hodson, S. (2018). Making FAIR data a reality… and the challenges of interoperability and reusability. Open Science Conference 2018.](https://www.open-science-conference.eu/wp-content/uploads/2018/03/OSC2018_Hodson.pdf)
+- **FAIR Workflows:** Also workflows can be made FAIR. This is a quite new topic. Here's the [workflows communities approach](https://workflows.community/groups/fair/)
+- **History of FAIR:** Information on the [history of the FAIR principles](https://www.datafairport.org/)
+
+### References
+
+- [OpenAIRE (2017). What is Metadata.](https://www.openaire.eu/what-is-metadata)
+- [Fürst, Elena, Gänsdorfer, Nikos, Kalová, Tereza, Macher, Therese, Schranzhofer, Hermann, Stork, Christiane, & Thöricht, Heike (2022). Open Educational Resources Research Data Management. DOI: 10.5281/zenodo.6923397](https://github.com/schranzhofer/OER_for_RDM/blob/master/OER_for_RDM_English.md#fair-principles)
+- [GO FAIR (2023). FAIR Principles.](https://www.go-fair.org/fair-principles/)
+- [Creative Commons (2023). About the licenses.](https://creativecommons.org/licenses/?lang=en)
+
+## Exam
+
+Let's test your theoretical knowledge on open science now. It's important that you understand these concepts. We will apply them later on in the course!
+
+What do the 4 Rs in the context of Open Science stand for?
+
+ [( )] Readable, refreshable, recognizable, and receivable
+ [(X)] Reliable, reproducable, reusable, and relevant
+ [( )] Recitable, renameable, replicatable, and repairable.
+
+Which statement is correct about Open Science?
+
+ [( )] Open Science sounds good, but it is unfair because researchers with limited financial means cannot afford access to open research results.
+ [(X)] Open Science promotes the transparency of science and the free reuse of existing research results by everyone.
+ [( )] Open Science ensures better networking of researchers within the EU. A worldwide exchange is currently not possible.
+
+What are the arguments in favour of Open Data?
+
+ [( )] Generally, open data is not subject to a review process, so this type of data publication is always significantly faster.
+ [( )] Lower costs and less time needed to prepare the data than with a closed access publication.
+ [(X)] Accessibility of scientific data and metadata, source texts and digital reproductions.
+
+FAIR is an acronym that stands for...
+
+ [(X)] Findable, Accessible, Interoperable and Reusable
+ [( )] Fast Artificial Intelligence Research
+ [( )] Fair, Accurate, Inclusive and Respectful Education
+ [( )] Free, Available, Implemented and Ready
+
+Findable means...
+
+ [[X]] (Meta)data are assigned a globally unique and persistent identifier
+ [[ ]] (Meta)data are optimized to show up in the highest positions in google searches
+ [[X]] (Meta)data are registered or indexed in a searchable resource
+
+Accessible means…
+
+ [[ ]] (Meta)data must be open.
+ [[X]] (Meta)data does not necessarily have to be open.
+ [[X]] (Meta)data has access conditions and these are clear to both humans and machines.
+ [[ ]] (Meta)data is open for access, but not for reuse.
+
+Interoperable means...
+
+ [( )] (Meta)data is created and described by at least two researchers from differenct institutes
+ [(X)] (Meta)data use standards, formats and vocabularies that allow it to be exchanged and combined across computer systems and between humans
+
+Reusable means…
+
+ [(X)] the data has clear usage licenses and is usable by both people and machines.
+ [( )] that all data is usable.
+ [( )] the data has clear usage licenses to be used by people.
diff --git a/_sources/1.3_openscience/1.3.2_opendataopensource.md b/_sources/1.3_openscience/1.3.2_opendataopensource.md
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+# Open Data and Open Source Software
+
+## Learning Objectives
+
+- Understand what Open Data is
+- Understand what Open Source Software is
+
+## What is Open Data?
+
+### Open Data
+
+Open data is data that **anyone can access, use and share**. Open data becomes usable when made available in a common, machine-readable format. **Open data must be licensed**. Its licence must permit people to use the data in any way they want, including transforming, combining and sharing it with others, even commercially. Any restrictions imposed on the use of open data will limit its potential for creating new value.
+
+- **Limitations:** For data to be open, it should have no limitations that prevent it from being used in any particular way. Anyone should be free to use, modify, combine and share the data, even commercially
+- **Cost:** Open data must be free to use, but this does not mean that it must be free to access. There is often a cost to creating, maintaining and publishing usable data. Ideally, any fee for accessing open data should be no more than the reasonable reproduction cost of the unit of data that is requested. This reproduction cost tends to be negligible for many datasets. Live data and big data can incur ongoing costs related to reliable service provision.
+- **Reuse:** Once the user has the data, they are free to use, reuse and redistribute it – even commercially. Open data is measured by what it can be used for, not by how it is made available. Aspects like format, structure and machine readability all make data more usable, and should all be carefully considered. However, these do not make the data more open.
+- **FAIR vs Open Data:** FAIR data is not the same as open data. For example, it is not always possible to grant free access to data for economic and legal reasons. Restrictions on access are compatible with FAIR principles, as long as the conditions and ways of access are evident.
+
+[](https://www.youtube.com/watch?v=ULJSlXuk0FU)
+
+### Open Data in this course
+
+The creation of this course would not be possible without Open Data. Here are just a few examples:
+
+- [This course](https://github.com/EO-College/cubes-and-clouds#license) itself
+- Most of the referenced learning resources e.g. [Open Science: A Practical Guide for Early-Career Researchers](https://zenodo.org/record/7716153) that we based the first part of this lesson on.
+- [ESA Sentinel Satellite Data](https://open.esa.int/copernicus-sentinel-satellite-imagery-under-open-licence/)
+
+### References
+
+- [European Commission (2022). What is open data?. https://data.europa.eu/elearning/en/module1/#/id/co-01](https://data.europa.eu/elearning/en/module1/#/id/co-01) - This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+- [Open Knowledge Foundation (2022). Open Data Handbook.](https://opendatahandbook.org/guide/en/what-is-open-data/)
+- [Fürst, Elena, Gänsdorfer, Nikos, Kalová, Tereza, Macher, Therese, Schranzhofer, Hermann, Stork, Christiane, & Thöricht, Heike (2022). Open Educational Resources Research Data Management. DOI: 10.5281/zenodo.6923397](https://github.com/schranzhofer/OER_for_RDM/blob/master/OER_for_RDM_English.md#fair-data-vs-open-data)
+
+> Since we use material from the European Commissions [data.europa.eu e-learning programme], which is published under the *Creative Commons Attribution-ShareAlike 4.0 International License* we have to publish this section **What is Open Data** under This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
+
+## What is Open Source?
+
+### Open Source
+
+Open Source does not simply mean that the source code of a project is available, which is only one element of an Open Source project. The Open Source Initiative (OSI) provides a commonly accepted [definition](https://opensource.org/osd/) of what constitutes Open Source. To summarize that, in order to be considered Open Source:
+
+- Open Source Software needs a license,
+- a work has to allow free redistribution,
+- the source code needs to be made available,
+- it must be possible to create further works based on it,
+- there must be no limitations of who may use the work or for what purpose (so something like "no commercial use" or "no military use" won't work with Open Source),
+- the work must not require an additional license on top of the one it comes with,
+- and finally, the license must not depend on a specific distribution format, technology or presence of other works.
+
+
+
+[](https://www.youtube.com/watch?v=1ehpgbb3XD0)
+
+### Open Source Software used in this course
+
+The creation of this course would not be possible without Open Source Software. Here are just a few examples of Open Source Software used in this course:
+
+- [Python](https://docs.python.org/3/license.html), used in the coding exercises
+- [Wordpress](https://wordpress.org/about/license/), powering EOCollege's content
+- [git](https://git-scm.com/about/free-and-open-source), for versioning the content of this course and collaborating with colleagues
+- [openEO](https://openeo.org/about.html#openeo), used in the coding exercises for standarized interaction with cloud platforms
+- [STAC Spec](https://stacspec.org/en), for standardizing metadata, so that we can find the data we need and create
+- [leaflet](https://github.com/Leaflet/Leaflet/blob/main/LICENSE) for the interactive visualization of results
+- [GDAL](https://gdal.org/license.html), powering most geospatial software and is the backbone of many EO cloud platforms
+
+### Further Reading
+
+Help for understanding licenses and choosing the right Open Source license
+
+- [tl;drLegal (FOSSA) (2023). Software Licencses In Plain English.](https://www.tldrlegal.com/)
+- [GitHub Inc. (2023) Choose an open source license.](https://choosealicense.com/)
+
+And plentiful resources on open source projects, how to contribute and incorporate them into your work
+
+- [opensource.com (2023). Open Source resources.](https://opensource.com/resources)
+
+### References
+
+- [Open Source Initiative (2007). The Open Source Definition (v1.9).](https://opensource.org/osd/)
+- [Gina Häußge (2022). A dev’s guide to open source software licensing. The ReadME Project.](https://github.com/readme/guides/open-source-licensing)
+
+## Exam
+
+Which statement about Open Data is correct?
+
+ [(X)] Open Data means information that is freely accessible.
+ [( )] With Open Data, only data that is related to a scientific interpretation can be considered.
+ [( )] With Open Data, the availability and usability of data on the web is limited.
+
+FAIR data is always open data.
+
+ [( )] True
+ [(X)] False
+
+What is true about Open Source Software projects
+
+ [[X]] The source code is completely available to the public.
+ [[ ]] You cannot contribute to Open Source Software Projects.
+ [[X]] Open Source Software Projects are community driven.
+ [[ ]] You can never use Open Source Software for commercial purposes.
+ [[ ]] If software is published under a license, it is not open source.
+
+What is GitHub?
+
+ [( )] GitHub is a cloud storage system specialized in storing big earth observation data sets.
+ [(X)] GitHub is a code hosting platform for version control and collaboration. It lets you and others work together on projects from anywhere.
+
+Find the following GitHub repositories and copy their link into the text box. *Copy the complete link starting with `https://`*
+
+| Project | Link |
+|------------------------------------------------------------|-----------------------------------------------------|
+| openEO python client | [[https://github.com/Open-EO/openeo-python-client]] |
+| Spatio Temporal Asset Catalogue Specification (STAC Spec) | [[https://github.com/radiantearth/stac-spec]] |
+| Geographic Data Abstraction Library (GDAL) | [[https://github.com/OSGeo/gdal]] |
diff --git a/_sources/1.3_openscience/1.3.3_openscienceineo.md b/_sources/1.3_openscience/1.3.3_openscienceineo.md
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+# The Open Science Journey - Open Science in geospatial, EO and EO cloud platforms
+
+## Learning Objectives
+
+- Understand what Open Science is
+- Get to know the FAIR concept
+- Follow the steps of creating Open Science
+- Understand the role of Open Science in geospatial, EO and EO cloud platforms
+
+## The Open Science Journey
+
+Finally let's see how open science principles are applied in the field of geospatial, earth observation and EO cloud platforms. To begin we will have a look at the open science journey and a research project that has adapted openness and the FAIR principles very well. Then we will have a look at the role open science plays in today's geospatial and EO world.
+
+This drag and drop game asks you to connect the tasks to their respective step within the open science journey. If you hover over the icons, their description will pop up.
+
+
+
+### Open Science in the ClirSnow Project
+
+The ClirSnow Project is a great example of how the concepts of opennes and FAIR are applied to a real world research project.
+
+[](https://www.youtube.com/watch?v=lmd9wwGNw6M)
+> Video content in collaboration with [Michael Matiu](https://mitmat.eu/) (University of Trento).
+> *"It seems like a lot of work the first time you do it. And it is. But once you know how to do it, you will use it in every research project, because it actually makes research so much easier. And, it will boost your research impact and credibility. It is really worth it."*
+
+## The Role of Open Source Software in Geospatial - The example of GDAL
+
+Open Science plays an important role in geospatial. Open source software is a part of that and the Geographic Data Abstraction Library (GDAL) software is a great example of how important open source software is in the geospatial world.
+Paul Ramsey, the co-founder of the PostGIS extension, has described what GDAL is in a metaphoric way in a [mapscaping.com podcast](https://mapscaping.com/podcast/gdal-geospatial-data-abstraction-library/): “GDAL is data plumbing, a bit like an international electrical plug set for traveling — it’s got multiple different shaped plugs. Electricity is “just” electrons moving around. But they can move around as DC, AC, 120 volts or 240 volts. Plus, there are all these different ways you can plug and join electrical things. At the core, electricity is electrons vibrating, but it can be quite complex to get your hair dryer spinning.”
+Howard Butler, a director of the Open-Source Geospatial Foundation, said about the importance of GDAL: “[…] It's open, it provides core functionality, I can't understand how anybody gets anything done without it.“
+
+[](https://www.youtube.com/watch?v=DbU35RqaQ-U)
+> Video content in collaboration with [Even Rouault](https://www.spatialys.com/en/about/) (Main Developer of GDAL).
+
+## Open Science in EO Cloud Platforms
+
+- **Code:** Workflows and Code can easily be shared on EO Cloud Platforms. There are openly available tutorial notebooks. Workflows can be shared as user defined processes and be reused by the community. There are user forums that share solutions and snippets. OpenEO, a standardized processing API for EO in the cloud, allows code to be portable between different cloud platforms. This increases reprodicibility, collaboration and prevents vendor locks.
+ - **To Do: Image Slider:** openEO Platform Forum, Tutorial Notebooks Microsoft Planetary Computer, User Defined Processes openEO,
+
+- **Results:** There are multiple ways to share results created in EO cloud platforms. Ideally they can be ingested into the platform and be made available as collections for other users directly upon creation. If the result comes with appropiate metadata (e.g. according to the STAC specification) they can easily be registered in publicly avialable STAC Catalogues. Cloud Native Data Formats (described in more detail in lesson [2.4 Formats and Performance](../2.4_formats_and_performance/2.4_formats_and_performance.md)), like cloud optimized geotiff, are accessible via https requests. So instead of sharing a file, only a URL pointing to the file is shared.
+ - **To Do: Image Slider**: Collection in a Platform, STAC Catalogue, Link to a COG
+
+- **Publication:** If a publication is built on top of results produced in an EO cloud platform, the results and code can easily be linked to the publication in one of the forms described aboved. For example, you can publish your openEO process graph and link to it, and provide a link to a STAC Catalogue where the results are accessible.
+ - **To Do: Example of a Publication where the code is available on a cloud platform**
+
+- **FAIRness:**
+ - Findable: Data is usually presented through a data catalogue (e.g. STAC Catalogues are used in openEO platform and the Microsoft Planetary Computer) that is explicitly made for searching data. In many cases searching data works even without registration on the platform.
+ - Accessible: Data access in cloud platforms is usually granted after registration and authentication. Since cloud computing resources can easily be misused a certain degree of access control is necessary.
+ - Interoperable: Processing standards like openEO aim at making the code interoperable, which means it is transferable between platforms. Standardised metadata attached to the results,the use of cloud optimized formats and reingestion of the results into the platform guarantee easy uptake of the results right away. Different sources of satellite data are made interoperable by the cloud platform through the use of data cubes and processing on the fly - reprojections, regridding and temporal alignment are enabled on the fly.
+ - Reusable: To make results reusable for others, they need to be accessible and have an open license. Ideally, a license of choice can be added to the metadata and the results are reingested into the platform as a public collection, available for everyone.
+
+- **Analysis Ready Data (ARD):** Analysis Ready Data are in the context of EO cloud platforms are usually satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets. This means for example that atmospheric correction and cloud masking has already been applied to optical data. Many collections on cloud platforms are analysis ready, so that users can directly start the analysis withouth the tedious and technically demanding preprocessing steps. Since 'analysis ready' can mean different things to different people, CEOS is working on standardizing what analysis ready data are.
+ - **To Do: Image of CEOS ARD**
+
+### References
+
+- [ARDC Ltd. (2022). How to Make Your Data FAIR. DOI: 10.5281/zenodo.7426145.](https://ardc.edu.au/resource/making-your-data-fair-a-flowchart/)
+- [GO FAIR (2022). The FAIRification Process.](https://www.go-fair.org/fair-principles/fairification-process/ )
+- [Mapscaping Podcast with Paul Ramsey (2021). GDAL - Geospatial Data Abstraction Library](https://mapscaping.com/podcast/gdal-geospatial-data-abstraction-library/)
+- [CEOS (2022). CEOS Analysis Ready Data.](https://ceos.org/ard/)
+
+## Exam
+
+Search a license from the [Creative Commons License Chooser](https://creativecommons.org/choose/) that adheres to the following: **proper attribution/citation must be included when used**, **free for commercial use** and **Adaptions of the work can be shared, but only under the same or a compatible license**.
+
+ [( )] CC BY 4.0
+ [(X)] CC BY-SA 4.0
+ [( )] CC BY-SA-ND 4.0
+
+Was the license you have just chosen a software license or a data license?
+
+ [( )] Software License
+ [(X)] Data License
+
+Find the Open Research Data Set *"Snow cover in the European Alps: Station observations of snow depth and depth of snowfall"* on the catalogue [OpenAIRE](https://explore.openaire.eu/).
+
+Share the DOI link to the data set version v1.3 in the format `https://doi.org/10.5281/zenodo.XXXXXXX`. *Hint the last numbers are 74*
+
+ [[https://doi.org/10.5281/zenodo.5109574]]
+
+On which repository is the data set registered?
+
+ [( )] Integrated Ocean Observing System (https://ioos.noaa.gov)
+ [( )] PANGAEA (https://pangaea.de)
+ [(X)] ZENODO (https://zenodo.org)
+
+Which license is used for the data set? Copy the URL to the license here.
+
+ [(X)] Creative Commons Attribution 4.0 International
+ [( )] Creative Commons Attribution-NonCommercial 4.0 International
+ [( )] Creative Commons Attribution-ShareAlike 4.0 International
+
+Find the open access publication that is connected to the dataset. The one that has been published in "The Cryosphere". Copy the DOI of the article here in the format `https://doi.org/XX.XXXX/tc-XX-XXXX-XXXX`. *Hint: The DOI ends with 21*
+
+ [[https://doi.org/10.5194/tc-15-1343-2021]]
+
+Under which license is this course published. You can find this out on the courses GitHub page.
+
+ [( )] Massachusets Institute of Technology License (MIT License)
+ [( )] Creative Commons Attribution-ShareAlike 4.0 International License
+ [(X)] Creative Commons Attribution 4.0 International License
+
+How is data FAIR in a cloud platform? Connect the subjects to the FAIR keywords.
+
+ [[Findable] [Accessible] [Interoperable] [Reusable]]
+ [(X) ( ) ( ) ( ) ] STAC Metadata, Metadata Catalogue
+ [( ) ( ) (X) ( ) ] Usage of abstract data cubes instead of different file formats
+ [( ) (X) ( ) ( ) ] Authentication, Login, Free Trial Accounts
+ [( ) ( ) ( ) (X) ] Data licenses attached to collections, provenance of the data is reported
+
diff --git a/_sources/2.1_data_discovery/2.1_data_discovery.md b/_sources/2.1_data_discovery/2.1_data_discovery.md
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+# Data Discovery
+
+## Learning Objectives
+
+- Find out what Data Sources we have
+- Get to know where to get data
+- Discover data catalogues
+- Learn what STAC is
+
+## What kind of data is available
+
+[](https://youtu.be/NafY3AIw6f0)
+> Video content in collaboration with [Angelos Tzotsos](https://www.linkedin.com/in/tzotsos/?originalSubdomain=gr) (President OSGeo) and [Tom Kralidis](https://www.linkedin.com/in/tomkralidis/?originalSubdomain=ca) (Meteorological Service of Canada).
+
+## Geospatial Data
+
+Knowledge of various geospatial data types is essential for understanding, representing, and analyzing acquired data from various domains. Data carry not only spatial information but also measured values, derived data, or other targeted information. Based on their properties, they can be used for different purposes.
+
+There are several common geospatial data types, each serving a specific purpose and having distinct characteristics.
+
+
+> Figure: Common data types in EO. Raster, Polygon, Point.
+
+### Raster data
+
+Raster data are represented as pixels and grid cells where every pixel has a value associated with it. Data stored in raster format can be discrete (representing distinct categories, eg. land cover types) or continuous (for example temperature or elevation).
+
+Examples of raster data sources:
+
+- **Satellite data** - Satellite imagery data are a common example of raster data. There are many types of data acquired by satellites and their processing. The temporal aspect of images is very important for continuous analysis.
+- **Aerial/drone orthophoto** - Digital imagery obtained by aircrafts (e.g. drones) is usually high-resolution raster data usable for detailed analysis.
+- **DEM** - Digital Elevation Models are representing the surface of (usually) Earth and are essential for computations on a surface level.
+- **Cloud mask** - Cloud mask refers to the task of cloud detection in optical satellite data, where the common task is to detect (and remove) clouds to obtain better quality data.
+
+### Vector data
+
+Vector data are a fundamental type of geospatial data. Vector data represents spatial features using 3 basic classes - points, lines, and polygons and the attributes associated with them. With vector data, it is possible to capture and analyze complex spatial patterns and attributes with great accuracy, providing valuable insights into spatial phenomena and supporting decision-making processes. Vector data are commonly used in addition to raster data to highlight or accompany trends and results.
+
+Examples of vector data sources:
+
+- **Building footprints (Polygons)** - Building footprints are usually represented as polygons and capture the perimeter of buildings on the Earth's surface. These footprints can vary in complexity from simple rectangles for standard buildings to more intricate shapes that outline the exact contours of complex structures.
+- **Street networks** - Street networks are depicted as lines, representing the pathways through which vehicles and pedestrians can move. Each line segment can indicate a portion of a street, alley, highway, or path, connecting intersections and endpoints to map out the entire transportation infrastructure of an area.
+- **Points of interest** - Points of Interest (POIs) are example of point vector data. They represents specific locations that hold importance or interest within a geographic area or usecase. These can include landmarks, businesses, public facilities, and natural features, among others.
+
+
+### In-situ data
+
+In-situ data are data collected directly at the place of interest and are connected to a special location. There are many reasons why these data are important and one of them is an enhancement of data collected by other means eg. weather measurements from satellites enhanced with local air quality measurements. There are countless examples of what can be measured, two common examples are:
+
+- **Air temperature**
+- **Soil quality**
+
+### Datacubes
+
+Datacube refers to a multidimensional representation of data that incorporates spatial and temporal dimensions. It is a concept used to organize and analyze large volumes of geospatial information in a structured and efficient manner. Datacubes are combining the data types discussed above into one data structure. More about datacubes is in [Lesson 1.2](../1.2/datacubes.md)
+
+
+# EO Catalog protocols
+
+For Earth observation data discovery, it is essential to know about various used catalog protocols that define standardized methods and formats for organizing, describing, and accessing Earth observation data catalogs.
+
+Some commonly used protocols shared between diffent platforms include **OpenSearch**, **OGC CSW**, **OGC API - Features**, **OGC API - Records**, **STAC**, **OData** and more.
+
+## OpenSearch
+
+The OpenSearch specification was launched in 2005 by A9.com, an Amazon subsidiary, as a means for sharing search queries and search results in a standardized format.
+The specification was intended to allow syndication of search results that could then be aggregated by one large index.
+OpenSearch provides a simple to use description of the search interface, which is called an OpenSearch Description document (OSDD).
+A client (e.g., a browser) can use this description to check which response formats are supported and how a query/filter can be formulated.
+The OpenSearch based REST services are usually offered by existing EO data platforms for compatibility reasons as the protocol itself is stable and not extended anymore.
+The data models of most catalogs build on top of XML or GeoJSON and allow filtering on a set of simple but not standardized properties. The protocol supports both textual and geospatial search and filtering capabilities, making it suitable for a wide range of applications, including web search engines and geospatial data catalogs.
+
+## OGC CSW
+
+Catalogue Service for the Web (CSW) standardized by Open Geospatial Consortium (OGC), offers a framework for publishing, discovering, and accessing metadata records, allowing users to effectively search and retrieve geospatial data and related information. The catalogue is made up of records that describe geospatial data, linked geospatial services and related resources.
+CSW enables metadata query using metadata core (mandatory) elements.
+Catalogue services support the use of one of several identified query languages to find and return results using well-known content models (metadata schemas) and encodings.
+
+## OGC API - Features
+
+OGC API - Features is a modern and flexible geospatial data access protocol developed by the OGC. It provides a standardized and RESTful approach for querying, retrieving, and manipulating geospatial feature data over the web. By leveraging the power of web technologies such as HTTP, JSON, and GeoJSON, OGC API - Features simplifies the process of accessing and working with geospatial data. It allows users to retrieve specific features based on spatial and attribute filters, perform spatial and attribute queries, and even modify feature data through standard HTTP methods.
+
+## OGC API - Records
+
+OGC API - Records is a multi-part draft specification (built on top of OGC API - Features) that offers the capability to create, modify, and query metadata on the Web. The draft specification enables the discovery of geospatial resources by standardizing the way collections of descriptive information about the resources (metadata) are exposed. The specification also enables the discovery and sharing of related resources that may be referenced from geospatial resources or their metadata by standardizing the way all kinds of records are exposed and managed.
+
+## STAC
+
+STAC stands for SpatioTemporal Asset Catalog. It is a community specification that provides a common way for describing and cataloging assets that have a connection to space and time, usually but not necessarily on the Earth. The STAC specification focuses on organizing and sharing geospatial data in a way that is accessible, interoperable, and scalable. The STAC Specification consists of 4 semi-independent specifications (Catalog, Collection, Item and API) which can work independently or be used together. All of them can be and are enriched by a variety of extensions.
+It is a relatively new specification but increasingly integrated by various data providers and seen as future of EO Data cataloguing and discovery. The data model in the dataspace is still evolving to comply fully with all standardized properties. Because of that, more attention is provided to STAC than other catalogue protocols in this tutorial.
+
+
+### The components of STAC
+The STAC specification is divided into three main parts:
+- STAC specification for static catalogs, which consists of three parts:
+ - STAC Items
+ - STAC Catalogs
+ - STAC Collections
+- STAC API specification for dynamic catalogs.
+- STAC extensions (both for static STAC and the STAC API)
+
+All these components are fairly independent, but all components work together and use links to express the relationship between them so that eventually clients can traverse through them. The links to the actual spatio-temporal data files that the STAC metadata describes are handled specifically and are called STAC Assets. Assets can be made available in Items and Collections.
+
+
+
+>Image by Matthias Mohr from https://mohr.ws/foss4g/
+
+#### STAC Item
+
+A STAC Item is the foundational building block of STAC. It is a GeoJSON feature supplemented with additional metadata that enables clients to traverse through catalogs. Since an item is a GeoJSON, it can be easily read by any modern GIS or geospatial library. One item can describe one or more SpatioTemporal Asset(s). For example, a common practice of using STAC for imagery is that each band in a scene is its own STAC Asset and there is one STAC Item to represent all the bands in a single scene.
+
+The STAC Item JSON specification uses standard GeoJSON fields as well as a few additional informational fields to describe the asset(s) more thoroughly.
+
+STAC Item (and other components) have some required fields which must be always filled with information. In the example below, required fields like type, stac_version or id are filled. Properties are also required fields, but here also extended by many STAC extensions, in the format of extension_name:field_name: value. STAC extensions are also listed in the stac_extensions field. Complete STAC Item spec can be found on [GitHub](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md).
+
+
+ Example of a Sentinel 2 L2A STAC Item with one band asset.
+
+```json
+{
+ "type":"Feature",
+ "stac_version":"1.0.0",
+ "id":"S2B_43SCR_20231123_0_L2A",
+ "properties":{
+ "created":"2023-11-23T08:25:34.597Z",
+ "platform":"sentinel-2b",
+ "constellation":"sentinel-2",
+ "instruments":[
+ "msi"
+ ],
+ "eo:cloud_cover":0.414053,
+ "proj:epsg":32643,
+ "mgrs:utm_zone":43,
+ "mgrs:latitude_band":"S",
+ "mgrs:grid_square":"CR",
+ "grid:code":"MGRS-43SCR",
+ "view:sun_azimuth":161.976971280106,
+ "view:sun_elevation":35.5947611411469,
+ "s2:degraded_msi_data_percentage":0,
+ "s2:nodata_pixel_percentage":77.337396,
+ "s2:saturated_defective_pixel_percentage":0,
+ "s2:dark_features_percentage":0,
+ "s2:cloud_shadow_percentage":0.002591,
+ "s2:vegetation_percentage":0.010072,
+ "s2:not_vegetated_percentage":98.205149,
+ "s2:water_percentage":0.9589,
+ "s2:unclassified_percentage":0.38101,
+ "s2:medium_proba_clouds_percentage":0.410817,
+ "s2:high_proba_clouds_percentage":0.003235,
+ "s2:thin_cirrus_percentage":0,
+ "s2:snow_ice_percentage":0.028226,
+ "s2:product_type":"S2MSI2A",
+ "s2:processing_baseline":"05.09",
+ "s2:product_uri":"S2B_MSIL2A_20231123T054139_N0509_R005_T43SCR_20231123T070647.SAFE",
+ "s2:generation_time":"2023-11-23T07:06:47.000000Z",
+ "s2:datatake_id":"GS2B_20231123T054139_035066_N05.09",
+ "s2:datatake_type":"INS-NOBS",
+ "s2:datastrip_id":"S2B_OPER_MSI_L2A_DS_2BPS_20231123T070647_S20231123T054133_N05.09",
+ "s2:granule_id":"S2B_OPER_MSI_L2A_TL_2BPS_20231123T070647_A035066_T43SCR_N05.09",
+ "s2:reflectance_conversion_factor":1.02412472897181,
+ "datetime":"2023-11-23T05:50:10.118000Z",
+ "s2:sequence":"0",
+ "earthsearch:s3_path":"s3://sentinel-cogs/sentinel-s2-l2a-cogs/43/S/CR/2023/11/S2B_43SCR_20231123_0_L2A",
+ "earthsearch:payload_id":"roda-sentinel2/workflow-sentinel2-to-stac/ee5536069c6dd3a13ae2dbd530beafeb",
+ "earthsearch:boa_offset_applied":true,
+ "processing:software":{
+ "sentinel2-to-stac":"0.1.1"
+ },
+ "updated":"2023-11-23T08:25:34.597Z"
+ },
+ "geometry":{
+ "type":"Polygon",
+ "coordinates":[
+ [
+ [
+ 73.91301155493866,
+ 32.53264844646899
+ ],
+ [
+ 73.65302362425264,
+ 31.539681428386213
+ ],
+ [
+ 74.04974127686076,
+ 31.543245006457585
+ ],
+ [
+ 74.03945448697993,
+ 32.53367782512506
+ ],
+ [
+ 73.91301155493866,
+ 32.53264844646899
+ ]
+ ]
+ ]
+ },
+ "links":[
+ {
+ "rel":"self",
+ "type":"application/geo+json",
+ "href":"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2B_43SCR_20231123_0_L2A"
+ },
+ {
+ "rel":"canonical",
+ "href":"s3://sentinel-cogs/sentinel-s2-l2a-cogs/43/S/CR/2023/11/S2B_43SCR_20231123_0_L2A/S2B_43SCR_20231123_0_L2A.json",
+ "type":"application/json"
+ },
+ {
+ "rel":"license",
+ "href":"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice"
+ },
+ {
+ "rel":"derived_from",
+ "href":"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c/items/S2B_43SCR_20231123_0_L1C",
+ "type":"application/geo+json"
+ },
+ {
+ "rel":"parent",
+ "type":"application/json",
+ "href":"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a"
+ },
+ {
+ "rel":"collection",
+ "type":"application/json",
+ "href":"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a"
+ },
+ {
+ "rel":"root",
+ "type":"application/json",
+ "href":"https://earth-search.aws.element84.com/v1"
+ },
+ {
+ "rel":"thumbnail",
+ "href":"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2B_43SCR_20231123_0_L2A/thumbnail"
+ }
+ ],
+ "assets":{
+ "blue":{
+ "href":"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/43/S/CR/2023/11/S2B_43SCR_20231123_0_L2A/B02.tif",
+ "type":"image/tiff; application=geotiff; profile=cloud-optimized",
+ "title":"Blue (band 2) - 10m",
+ "eo:bands":[
+ {
+ "name":"blue",
+ "common_name":"blue",
+ "description":"Blue (band 2)",
+ "center_wavelength":0.49,
+ "full_width_half_max":0.098
+ }
+ ],
+ "gsd":10,
+ "proj:shape":[
+ 10980,
+ 10980
+ ],
+ "proj:transform":[
+ 10,
+ 0,
+ 300000,
+ 0,
+ -10,
+ 3600000
+ ],
+ "raster:bands":[
+ {
+ "nodata":0,
+ "data_type":"uint16",
+ "bits_per_sample":15,
+ "spatial_resolution":10,
+ "scale":0.0001,
+ "offset":-0.1
+ }
+ ],
+ "roles":[
+ "data",
+ "reflectance"
+ ]
+ },
+ "thumbnail":{
+ "href":"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/43/S/CR/2023/11/S2B_43SCR_20231123_0_L2A/thumbnail.jpg",
+ "type":"image/jpeg",
+ "title":"Thumbnail image",
+ "roles":[
+ "thumbnail"
+ ]
+ }
+ },
+ "bbox":[
+ 73.65302362425264,
+ 31.539681428386213,
+ 74.04974127686076,
+ 32.53367782512506
+ ],
+ "stac_extensions":[
+ "https://stac-extensions.github.io/view/v1.0.0/schema.json",
+ "https://stac-extensions.github.io/grid/v1.0.0/schema.json",
+ "https://stac-extensions.github.io/mgrs/v1.0.0/schema.json",
+ "https://stac-extensions.github.io/raster/v1.1.0/schema.json",
+ "https://stac-extensions.github.io/processing/v1.1.0/schema.json",
+ "https://stac-extensions.github.io/eo/v1.1.0/schema.json",
+ "https://stac-extensions.github.io/projection/v1.1.0/schema.json"
+ ],
+ "collection":"sentinel-2-l2a"
+}
+```
+
+
+Source URL: [https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2B_43SCR_20231123_0_L2A](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items/S2B_43SCR_20231123_0_L2A)
+
+#### STAC Catalog
+
+A STAC Catalog is an entity that logically groups other Catalogs, Collections, and Items. A Catalog contains links to these other entities and can include additional metadata to describe the entities contained therein. A catalog is usually the starting point for navigating a STAC. More specifically, a `catalog.json` file contains links to some combination of other STAC Catalogs, Collections, and/or Items. We can think of it like a directory on a computer although it doesn't necessarily need to mirror the local directory tree.
+
+There are no restrictions on the way STAC Catalogs are organized. Therefore, the combination of STAC components within a STAC Catalog is quite variable and flexible. Many implementations use a set of 'sub-catalog(s)' that group the items in some sensible way, e.g. by years as a first level and months as a second level. It can be easily extended, for example, to include additional metadata to further describe its holdings, as the STAC Collection does.
+
+#### STAC Collection
+
+A STAC Collection is similar to a STAC Catalog, but includes and partially requires additional metadata about a set of items that exist as part of the collection. It adds additional fields to enable the description of information like the spatial and temporal extent of the data, the license, keywords, providers, etc. Therefore, it can easily be extended with additional collection-level metadata that is common across all children. For example, it could summarize that all Items underneath hold data in either 10m or 30m spatial resolution.
+
+
+
+ Example of a Sentinel 2 L2A STAC Collection.
+
+```json
+{
+ "type":"Collection",
+ "id":"sentinel-2-l2a",
+ "stac_version":"1.0.0",
+ "description":"Global Sentinel-2 data from the Multispectral Instrument (MSI) onboard Sentinel-2",
+ "links":[
+ {
+ "rel":"self",
+ "type":"application/json",
+ "href":"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a"
+ },
+ {
+ "rel":"cite-as",
+ "href":"https://doi.org/10.5270/S2_-742ikth",
+ "title":"Copernicus Sentinel-2 MSI Level-2A (L2A) Bottom-of-Atmosphere Radiance"
+ },
+ {
+ "rel":"license",
+ "href":"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice",
+ "title":"proprietary"
+ },
+ {
+ "rel":"parent",
+ "type":"application/json",
+ "href":"https://earth-search.aws.element84.com/v1"
+ },
+ {
+ "rel":"root",
+ "type":"application/json",
+ "href":"https://earth-search.aws.element84.com/v1"
+ },
+ {
+ "rel":"items",
+ "type":"application/geo+json",
+ "href":"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/items"
+ },
+ {
+ "rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables",
+ "type":"application/schema+json",
+ "href":"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/queryables"
+ },
+ {
+ "rel":"aggregate",
+ "type":"application/json",
+ "href":"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/aggregate",
+ "method":"GET"
+ },
+ {
+ "rel":"aggregations",
+ "type":"application/json",
+ "href":"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a/aggregations"
+ }
+ ],
+ "stac_extensions":[
+ "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json",
+ "https://stac-extensions.github.io/view/v1.0.0/schema.json",
+ "https://stac-extensions.github.io/scientific/v1.0.0/schema.json",
+ "https://stac-extensions.github.io/raster/v1.1.0/schema.json",
+ "https://stac-extensions.github.io/eo/v1.0.0/schema.json"
+ ],
+ "title":"Sentinel-2 Level 2A",
+ "extent":{
+ "spatial":{
+ "bbox":[
+ [
+ -180,
+ -90,
+ 180,
+ 90
+ ]
+ ]
+ },
+ "temporal":{
+ "interval":[
+ [
+ "2015-06-27T10:25:31.456000Z",
+ null
+ ]
+ ]
+ }
+ },
+ "license":"proprietary",
+ "keywords":[
+ "sentinel",
+ "earth observation",
+ "esa"
+ ],
+ "providers":[
+ {
+ "name":"ESA",
+ "roles":[
+ "producer"
+ ],
+ "url":"https://earth.esa.int/web/guest/home"
+ },
+ {
+ "name":"Sinergise",
+ "roles":[
+ "processor"
+ ],
+ "url":"https://registry.opendata.aws/sentinel-2/"
+ },
+ {
+ "name":"AWS",
+ "roles":[
+ "host"
+ ],
+ "url":"http://sentinel-pds.s3-website.eu-central-1.amazonaws.com/"
+ },
+ {
+ "name":"Element 84",
+ "roles":[
+ "processor"
+ ],
+ "url":"https://element84.com"
+ }
+ ],
+ "summaries":{
+ "platform":[
+ "sentinel-2a",
+ "sentinel-2b"
+ ],
+ "constellation":[
+ "sentinel-2"
+ ],
+ "instruments":[
+ "msi"
+ ],
+ "gsd":[
+ 10,
+ 20,
+ 60
+ ],
+ "view:off_nadir":[
+ 0
+ ],
+ "sci:doi":[
+ "10.5270/s2_-znk9xsj"
+ ],
+ "eo:bands":[
+ {
+ "name":"blue",
+ "common_name":"blue",
+ "description":"Blue (band 2)",
+ "center_wavelength":0.49,
+ "full_width_half_max":0.098
+ }
+ ]
+ }
+}
+```
+
+
+Source URL: [https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a](https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a)
+
+#### STAC API
+
+STAC API is a dynamic version of a static SpatioTemporal Asset Catalog and provides a RESTful endpoint that enables the search of STAC Items and STAC Collections. STAC Catalogs don't play a big role in APIs as they are mostly used as an entity for grouping larger static catalogs into smaller chunks, which is usually not needed in the context of a dynamic API.
+
+If the API implements the Filter or Query extension, additionally the user is allowed to search for specific content based on a set of available metadata fields. Additional extensions may support more interactive elements such as aggregations, or managing the metadata (updating it, creating new entities, or deleting some) through transactions.
+
+A part of the STAC API is built on top of [OGC API - Features](#ogc-api---features).
+
+
+#### STAC Extension
+
+Extensions to STAC are split into two parts: STAC extensions and STAC API extensions. They are both an important addition to the STAC specifications and can provide either additions to the data model (i.e. additional JSON properties such as `eo:cloud_cover`) or behavioural changes (e.g. additional types of links or a sorting functionality). Most tend to be about describing a particular domain or type of data.
+
+To find out which extensions do the STAC API, STAC Catalog, Collection or Item object implement, you can explore [list of STAC extensions](https://stac-extensions.github.io/#list-of-stac-extensions) or [list of STAC API extensions](https://stac-api-extensions.github.io/#list-of-stac-api-extensions).
+
+
+## ODAta
+
+OData (Open Data Protocol) specifies a variety of best practices for creating and using REST APIs that can be handled by a large set of client tools like common web browsers, download-managers. The OData protocol can be used for building URI for performing search queries and product downloads for example on the Copernicus Dataspace.
+
+
+### References and further readings
+
+- [OpenSearch specification](https://github.com/dewitt/opensearch)
+- [OGC OpenSearch Geo, Time and EO extensions](https://www.ogc.org/standard/opensearch)
+- [OpenSearch API access examples](https://documentation.dataspace.copernicus.eu/APIs/OpenSearch.html)
+- [OGC CSW specification](https://www.ogc.org/standard/cat/)
+- [OGC API - Features](https://www.ogc.org/standard/ogcapi-features/)
+- [OGC API - Records (Draft)](https://docs.ogc.org/DRAFTS/20-004.html)
+- More to read about [STAC specification](https://stacspec.org/en)
+- [STAC Tutorials](https://stacspec.org/en/tutorials/)
+- [OData on Copernicus Dataspace](https://documentation.dataspace.copernicus.eu/APIs/OData.html)
+
+## Where to search for data
+
+Earth observation data access is not limited to a single platform or a single entry point. What follows is a non-exhaustive list of some well-known Earth observation data catalogues usually based around the original agency providing the data:
+
+- [EarthData Search](https://search.earthdata.nasa.gov/search): A comprehensive data discovery and access tool provided by NASA's Earth Observing System Data and Information System (EOSDIS). Contains wide range of NASA's Earth science data.
+- [Copernicus Data Space](https://dataspace.copernicus.eu/explore-data/data-collections): Currently in development platform aiming to provide immediate access to large amounts of open and free Earth observation data and scalable interfaces including both new and historical Sentinel images, commercial datasets, as well as Copernicus Contributing Missions.
+- [USGS Earth Explorer](https://earthexplorer.usgs.gov/): EarthExplorer (EE) provides online search, browse display, metadata export, and data download for earth science data from the archives of the U.S. Geological Survey (USGS). Usually of largest demand are data from the Landsat missions.
+- [Open Science Catalog](https://opensciencedata.esa.int/): A catalog of publicly available geoscience products, datasets and resources developed in the frame of scientific research Projects funded by ESA EO. Queriable by themes, projects, variables and products.
+- [NCEI Catalog](https://www.ncei.noaa.gov/access): NOAA's National Centers for Environmental Information (NCEI) provides access to various environmental data, including satellite imagery, climate data, and other geospatial datasets. In particular it is composed of oceanic, atmospheric, and geophysical data.
+- [STAC Index](https://stacindex.org/catalogs): A list of publicly available STAC APIs and Static Catalogs.
+
+
+
+
+
+
+## How to search for data
+
+Large EO data portals usually allow two main different ways of access based on the technical proficiency of the target user group. User can either use graphical user interface of selected portal or use one the available APIs for search. Bellow are examples of way how to search for your data.
+
+### Web Browser Catalogue client
+
+Many large portals provide a easy-to-use search and filtering GUI, which under the hood uses one of the provided catalog APIs. This allows less experienced users to perform queries usually for smaller subset of data with the help of an attached Web map client to orient themselves easily.
+
+
+
+### API access
+
+For batch operations or script access to the catalogs, it is envisioned that direct approach of the API is performed instead. This removes the need for direct user interaction to the web platform and can be used from user provided scripts or other programs and CLI tools.
+
+Fore more information about performing filtering or queries, see [Data Properties](../2.2_data_properties/2.2_properties.md).
+
+### STAC Browser
+
+In order to access and browse any online STAC catalog or API, a rich web client application STAC Browser can be used on [radiantearth.github.io/stac-browser](https://radiantearth.github.io/stac-browser). It does does allow a wide variety of filtering capabilities.
+
+### STAC QGIS plugin
+
+[QGIS STAC API Browser](https://stac-utils.github.io/qgis-stac-plugin/) provides a simple and user-friendly approach in searching and using STAC API resources in QGIS. It offers a comfortable way to filter and browse the STAC API items and ability to add the STAC API assets as map layers into the QGIS.
+
+### QGIS MetaSearch Catalog Client
+
+[MetaSearch](https://docs.qgis.org/3.28/en/docs/user_manual/plugins/core_plugins/plugins_metasearch.html) is a QGIS plugin to interact with metadata catalog services, supporting both the OGC API - Records and OGC Catalog Service for the Web (CSW) standards.
+
+### OWSLib
+
+[OWSLib](https://owslib.readthedocs.io/en/latest/) is a Python package for client programming with Open Geospatial Consortium (OGC) web service (hence OWS) interface standards, and their related content models. The OWSLib client offers support for OGC CSW, OGC API - Records and OpenSearch based catalog services.
+
+### PySTAC client
+
+The [STAC Python Client (pystac_client)](https://pystac-client.readthedocs.io/en/stable/) is a Python package for working with STAC Catalogs and APIs that conform to the STAC and STAC API specs in a seamless way. PySTAC Client builds upon PySTAC through higher-level functionality and ability to leverage STAC API search endpoints.
+
+[](assets/stac_leafmap.mp4)
+
+> Video produced by Qiusheng Wu (https://github.com/opengeos/leafmap/pull/347#issuecomment-1402560159)
+
+
+### Further Reading
+- [STAC tutorials](https://stacspec.org/en/tutorials/)
+- [Echoes in space course](https://eo-college.org/courses/echoes-in-space/)
+
+
+### References
+- [STAC](https://stacspec.org/en)
+- [OWSLib](https://owslib.readthedocs.io/en/latest/)
+- [STAC Python Client (pystac_client)](https://pystac-client.readthedocs.io/en/stable/)
+- [radiantearth.github.io/stac-browser](https://radiantearth.github.io/stac-browser)
+- [OpenSearch specification](https://github.com/dewitt/opensearch)
+
+## Quiz
+Let's test your understanding of data types and where to find them. We will be working with the actual data shortly in the next chapters!
+
+### STAC
+What does the STAC stand for:
+
+ [( )] Spatio and Temporal Asset Certification
+ [(x)] SpatioTemporal Asset Catalog
+ [( )] SpatioTemporal Asset Collections
+
+In which line are only mandatory properties of STAC item?
+
+ [(x)] type, id, assets, properties
+ [( )] id, assets, collection, stac_version
+ [( )] type, id, stac_version, stac_extensions
+
+
+### Data Catalogs and Access Protocols
+
+Which one of the listed data access API's can you use on Copernicus Dataspace? See [list of API's available](https://dataspace.copernicus.eu/analyse/apis)
+
+ [(x)] OData
+ [(x)] STAC API
+ [(x)] OGC API
+ [( )] AstraDataServe
+ [( )] OrbitQueryHub
+
+
+Use an online catalog browser of [Copernicus Dataspace](https://dataspace.copernicus.eu/browser/) to search for Sentinel 2 L2A products over the island of Sardinia between 1.9.2023 and 30.9.2023 with cloud coverage less than or equal to 20% and get the number of returned products. Use enclosed [geometry file](assets/sardinia.geojson) and upload it to the browser as shown on image below.
+
+
+
+ [( )] 101-200
+ [( )] 201-300
+ [(x)] 0-100
diff --git a/_sources/2.2_data_properties/2.2_data_properties.md b/_sources/2.2_data_properties/2.2_data_properties.md
new file mode 100644
index 0000000..fe4819e
--- /dev/null
+++ b/_sources/2.2_data_properties/2.2_data_properties.md
@@ -0,0 +1,127 @@
+# Data Properties
+
+## Learning objectives
+
+- Learn what are metadata and why they are important
+- Discover data properties and how to filter by them
+
+## What are data properties and metadata
+
+Data properties and metadata are sets of characteristics of geospatial data that serve different purposes. Commonly data properties are seen as attributes describing the image itself whereas, on the other hand, metadata describes the properties of a product.
+
+**Data Properties:** Data properties refer to the characteristics and attributes of the geospatial data itself. These properties describe the actual content, structure, and values within the dataset. For raster data, properties can include information such as pixel values, cell size, spatial resolution, number of bands, and data format. For vector data, properties may include attributes associated with points, lines, or polygons, such as feature identifiers, names, classifications, or numerical values. Data properties are essential for understanding the data at a fundamental level and are used in the analysis, visualization, and processing tasks.
+
+**Metadata:** Metadata, on the other hand, provides descriptive information about the data product. It serves as documentation that complements the data properties and offers additional context and details about the data. Metadata describes the origin, and characteristics, and helps with usage of the data, allowing users to discover, evaluate, and effectively utilize the data. It typically includes information such as the data source, acquisition parameters, coordinate reference system, temporal coverage, quality measures, and data access policies. Metadata plays a crucial role in data management, data sharing, and data interoperability by providing the necessary documentation and context for understanding and utilizing the data.
+
+Both together provide a complete description of the data of our interest itself and how to work with them. Standardized ways how to describe metadata and data properties allow geospatial systems to work with data easily.
+
+
+> Figure: STAC standardizing Metadata.
+
+## Properties and metadata used for filtering
+
+In [previous lesson](../2.1_data_discovery/2.1_data_discovery.md) we learned where to find data. Now it is time to look at how we can select only the data we want. Properties and metadata are a great help when we are not interested in the whole collection, but only in certain regions, times or even bands of selected satellite products.
+
+In (many of) the data catalogs, we can filter by specific values for each satellite. Let's talk more about some of them shortly
+
+- **Dataset name/identifier:** Filtering directly by name of a product as a unique name or identifier is assigned to the dataset, allowing it to be easily identified and referenced.
+- **Time range:** The temporal coverage or specific dates associated with the data acquisition. By this, we can easily select products from the same location with different dates of acquisition. This is particularly relevant for time-series or multi-temporal datasets.
+- **Bounding box or other area of interest:** Spatial Extent or the geographic coverage of the raster data, typically defined by the bounding coordinates (longitude and latitude) that encompass the dataset. Usually you can select your own area or interest as a rectangle/geometry or use a map window for taking the current extent
+- **Mission:** You can select by satellite used for data acquisition. In the case of Sentinel missions, you can select only Sentinel-1 as an example
+- **Processing levels:** Typically you can also select by processing level you are interested in for your missions. Typically Level-0 means unprocessed, raw data, and with higher number represents more corrections were applied.
+- **Sensors or instrument:** Selection by the output of a specific sensor or instrument.
+- **Cloud coverage/polarization**: Based on a mission, a selection filter can be made by specific parameters. For multi-spectral data such as captured by Sentinel-2, we can typically filter by cloud coverage in percentage. This is used as we are interested in lower cloud coverage percent for analysis. Similarly, specific polarizations can be selected for SAR data such as captured by Sentinel-1.
+- **Orbit number:** Typically integer number selects a specific orbit number user is interested in.
+- **Orbit direction:** For the majority of data types the selection is either ascending or descending.
+- **Availability status/Timeliness:** Some missions have different time availability for their data allowing them to select Near Real-Time acquisitions (approximately 3 hours after acquisition), raw data with a certain delay, or processed data later. It is also common that infrequently accessed data or older data than a certain threshold (years) can only be accessed 'on demand'. Their 'Availability status' is usually set as 'Archived' or similar and they but must be 'tasked to be brought online from archive storage' by the user. This operation usually takes minutes to hours to complete. The user of the platform can later access the catalog again and hopefully the product have been brought online in the meantime.
+
+In the examples above it also became more clear why a standardized way of expressing the metadata is important. For example, how do you know whether the cloud cover is a percentage expressed in a range from 0 to 1 or from 0 to 100? How would you filter on this property, if both scales would be mixed?
+
+
+
+
+
+[](https://www.youtube.com/watch?v=r2qSTdW-_Wk)
+> Video content in collaboration with [Matthias Mohr](https://mohr.ws/) (Major STAC contributor).
+> *"Having metadata in a standardized format makes your data available to others in a simple and unified way. Similarly, you can find and work with the data of others more easily. You can achieve these benefits with STAC."*
+
+### Metadata and properties which are not typically used for filtering
+
+Many metadata are contained within the product but are not used for filtering on the platforms or accessing hubs directly. Among these is for example Author of the dataset or Licence. You should be able to get all information about those in data metadata when you are accessing the data itself or on the general page with information.
+
+Usually it is unfortunatelly not possible to filter by or search by direct data properties, e.g. by values in data.
+
+
+### Dimensions
+
+Dimensions are important description of data and their properties. More information about dimensions of data and datacubes was covered in the lecture about [Datacubes](../1.2_data_cube/1.2_data_cube.md)
+
+- x, y and sometimes z - Spatial dimension of data
+- temporal/time dimension - capturing the time aspect for time series analysis
+
+### Value Types (data types)
+
+Once we have selected data products we are interested in, we can look directly into values to select what we are interested in. Common data types representing measured values are these:
+
+- bitmask 0/1
+- 8bit 0-255
+- UInt16 - 0-65k
+- Int16 - -32k - 32k
+- Float32
+
+
+### Further Reading
+- [Standartized data management with STAC](https://space.cloud68.co/s/xExLwCmzzKEcoB9?dir=undefined&path=%2FLumbardhi%2F28.06.2023&openfile=1351852)
+- [STAC sessions from FOSS4G 2023](https://space.cloud68.co/s/xExLwCmzzKEcoB9?dir=undefined&path=%2FLumbardhi%2F28.06.2023&openfile=1351852)
+
+### References
+
+## Quiz
+
+Which of the following is not considered as Data Metadata
+
+ [( )] Instrument
+ [( )] Licence
+ [(x)] Values from sensors
+
+Which of the following is not considered as Data Properties
+
+ [(x)] Licence
+ [( )] Pixel values
+ [( )] Number of bands
+
+Which of these properties and metadata are commonly used for filtering when searching data?
+
+ [(x)] Bounding Box
+ [( )] E-Mail Adress
+ [(x)] Time Range
+ [(x)] Sensor
+ [( )] Telephone Number
+ [(x)] Cloud Coverage
+
+Which statements about the advantage of filtering data by metadata and properties are true?
+
+ [( )] All images are considered for subsequent analysis.
+ [(x)] Only images that are relevant are considered for subsequent analysis.
+ [(x)] The amount of data is reduced before starting the actual analysis.
+
+
+**Removed until exercise is accessible:** Is the same number of products findable on all plafforms and in all ways?
+In the last chapter, there was exercise about finding Sentinel 2 L2A products over the island of Sardinia between 1.9.2023 and 30.9.2023 with cloud coverage less than or equal to 20% on Copernicus Dataspace via Web Browser. Correct answer was 59. Is the same number returned by programatically accessing catalog with code?
+
+ [(x)] Yes
+ [( )] No
+
+**Removed until exercise is accessible:** Following the exercise with gdalinfo, answer following questions about the raster used there:
+
+What is the NODATA value?
+
+ [[0]]
+
+**Removed until exercise is accessible:** What are the coordinates of upper left corner in degrees?
+
+ [[72d52'14.58"E, 32d31' 9.58"N]]
+
+**Removed until exercise is accessible:** What is the EPSG code of the raster?
+
+ [[4326]]
diff --git a/_sources/2.3_data_access/2.3_data_access.md b/_sources/2.3_data_access/2.3_data_access.md
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+# Access EO Data from the Cloud
+
+Using a cloud provider for accessing data, and in this specific scenario Earth Observation data, could improve your productivity a lot. To get the most out of it, we will provide you some important insights.
+
+## Learning objectives
+
+- In this lecture you will learn the usage and peculiarities of the main data operators commonly available on cloud platforms like:
+ - Data loading
+ - Filter
+ - Apply
+ - Reduce
+ - Resample
+ - Aggregate
+- Finally, you will be able to create a simple workflow with openEO.
+
+The exercise will use the openEO Python Client Side Processing functionality, which allows to experiment using openEO without a connection to an openEO back-end.
+
+## Raster Data Loading
+
+In openEO, your process graph will always start with defining the data you want to load, which can be done mainly using the following processes:
+
+1. `load_collection`: loads a collection from the current back-end by its id and returns it as a processable data cube. The data that is added to the data cube can be restricted with the parameters spatial_extent, temporal_extent, bands and properties.
+2. `load_stac`: loads data from a static STAC catalog or a STAC API Collection and returns the data as a processable data cube. A batch job result can be loaded by providing a reference to it. If supported by the underlying metadata and file format, the data that is added to the data cube can be restricted with the parameters spatial_extent, temporal_extent and bands.
+
+[Exercise 2.3 Data Access Lazy Loading](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/2.3_data_access/exercises/23_data_access_lazy_loading.ipynb)
+
+### References
+
+- [openEO Python Client Processing documentation](https://open-eo.github.io/openeo-python-client/cookbook/localprocessing.html)
+- [load_collection process definition](https://processes.openeo.org/#load_collection)
+- [load_stac process definition](https://processes.openeo.org/#load_stac)
+
+## Processes on Datacubes
+
+In the following part, the basic processes for manipulating datacubes are introduced.
+
+### Filter
+
+When filtering data (e.g. [`filter_spatial`](https://processes.openeo.org/#filter_spatial), [`filter_temporal`](https://processes.openeo.org/#filter_temporal), [`filter_bands`](https://processes.openeo.org/#filter_bands)), only the data that satisfies a condition is returned. For example, this condition could be a timestamp or interval, (a set of) coordinates, or specific bands. By applying filtering the datacube becomes smaller, according to the selected data.
+
+> :warning: Simplified
+`filter([🌽, 🥔, 🐷], isVegetarian) => [🌽, 🥔]`
+>
+
+In the image, the example datacube can be seen at the top with labeled dimensions. The filtering techniques are displayed separately below. On the left, the datacube is filtered temporally with the interval `["2020-10-15", "2020-10-27"]`. The result is a cube with only the rasters for the timestep that lies within that interval (`"2020-10-25"`) and unchanged bands and spatial dimensions. Likewise, the original cube is filtered for a specific band `["nir"]` in the middle and a specific spatial region `[Polygon(...)]` on the right.
+
+
+
+> Figure: Datacube filtering: From the datacube 4 by 3 grid, arrows depict what happens if the grid is filtered. Temporal filtering results in data for one timestep with all four bands, filtering bands results in data with one band with all three timesteps, and spatial filtering results in all timesteps and bands being preserved, but all with a smaller area. Reference: [openeo.org (2022). Processes on Datacubes.](https://openeo.org/documentation/1.0/datacubes.html#processes-on-datacubes)
+
+[Exercise 2.3 Data Access Filter](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/2.3_data_access/exercises/23_data_access_filter.ipynb)
+
+### Apply
+
+The `apply*` functions (e.g. [`apply`](https://processes.openeo.org/#apply), [`apply_neighborhood`](https://processes.openeo.org/#apply_neighborhood), [`apply_dimension`](https://processes.openeo.org/#apply_dimension)) employ a process on the datacube that calculates new pixel values for each pixel, based on `n` other pixels. Please note that several programming languages use the name `map` instead of `apply`, but they describe the same type of function.
+
+> :warning: Simplified
+`apply([🌽, 🥔, 🐷], cook) => [🍿, 🍟, 🍖]`
+>
+
+For the case `n = 1` this is called a unary function and means that only the pixel itself is considered when calculating the new pixel value. A prominent example is the `absolute()` function, calculating the absolute value of the input pixel value.
+
+
+
+> Figure: Datacube apply unary: 3 example tiles hold values below and above 0. after applying the process 'absolute', all values in the three example tiles have changed to their absolute values above 0. Reference: [openeo.org (2022). Processes on Datacubes.](https://openeo.org/documentation/1.0/datacubes.html#processes-on-datacubes)
+
+
+If `n` is larger than 1, the function is called n-ary. In practice, this means that the pixel neighbourhood is taken into account to calculate the new pixel value. Such neighbourhoods can be of spatial and/or temporal nature. A spatial function works on a kernel that weights the surrounding pixels (e.g. smoothing values with nearby observations), a temporal function works on a time series at a certain pixel location (e.g. smoothing values over time). Combinations of types to n-dimensional neighbourhoods are also possible.
+
+In the example below, an example weighted kernel (shown in the middle) is applied to the cube (via [`apply_kernel`](https://processes.openeo.org/#apply_kernel)). To avoid edge effects (affecting pixels on the edge of the image with less neighbours), a padding has been added in the background.
+
+
+
+> Figure: Datacube apply spatial kernel: Three example tiles hold some values with a lot of variance. A spatial kernel (a cell plus it's 4 direct neighbours) is applied to all pixels, and the result appears to be spatially smoothed, with less variance. Reference: [openeo.org (2022). Processes on Datacubes.](https://openeo.org/documentation/1.0/datacubes.html#processes-on-datacubes)
+
+Of course this also works for temporal neighbourhoods (timeseries), considering neighbours before and after a pixel. To be able to show the effect, two timesteps were added in this example figure. A moving average of window size 3 is then applied, meaning that for each pixel the average is calculated out of the previous, the next, and the timestep in question (tn-1, tn and tn+1). No padding was added which is why we observe edge effects (NA values are returned for t1 and t5, because their temporal neighbourhood is missing input timesteps).
+
+
+
+> Figure: Datacube apply temporal moving average: Smoothing is applied to 5 example tiles by calculating the mean of 3 timesteps of every single pixel. The resulting tiles for the timestamps look much more alike. Reference: [openeo.org (2022). Processes on Datacubes.](https://openeo.org/documentation/1.0/datacubes.html#processes-on-datacubes)
+
+Alternatively, a process can also be applied along a dimension of the datacube, meaning the input is no longer a neighbourhood of some sort but all pixels along that dimension (`n` equals the complete dimension). If a process is applied along the `time` dimension (e.g. a breakpoint detection), the complete pixel timeseries are the input. If a process is applied along the `spatial` dimensions (e.g. a `mean`), all pixels of an image are the input. The process is then applied to all pixels along that dimension and the dimension continues to exist. This is in contrast to [reduce](#reduce), which drops the specified dimension of the data cube. In the image below, a `mean` is applied to the `time` dimension. An example pixel timeseries is highlighted by a green line and processed step-by-step.
+
+
+
+> Figure: Datacube apply dimension time: The mean of all 5 timesteps is calculated for every single pixel. The resulting 5 tiles look exaclty the same, as they have been averaged. Reference: [openeo.org (2022). Processes on Datacubes.](https://openeo.org/documentation/1.0/datacubes.html#processes-on-datacubes)
+
+[Exercise 2.3 Data Access Apply](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/2.3_data_access/exercises/23_data_access_apply.ipynb)
+
+### Reduce
+
+The [`reduce_dimension`](https://processes.openeo.org/#reduce_dimension) process _collapses_ a whole dimension of the datacube. It does so by using some sort of **reducer**, which is a function that calculates a single result from an amount of values, as e.g. `mean()`, `min()` and `max()` are. For example we can reduce the time dimension (`t`) of a timeseries by calculating the mean value of all timesteps for each pixel. We are left with a cube that has no time dimension, because all values of that dimension are compressed into a single mean value. The same goes for e.g. the spatial dimensions: If we calculate the mean along the `x` and `y` dimensions, we are left without any spatial dimensions, but a mean value for each instance that previously was a raster is returned. In the image below, the dimensions that are reduced are crossed out in the result.
+
+> :warning: Simplified
+`reduce([🥬, 🥒, 🍅, 🧅], prepare) => 🥗`
+>
+
+Think of it as a waste press that does math instead of using brute force. Given a representation of our example datacube, let's see how it is affected.
+
+
+
+> Figure: Datacube reduce: Three arrows depict what happens to the 12 example tiles, if they are reduced: Reducing timesteps leads to four tiles (one for each band), and the time dimension is deleted. Reducing bands lead to one tile per timestep, and the bands dimension is deleted. Reducing spatially leads to the original 4 by 3 bands by time layout, but the result has no spatial dimension and thus, the tiles have been turned into single values, per tile. Reference: [openeo.org (2022). Processes on Datacubes.](https://openeo.org/documentation/1.0/datacubes.html#processes-on-datacubes)
+
+[Exercise 2.3 Data Access Reduce](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/2.3_data_access/exercises/23_data_access_reduce.ipynb)
+
+### Resample
+
+In a resampling processes (e.g. [`resample_cube_spatial`](https://processes.openeo.org/#resample_cube_spatial), [`resample_cube_temporal`](https://processes.openeo.org/#resample_cube_temporal)), the _layout_ of a certain dimension is changed into another _layout_, most likely also changing the resolution of that dimension. This is done by mapping values of the source (old) datacube to the new layout of the target (new) datacube. During that process, resolutions can be _upscaled_ or _downscaled_ (also called _upsampling_ and _downsampling_), depending on whether they have a finer or a coarser spacing afterwards. A function is then needed to translate the existing data into the new resolution. A prominent example is to reproject a datacube into the coordinate reference system of another datacube, for example in order to merge the two cubes.
+
+> :warning: Simplified
+`resample(🖼️, downscale) => 🟦`
+
+`resample(🌍, reproject) => 🗺️`
+>
+
+The first figure gives an overview of temporal resampling. How exactly the input timesteps are rescaled to the output timesteps depends on the resampling function.
+
+
+
+> Figure: Datacube temporal resampling (up and down): Downsampling: To a timeline-representation of the example tiles, another timeline with only 2 steps at different dates is applied. The result has tiles only at those new timesteps. In Upsampling, the existing 3 timesteps are sampled into 5 result timesteps. Reference: [openeo.org (2022). Processes on Datacubes.](https://openeo.org/documentation/1.0/datacubes.html#processes-on-datacubes)
+
+The second figure displays spatial resampling. Observe how in the upsampling process, the output datacube has not gained in information value. The resulting grid still carries the same pixel information, but in higher spatial resolution. Other upsampling methods may yield smoother results, e.g. by using interpolation.
+
+
+
+> Figure: Datacube spatial resampling (up and down): Downsampling: The resulting tiles have a lower spatial resolution than the input tiles. Upsampling: The resulting tiles have a higher spatial resolution than the input tiles, but contain the same image than before (no information added). Reference: [openeo.org (2022). Processes on Datacubes.](https://openeo.org/documentation/1.0/datacubes.html#processes-on-datacubes)
+
+[Exercise 2.3 Data Access Resample](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/2.3_data_access/exercises/23_data_access_resample.ipynb)
+
+### Aggregate
+
+An aggregation of a datacube can be thought of as a grouped reduce. That means it consists of two steps:
+
+1. Grouping via a grouping variable, i.e. spatial geometries or temporal intervals
+2. Reducing these groups along the grouped dimension with a certain reducer function, e.g. calculating the mean pixel value per polygon or the maximum pixel values per month
+
+While the layout of the reduced dimension is changed, other dimensions keep their resolution and geometry. But in contrast to pure `reduce`, the dimensions along which the reducer function is applied still exist after the operation.
+
+> :warning: Simplified
+`aggregate(👪 👩👦 👨👩👦👦, countFamilyMembers) => [3️⃣, 2️⃣, 4️⃣]`
+>
+
+A temporal aggregation (e.g. [`aggregate_temporal`](https://processes.openeo.org/#aggregate_temporal)) is similar to the downsampling process, as it can be seen in the according image above. Intervals for grouping can either be set manually, or periods can be chosen: monthly, yearly, etc. All timesteps in an interval are then collapsed via a reducer function (`mean`, `max`, etc.) and assigned to the given new labels.
+
+A spatial aggregation (e.g. [`aggregate_spatial`](https://processes.openeo.org/#aggregate_spatial)) works in a similar manner. Polygons, lines and points can be selected for grouping. Their spatial dimension is then reduced by a given process and thus, a vector cube is returned. The vector cube then has dimensions containing features, attributes and time. In the graphic below, the grouping is only shown for the first timestep.
+
+
+
+> Figure: Datacube spatial aggregation: A line and a polygon are selected from the original example tiles. The pixels covered by these geometries are aggregated and the result consists no longer of imagery tiles but of an array with values for 2 geometries by 4 bands by 3 timesteps. Reference: [openeo.org (2022). Processes on Datacubes.](https://openeo.org/documentation/1.0/datacubes.html#processes-on-datacubes)
+
+[Exercise 2.3 Data Access Aggregate](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/2.3_data_access/exercises/23_data_access_aggregate.ipynb)
+
+### Recap Processes
+Here are some exercises to recap the different processes that can be used on a data cube.
+
+
+
+
+
+
+
+
+
+
+
+
+
+### References
+- [openeo.org (2022). Processes on Datacubes.](https://openeo.org/documentation/1.0/datacubes.html#processes-on-datacubes)
+
+## openEO - A standardized API for EO cloud processing
+
+### The need for a standarized API in EO cloud processing
+
+Earth Observation data are becoming too large to be downloaded locally for analysis. Also, the way they are organised (as tiles, or granules: files containing the imagery for a small part of the Earth and a single observation date) makes it unnecessary complicated to analyse them. The solution to this is to store these data in the cloud, on compute back-ends, process them there, and browse the results or download resulting figures or numbers. But how do we do that? openEO develops an open application programming interface (API) that connects clients like R, Python and JavaScript to big Earth observation cloud back-ends in a simple and unified way.
+With such an API,
+
+- each client can work with every back-end, and
+- it becomes possible to compare back-ends in terms of capacity, cost, and results (validation, reproducibility)
+
+### What does openEO stand for?
+
+The acronym openEO contracts two concepts:
+
+open: used here in the context of open source software; open source software is available in source code form, and can be freely modified and redistributed; the openEO project will create open source software, reusable under a liberal open source license (Apache 2.0)
+EO: Earth observation
+Jointly, the openEO targets the processing and analysis of Earth observation data. The main objectives of the project are the following concepts:
+
+- Simplicity: nowadays, many end-users use Python or R to analyse data and JavaScript to develop web applications; analysing large amounts of EO imagery should be equally simple, and seamlessly integrate with existing workflows
+- Unification: current EO cloud back-ends all have a different API (opens new window), making EO data analysis hard to validate and reproduce and back-ends difficult to compare in terms of capability and costs, or to combine them in a joint analysis across back-ends. A unified API can resolve many of these problems.
+
+### Why an API?
+
+An API is an application programming interface. It defines a language that two computers (a client and a server) use to communicate.
+The following figure shows how many interfaces are needed to be able to compare back-ends from different clients, without an openEO API. And if you use the slider you will see how the situation becomes much clearer with a standardized API.
+
+
+
+
+[](https://youtu.be/iBRL3yVmA2M)
+> Video content in collaboration with [Edzer Pebesma](https://www.uni-muenster.de/Geoinformatics/institute/staff/index.php/119/edzer_pebesma) (University of Münster).
+> *"For analysing Earth Observation data, don't use a platform that locks you in."*
+
+### References
+- [openeo.org - About OpenEO](https://openeo.org/about.html#openeo)
+- [Edzer Pebesma, Wolfgang Wagner, Jan Verbesselt, Erwin Goor, Christian Briese, Markus Neteler (2016). OpenEO: a GDAL for Earth Observation Analytics. https://r-spatial.org/2016/11/29/openeo.html](https://r-spatial.org/2016/11/29/openeo.html)
+- [openeo.org - Project Deliverable 22](https://github.com/Open-EO/openeo-D22/tree/master)
+
+# Quiz
+
+Loading: What is the difference between using an openEO remote back-end and openEO Python Client-Side-Processing? Tick what is correct. *Answer in exercise: 23_data_access_lazy_loading.ipynb*
+
+ [[ ]] You need an account to authenticate in both cases
+ [[x]] You don't need an account with Client-Side Processing
+ [[x]] Client-side processing does not interact with an openEO back-end
+
+Loading: What are the dimension names of the loaded datacube? *Answer in exercise: 23_data_access_lazy_loading.ipynb*
+
+ [( )] northing, easting, time, spectral
+ [( )] lat, lon, t, bands
+ [(x)] time, band, y, x
+
+Filtering: How many time steps are left after filtering temporally 2022-05-10, 2022-06-30? *Answer in exercise: 23_data_access_filter.ipynb*
+
+ [( )] 11
+ [(x)] 9
+ [( )] 32
+
+Filtering: How many pixels are left along `y` after using `filter_bbox` with `spatial_extent = {"west": 11.259613, "east": 11.406212, "south": 46.461019, "north": 46.522237}`
+? *Answer in exercise: 23_data_access_filter.ipynb*
+
+ [( )] 1006
+ [( )] 1145
+ [(x)] 489
+
+Reducing Dimension: What would be a use case for reducing the time dimension? Tick what is correct.
+
+ [[ ]] Get a full time series graph
+ [[x]] Getting a cloudfree image for a certain time range
+ [[x]] Get information about a whole sesaon
+ [[ ]] Filling gaps in a time series
+
+Reducing Bands: How many pixels are left in the datacube after we use `reduce_dimension` over band? *Answer in exercise: 23_data_access_reduce.ipynb*
+
+ [[21226010]]
+
+Reducing Dimension: What are the dimension names after running `reduce_spatial`? *Answer in exercise: 23_data_access_reduce.ipynb*
+
+ [( )] x, y, band
+ [( )] time, x, y
+ [(x)] time, band
diff --git a/_sources/2.3_data_access/exercises/23_data_access_aggregate.ipynb b/_sources/2.3_data_access/exercises/23_data_access_aggregate.ipynb
new file mode 100644
index 0000000..5ccaa6e
--- /dev/null
+++ b/_sources/2.3_data_access/exercises/23_data_access_aggregate.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5f4a0cab-e26e-4942-96ee-78de70890ad9",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "43f2c4b3-88f7-4ebf-8248-a72728d14012",
+ "metadata": {},
+ "source": [
+ "# 2.3 Data Access and Basic Processing\n",
+ "\n",
+ "## Aggregate Operators"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5691278c-4d66-4ee2-8592-75431cbff3c7",
+ "metadata": {},
+ "source": [
+ "### `aggregate_temporal_period`: temporal aggregation with predefined intervals"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40dbc4e1-929a-4256-9e62-605d5a424fca",
+ "metadata": {},
+ "source": [
+ "Start importing the necessary libraries and initialize a local connection for Client-Side Processing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4eca5284-2873-4b5c-9505-d67545fdec05",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import openeo\n",
+ "from openeo.local import LocalConnection\n",
+ "local_conn = LocalConnection('')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0420def5-ebd5-446e-a6d0-799e3534e5d2",
+ "metadata": {},
+ "source": [
+ "Create the starting Sentinel-2 datacube:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "053869f1-540f-48c1-b3ab-113d5be08bc6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "url = \"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a\"\n",
+ "\n",
+ "spatial_extent = {\"west\": 11.4, \"east\": 11.42, \"south\": 45.5, \"north\": 45.52}\n",
+ "temporal_extent = [\"2022-01-01\", \"2022-12-31\"]\n",
+ "bands = [\"red\",\"green\",\"blue\"]\n",
+ "\n",
+ "s2_cube = local_conn.load_stac(url=url,\n",
+ " spatial_extent=spatial_extent,\n",
+ " temporal_extent=temporal_extent,\n",
+ " bands=bands\n",
+ ")\n",
+ "s2_cube.execute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49a04782-d412-4229-aa4b-d8e0283d5feb",
+ "metadata": {},
+ "source": [
+ "We might be interested in aggregating our data over periods like week, month, year etc., defining what operation to use to combine the data available in the chosen period.\n",
+ "\n",
+ "Using `aggregate_temporal_period` we can achieve this easily:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b652338d-5f31-4d33-9d46-b7ea76f1d23d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "s2_monthly_min = s2_cube.aggregate_temporal_period(period=\"month\",reducer=\"min\")\n",
+ "s2_monthly_min"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "420f8621-fb50-4fc1-89a2-4dfc7ac0beea",
+ "metadata": {},
+ "source": [
+ "Check what happens to the datacube inspecting the resulting xArray object. Now the `time` dimension has 12 labels, one for each month."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f38e5288-7b95-4693-a912-b51c31a60e16",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "s2_monthly_min.execute()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/2.3_data_access/exercises/23_data_access_apply.ipynb b/_sources/2.3_data_access/exercises/23_data_access_apply.ipynb
new file mode 100644
index 0000000..1d6ba29
--- /dev/null
+++ b/_sources/2.3_data_access/exercises/23_data_access_apply.ipynb
@@ -0,0 +1,803 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5f4a0cab-e26e-4942-96ee-78de70890ad9",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "43f2c4b3-88f7-4ebf-8248-a72728d14012",
+ "metadata": {},
+ "source": [
+ "# 2.3 Data Access and Basic Processing\n",
+ "\n",
+ "## Apply Operator\n",
+ "\n",
+ "The apply operator employ a process on the datacube that calculates new pixel values for each pixel, based on n other pixels.\n",
+ "\n",
+ "Let's start again with the same sample data from the Sentinel-2 STAC Collection, applying the filters directly in the `load_stac` call, to reduce the amount of data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "b5c18492-e3c7-4a05-a5b6-6c606b8253c1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Did not load machine learning processes due to missing dependencies: Install them like this: `pip install openeo-processes-dask[implementations, ml]`\n",
+ "/home/749c4c89-469c-4d4a-9ce4-9feffea0504a/.local/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
+ " times = pd.to_datetime(\n",
+ "/home/749c4c89-469c-4d4a-9ce4-9feffea0504a/.local/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
+ " times = pd.to_datetime(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "data = datacube.execute()\n",
+ "data[0].plot.imshow()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6097dca4-5ada-4ea1-bb8c-9a853f3fa2e8",
+ "metadata": {},
+ "source": [
+ "### Apply an arithmetic formula"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8c56a01b-795b-4b3e-99e2-93622302d955",
+ "metadata": {},
+ "source": [
+ "We would like to improve the previous visualization, rescaling all the pixels between 0 and 1.\n",
+ "\n",
+ "We can use `apply` in combination with other `math` processes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "9d71f32a-4f58-4709-96ad-ebea3c248539",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from openeo.processes import linear_scale_range\n",
+ "input_min = -0.1\n",
+ "input_max = 0.2\n",
+ "output_min = 0\n",
+ "output_max = 1\n",
+ "\n",
+ "def rescale(x):\n",
+ " return linear_scale_range(x,input_min,input_max,output_min,output_max)\n",
+ "\n",
+ "scaled_data = datacube.apply(rescale)\n",
+ "scaled_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "179b0c31-97ce-47d7-9a87-16fc7d2bd544",
+ "metadata": {},
+ "source": [
+ "Visualize the result and see how `apply` scaled the data resulting in a more meaningful visualization:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "6ec03fc3-83c1-48b4-9e08-37e5665215cd",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/749c4c89-469c-4d4a-9ce4-9feffea0504a/.local/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
+ " times = pd.to_datetime(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "scaled_data_xr = scaled_data.execute()\n",
+ "scaled_data_xr[0].plot.imshow()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "cubes-and-clouds-cubes-and-clouds2",
+ "language": "python",
+ "name": "conda-env-cubes-and-clouds-cubes-and-clouds2-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/2.3_data_access/exercises/23_data_access_filter.ipynb b/_sources/2.3_data_access/exercises/23_data_access_filter.ipynb
new file mode 100644
index 0000000..03c4860
--- /dev/null
+++ b/_sources/2.3_data_access/exercises/23_data_access_filter.ipynb
@@ -0,0 +1,211 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5f4a0cab-e26e-4942-96ee-78de70890ad9",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "43f2c4b3-88f7-4ebf-8248-a72728d14012",
+ "metadata": {},
+ "source": [
+ "# 2.3 Data Access and Basic Processing\n",
+ "\n",
+ "## Filter Operators\n",
+ "\n",
+ "When interacting with large data collections, it is necessary to keep in mind that it's not possible to load everything!\n",
+ "\n",
+ "Therefore, we always have to define our requirements in advance and apply them to the data using filter operators.\n",
+ "\n",
+ "Let's start again with the same sample data from the Sentinel-2 STAC Collection with an additional filter."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dda8de1c-83d3-4dfd-a506-eb1a2302d752",
+ "metadata": {},
+ "source": [
+ "### Properties Filter\n",
+ "\n",
+ "When working with optical data like Sentinel-2, most of the times we would like to discard cloudy acquisitions as soon as possible.\n",
+ "\n",
+ "We can do it using a property filter: in this case we want to keep only the acquisitions with less than 50% cloud coverage."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "90a6673e-3235-42e6-8174-1b6bae256b7c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "properties = {\"eo:cloud_cover\": dict(lt=50)}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b5c18492-e3c7-4a05-a5b6-6c606b8253c1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import openeo\n",
+ "from openeo.local import LocalConnection\n",
+ "local_conn = LocalConnection('')\n",
+ "\n",
+ "url = \"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a\"\n",
+ "spatial_extent = {\"west\": 11.1, \"east\": 11.5, \"south\": 46.1, \"north\": 46.5}\n",
+ "\n",
+ "datacube = local_conn.load_stac(url=url,\n",
+ " spatial_extent=spatial_extent,\n",
+ " properties=properties)\n",
+ "datacube.execute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6097dca4-5ada-4ea1-bb8c-9a853f3fa2e8",
+ "metadata": {},
+ "source": [
+ "### Temporal filter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8c56a01b-795b-4b3e-99e2-93622302d955",
+ "metadata": {},
+ "source": [
+ "To slice along time the data collection with openEO, we can use the `filter_temporal` process."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9d71f32a-4f58-4709-96ad-ebea3c248539",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "temporal_extent = [\"2022-05-10T00:00:00Z\",\"2022-06-30T00:00:00Z\"]\n",
+ "temporal_slice = datacube.filter_temporal(temporal_extent)\n",
+ "temporal_slice.execute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1a41ee31-5e68-4c5d-ab4b-d3e6198725de",
+ "metadata": {},
+ "source": [
+ "After running the previous cell, it is visible that the result has less elements (or labels) in the temporal dimension `time`.\n",
+ "\n",
+ "Additionally, the size of the selected data reduced a lot."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cba45782-c4ad-4993-b511-84ebb8b6d354",
+ "metadata": {},
+ "source": [
+ "**Quiz hint: look carefully at the dimensions of the resulting datacube!**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23071292-8c4c-49cb-8968-ee90588896b6",
+ "metadata": {},
+ "source": [
+ "### Spatial filter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ecbc445-2b37-4e7d-a538-f194cc86a58c",
+ "metadata": {},
+ "source": [
+ "To slice along the spatial dimensions the data collection with openEO, we can use `filter_bbox` or `filter_spatial` processes."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "499ef2a5-0344-4c1f-a833-4c79f4aa652a",
+ "metadata": {},
+ "source": [
+ "The `filter_bbox` process is used with a set of coordinates:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ef0a59e7-18ff-4d94-b28e-975d247ad8bb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "spatial_extent = {\"west\": 11.259613, \"east\": 11.406212, \"south\": 46.461019, \"north\": 46.522237}\n",
+ "spatial_slice = datacube.filter_bbox(spatial_extent)\n",
+ "spatial_slice.execute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d07a11e3-2712-44f8-b185-497f707b3450",
+ "metadata": {},
+ "source": [
+ "**Quiz hint: look carefully at the dimensions of the loaded datacube!**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4830d00-518e-4d26-ba35-bc593df3438a",
+ "metadata": {},
+ "source": [
+ "### Bands filter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "75116f86-a34b-4784-8c54-35b59bf9da8c",
+ "metadata": {},
+ "source": [
+ "To slice along the bands dimension, keeping only the necessary bands, we can use the `filter_bands` process."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2db7b95c-7b61-4421-a091-d5a2badb974b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bands = [\"red\",\"green\",\"blue\"]\n",
+ "bands_slice = datacube.filter_bands(bands)\n",
+ "bands_slice.execute()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/2.3_data_access/exercises/23_data_access_lazy_loading.ipynb b/_sources/2.3_data_access/exercises/23_data_access_lazy_loading.ipynb
new file mode 100644
index 0000000..213a657
--- /dev/null
+++ b/_sources/2.3_data_access/exercises/23_data_access_lazy_loading.ipynb
@@ -0,0 +1,131 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5f4a0cab-e26e-4942-96ee-78de70890ad9",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "be23f346-9a95-4315-b8b0-dff22a207084",
+ "metadata": {},
+ "source": [
+ "# 2.3 Data Access and Basic Processing\n",
+ "\n",
+ "The exercise will use the openEO Python Client Side Processing functionality, which allows to experiment using openEO without a connection to an openEO back-end."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "211eb4ab-7ef2-4ece-895f-ba4b8ab0cd18",
+ "metadata": {},
+ "source": [
+ "**Quiz hint: remeber this information for the final quiz!**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3261f721-f8ad-4d89-9c15-d3d6b1a39a73",
+ "metadata": {},
+ "source": [
+ "## Lazy data loading\n",
+ "\n",
+ "When accessing data using an API, most of the time the data is **lazily** loaded.\n",
+ "\n",
+ "It means that only the metadata is loaded, so that it is possible to know about the data dimensions and their extents (spatial and temporal), the available bands and other additional information.\n",
+ "\n",
+ "Let's start with a call to the openEO process `load_stac` for lazily loading some Sentinel-2 data from a public STAC Collection. _Please note that not every STAC Collection or Item is currently supported._\n",
+ "\n",
+ "We need to specify an Area Of Interest (AOI) to get only part of the Collection, otherwise our code would try to load the metadata of all Sentinel-2 tiles available in the world!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10295498-eff4-4a30-ad77-56d9333965b3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from openeo.local import LocalConnection\n",
+ "local_conn = LocalConnection('')\n",
+ "\n",
+ "url = \"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a\"\n",
+ "spatial_extent = {\"west\": 11.1, \"east\": 11.5, \"south\": 46.1, \"north\": 46.5}\n",
+ "\n",
+ "datacube = local_conn.load_stac(url=url,\n",
+ " spatial_extent=spatial_extent)\n",
+ "datacube"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7f25ed7a-efed-4655-aac4-7f06b198b6a1",
+ "metadata": {},
+ "source": [
+ "Calling the `.execute()` method, the data will be lazily loaded and an `xArray.DataArray` object returned.\n",
+ "\n",
+ "Running the next cell will show the selected data content with the dimension names and their extent:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0686d0ef-6d05-482e-a5dd-f0098fb6efba",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "datacube.execute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fd6e97d5-4962-4be4-9e1c-46fe522b7abc",
+ "metadata": {},
+ "source": [
+ "From the output of the previous cell you can notice something really interesting: **the size of the selected data is more than 3 TB!**\n",
+ "\n",
+ "But you should have noticed that it was too quick to download this huge amount of data.\n",
+ "\n",
+ "This is what lazy loading allows: getting all the information about the data in a quick manner without having to access and download all the available files."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "375af2c0-f6b9-47ac-b11f-d1504b27a564",
+ "metadata": {},
+ "source": [
+ "**Quiz hint: look carefully at the dimensions of the loaded datacube!**"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/2.3_data_access/exercises/23_data_access_reduce.ipynb b/_sources/2.3_data_access/exercises/23_data_access_reduce.ipynb
new file mode 100644
index 0000000..56acf82
--- /dev/null
+++ b/_sources/2.3_data_access/exercises/23_data_access_reduce.ipynb
@@ -0,0 +1,339 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5f4a0cab-e26e-4942-96ee-78de70890ad9",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "43f2c4b3-88f7-4ebf-8248-a72728d14012",
+ "metadata": {},
+ "source": [
+ "# 2.3 Data Access and Basic Processing\n",
+ "\n",
+ "## Reduce Operators\n",
+ "\n",
+ "When computing statistics over time or indices based on multiple bands, it is possible to use reduce operators.\n",
+ "\n",
+ "In openEO we can use the [reduce_dimension](https://processes.openeo.org/#reduce_dimension) process, which applies a reducer to a data cube dimension by collapsing all the values along the specified dimension into an output value computed by the reducer."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5691278c-4d66-4ee2-8592-75431cbff3c7",
+ "metadata": {},
+ "source": [
+ "Reduce the temporal dimension to a single value, the mean for instance:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b5c18492-e3c7-4a05-a5b6-6c606b8253c1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import openeo\n",
+ "from openeo.processes import clip\n",
+ "from openeo.local import LocalConnection\n",
+ "local_conn = LocalConnection('')\n",
+ "\n",
+ "url = \"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a\"\n",
+ "spatial_extent = {\"west\": 11.259613, \"east\": 11.406212, \"south\": 46.461019, \"north\": 46.522237}\n",
+ "temporal_extent = [\"2021-05-28T00:00:00Z\",\"2021-06-30T00:00:00Z\"]\n",
+ "bands = [\"red\",\"nir\"]\n",
+ "datacube = local_conn.load_stac(url=url,\n",
+ " spatial_extent=spatial_extent,\n",
+ " temporal_extent=temporal_extent,\n",
+ " bands=bands)\n",
+ "datacube = datacube.apply(lambda x: clip(x,0,10000)) # Get rid of possible negative values\n",
+ "\n",
+ "datacube_mean_time = datacube.reduce_dimension(dimension=\"time\",reducer=\"mean\")\n",
+ "datacube_mean_time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "420f8621-fb50-4fc1-89a2-4dfc7ac0beea",
+ "metadata": {},
+ "source": [
+ "Check what happens to the datacube inspecting the resulting xArray object:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b3709eb5-f855-4cca-a2aa-e7f6ca43053e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "datacube_mean_time.execute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3dbfaf5a-39af-4a38-9d88-91a9d60e24e1",
+ "metadata": {},
+ "source": [
+ "It is possible to reduce in the same way all the available dimensions of the datacube.\n",
+ "\n",
+ "We can, for instance, reduce the band dimension similarly as we did for the temporal dimension:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5ccfe36d-d5de-4770-96ed-aaeae71fdd3d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "datacube_mean_band = datacube.reduce_dimension(dimension=\"band\",reducer=\"mean\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f05c9f31-0f40-47ea-9bf2-d9ab4bd783d6",
+ "metadata": {},
+ "source": [
+ "The result will now contain values resulting from the average of the bands:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "55001580-1fed-4cc8-a7ec-f9e0832fa285",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "datacube_mean_band.execute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3685933c-1d13-4c91-84fe-3a042197e1c6",
+ "metadata": {},
+ "source": [
+ "**Quiz hint: look carefully at number of pixels of the loaded datacube!**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "80aef996-8571-44e5-86c1-c2f80c0e8ff1",
+ "metadata": {},
+ "source": [
+ "The reducer could be again a single process, but when computing spectral indices like NDVI, NDSI etc. an arithmentical formula is used instead.\n",
+ "\n",
+ "For instance, the [NDVI](https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index) formula can be expressed using a `reduce_dimension` process over the `bands` dimension:\n",
+ "\n",
+ "$$ NDVI = {{NIR - RED} \\over {NIR + RED}} $$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "996558e8-a2a3-40fe-8361-e52d3f5fb79a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def NDVI(data):\n",
+ " red = data.array_element(index=0)\n",
+ " nir = data.array_element(index=1)\n",
+ " ndvi = (nir - red)/(nir + red)\n",
+ " return ndvi\n",
+ "\n",
+ "ndvi = datacube.reduce_dimension(reducer=NDVI,dimension=\"band\")\n",
+ "ndvi_xr = ndvi.execute()\n",
+ "ndvi_xr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cbfa0b5c-e686-4565-870d-5d8450d008fa",
+ "metadata": {},
+ "source": [
+ "Visualize a sample NDVI result:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f6ff2001-fe22-461b-81ae-a80fe6fbdd30",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "ndvi_xr[0].plot.imshow(vmin=-1,vmax=1,cmap=\"Greens\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a960282-f910-40a0-8b18-d0daa8466549",
+ "metadata": {},
+ "source": [
+ "Additionally, it is possible to reduce both spatial dimensions of the datacube at the same time.\n",
+ "\n",
+ "To do this, we need the `reduce_spatial` process.\n",
+ "\n",
+ "This time we select a smaller area of interest, to reduce the amount of downloaded data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ba158589-c535-476c-acde-8bcb7fc8d7f6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "url = \"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a\"\n",
+ "spatial_extent = {\"west\": 11.31369, \"east\": 11.31906, \"south\": 46.52167, \"north\": 46.52425}\n",
+ "temporal_extent = [\"2021-01-01T00:00:00Z\",\"2021-12-30T00:00:00Z\"]\n",
+ "bands = [\"red\",\"nir\"]\n",
+ "properties = {\"eo:cloud_cover\": dict(lt=15)}\n",
+ "\n",
+ "datacube = local_conn.load_stac(url=url,\n",
+ " spatial_extent=spatial_extent,\n",
+ " temporal_extent=temporal_extent,\n",
+ " bands=bands,\n",
+ " properties=properties)\n",
+ "datacube = datacube.apply(lambda x: clip(x,0,10000)) # Get rid of possible negative values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f0fbb70b-84fb-4579-8d7b-cb4cafa09045",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "datacube_spatial_median = datacube.reduce_spatial(reducer=\"median\")\n",
+ "datacube_spatial_median"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "90538e1c-a697-4e86-ba26-564cdca4057e",
+ "metadata": {},
+ "source": [
+ "Verify that the spatial dimensions were collapsed:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d38d3737-9c89-4b24-8700-0ed461aa0080",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "datacube_spatial_median.execute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "63278785-863a-4815-ac1b-22394fec29ea",
+ "metadata": {},
+ "source": [
+ "We can combine this spatial reducer with the previous over bands to compute a time series of NDVI values:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "44e087bb-83b3-45f1-81d9-1e11f560eaf4",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ndvi_spatial = datacube_spatial_median.reduce_dimension(reducer=NDVI,dimension=\"band\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b9cf3f16-a8e5-4bca-a2b4-7396e2e4a86a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "ndvi_spatial_xr = ndvi_spatial.execute()\n",
+ "ndvi_spatial_xr = ndvi_spatial_xr.compute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50da1d87-92fe-485a-920f-cf1101a35636",
+ "metadata": {},
+ "source": [
+ "Remember that calling `.compute()` on an xarray + dask based object will load into memory the data.\n",
+ "In this case it will trigger the download of the data from the STAC Catalog and the processing defined as openEO process graph, computing the NDVI time series."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8cfd1db9-a7ed-4ec7-aabc-b6a599941817",
+ "metadata": {},
+ "source": [
+ "Visualize the NDVI time series:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15a9cd39-5fa9-4e17-852b-58297d622a77",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ndvi_spatial_xr.where(ndvi_spatial_xr<1).plot.scatter()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/2.3_data_access/exercises/23_data_access_resample.ipynb b/_sources/2.3_data_access/exercises/23_data_access_resample.ipynb
new file mode 100644
index 0000000..06a0c5f
--- /dev/null
+++ b/_sources/2.3_data_access/exercises/23_data_access_resample.ipynb
@@ -0,0 +1,171 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5f4a0cab-e26e-4942-96ee-78de70890ad9",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "43f2c4b3-88f7-4ebf-8248-a72728d14012",
+ "metadata": {},
+ "source": [
+ "# 2.3 Data Access and Basic Processing\n",
+ "\n",
+ "## Resample Operators\n",
+ "\n",
+ "Sometimes we need to align the spatial or temporal dimension of two datacubes, so that they can be merged together."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5691278c-4d66-4ee2-8592-75431cbff3c7",
+ "metadata": {},
+ "source": [
+ "### `resample_cube_spatial`: spatial resampling Sentinel-2 to match Landsat"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40dbc4e1-929a-4256-9e62-605d5a424fca",
+ "metadata": {},
+ "source": [
+ "Start importing the necessary libraries and initialize a local connection for Client-Side Processing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4eca5284-2873-4b5c-9505-d67545fdec05",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import openeo\n",
+ "from openeo.local import LocalConnection\n",
+ "local_conn = LocalConnection('')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0420def5-ebd5-446e-a6d0-799e3534e5d2",
+ "metadata": {},
+ "source": [
+ "Create two datacubes, one for Sentinel-2 and one for Landsat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "053869f1-540f-48c1-b3ab-113d5be08bc6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "url = \"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a\"\n",
+ "\n",
+ "spatial_extent = {\"west\": 11.4, \"east\": 11.42, \"south\": 45.5, \"north\": 45.52}\n",
+ "temporal_extent = [\"2023-06-01\", \"2023-06-30\"]\n",
+ "bands = [\"red\",\"nir\"]\n",
+ "\n",
+ "s2_cube = local_conn.load_stac(url=url,\n",
+ " spatial_extent=spatial_extent,\n",
+ " temporal_extent=temporal_extent,\n",
+ " bands=bands\n",
+ ")\n",
+ "s2_cube.execute()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "da39cb8c-e911-4f8d-aba6-52bf8569a48c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "url = \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/landsat-c2-l2\"\n",
+ "bands = [\"red\",\"nir08\"]\n",
+ "l8_cube = local_conn.load_stac(url=url,\n",
+ " spatial_extent=spatial_extent,\n",
+ " temporal_extent=temporal_extent,\n",
+ " bands=bands)\n",
+ "l8_cube.execute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49a04782-d412-4229-aa4b-d8e0283d5feb",
+ "metadata": {},
+ "source": [
+ "From the previous outputs, notice the shape difference in the spatial dimensions `x` and `y`.\n",
+ "\n",
+ "This is due to the different resolution of the two collections: 10m for Sentinel-2, 30m for Landsat.\n",
+ "\n",
+ "Now we use the `resample_cube_spatial` process to resample the Sentinel-2 data to match Landsat."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b652338d-5f31-4d33-9d46-b7ea76f1d23d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "s2_cube_30m = s2_cube.resample_cube_spatial(target=l8_cube,method=\"average\")\n",
+ "s2_cube_30m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "420f8621-fb50-4fc1-89a2-4dfc7ac0beea",
+ "metadata": {},
+ "source": [
+ "Check what happens to the datacube inspecting the resulting xArray object. Now the `x` and `y` shape is the same as Landsat:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f38e5288-7b95-4693-a912-b51c31a60e16",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "s2_cube_30m.execute()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "cubes-and-clouds-cubes-and-clouds2",
+ "language": "python",
+ "name": "conda-env-cubes-and-clouds-cubes-and-clouds2-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/2.4_formats_and_performance/2.4_formats_and_performance.md b/_sources/2.4_formats_and_performance/2.4_formats_and_performance.md
new file mode 100644
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+++ b/_sources/2.4_formats_and_performance/2.4_formats_and_performance.md
@@ -0,0 +1,201 @@
+# Data Formats and Performance
+
+## Learning Objectives
+- Understand what cloud native data formats are
+- Understand how the cloud does computing more efficiently
+
+
+## Outline
+- Cloud Native Data Formats
+ - Why do we need them
+ - What is there advantage over classical file formats
+ - Not one format to rule them all.
+ - Examples
+- Performance in the cloud
+ - Tiling
+ - Scaling
+ - Distributed Computing
+- The Microsoft Planetary Computer Setup - State of the Art open source cloud native technology stack
+ - ODC, STAC, xarray, dask, cogs, ...
+
+## Cloud native data formats
+
+### What are cloud native data formats
+Cloud native formats or cloud-optimized formats, are file formats specifically designed to optimize the storage, access, and processing of geospatial data in cloud computing environments. These formats are tailored to leverage the scalability, flexibility, and parallel processing capabilities of cloud infrastructure, enabling efficient handling of large-scale datasets.
+
+[](https://www.youtube.com/watch?v=hxv3LxsdGhw)
+> Video content in cooperation with [Aimee Barciauskas](https://developmentseed.org/team/aimee-barciauskas) (DevelopmentSeed) and [Ryan Avery](https://developmentseed.org/team/ryan-avery) (DevelopmentSeed).
+> *"Cloud-optimised means organizing so subsets of data can be read. Ideally, the data is also compressed. Both of these factors minimize the amount of data that has to be transferred across a network."*
+
+### Characteristics of cloud native data formats
+Cloud-optimized means mainly optimized "read" access with partial reads and also parallel reads. Main characteristics common for cloud-optimized formats:
+
+- **Data Chunking:** Cloud native formats employ a chunk-based organization, where the data is divided into smaller chunks or blocks. This enables parallel processing and efficient retrieval of specific portions of the data, reducing the need to access the entire dataset.
+
+- **Internal Indexing:** These formats incorporate internal indexing structures that facilitate fast spatial and attribute queries. This enables efficient data access and retrieval operations without the need for extensive scanning or processing of the entire dataset.
+
+- **Metadata Optimization:** Cloud native formats optimize metadata storage and indexing, allowing for efficient access and retrieval of metadata associated with the data at once. This supports faster discovery and interpretation of data properties and characteristics.
+
+- **Compression and Tiling:** Cloud native formats often employ advanced compression techniques to reduce storage requirements while maintaining data quality. Additionally, they utilize tiling strategies to divide the data into smaller, manageable tiles that can be independently accessed and processed.
+
+HTTP Range Request allows clients to request only a specific portion or range of data instead of a complete dataset.
+
+### Examples of cloud native data formats
+
+- **COG** - Cloud-Optimized GeoTIFF (COG) is an optimized version of the GeoTIFF format. It organizes raster data into chunks, utilizes internal tiling and compression, and uses HTTP range requests for efficient data access in the cloud.
+- **ZARR** Zarr is a format specifically designed for storing and accessing multidimensional arrays. It supports chunking, compression, and parallel processing, making it suitable for large-scale geospatial datasets, for example, weather data. Metadata is stored externally in data files itself.
+- **FlatGeoBuf** Cloud optimized vector data format. It is a binary encoding format for geodata and holds a collection of Simple Features.
+
+
+
+
+### Available Material
+- Ryan Avery, Aimee Barciauskas, Development Seed, United States (2023). Technologies used to Create, Store and Access Geospatial Data in the Cloud. https://2023.ieeeigarss.org/view_paper.php?PaperNum=5306
+- ESIP Talk on Cloud Native Formats: https://www.youtube.com/watch?v=ac_UKunUrNM
+- FOSS4G Talk On Cloud Native Formats (Matthew Hanson)
+ - https://talks.osgeo.org/foss4g-2023/talk/XBHYF9/
+ - https://space.cloud68.co/s/xExLwCmzzKEcoB9?dir=undefined&path=%2FLumbardhi%2F28.06.2023&openfile=1356611
+- OGC White Paper on Cloud Native Formats (Chris Holmes, Scott Simmons):
+ - https://www.ogc.org/blog-article/towards-a-cloud-native-ogc/
+ - https://www.ogc.org/blog-article/towards-a-cloud-native-geospatial-standards-baseline/
+ - https://www.ogc.org/blog-article/the-latest-on-cloud-native-geospatial-standards-in-ogc/
+- Cloud-Native Geospatial Foundation initiative of Radiant Earth:
+ - https://cloudnativegeo.org
+- [Tweet](https://twitter.com/opencholmes/status/1677144519215833089) from Chris Holmes (great example - postholer)
+
+## Scaling
+- Show concepts of scaling
+- Mention resource usage on cloud platforms. Track resources. Every processing has it's cost.
+
+Scaling refers to the process of increasing or decreasing the capacity or size of a system to handle a larger or smaller workload or data volume. Scaling does not necessarily means only in the direction of larger and bigger but also saving unnecessary costs in times when there is no traffic. In our context, scaling involves managing the growth of data, traffic, or processing requirements to ensure optimal performance and availability.
+
+## Horizontal vs vertical scaling
+
+Two classical approaches to scaling are horizontal and vertical:
+
+- **Horizontal scaling:** Also known as scaling out, horizontal scaling involves adding more machines or nodes to distribute the workload across a larger number of resources. This could mean adding more servers or instances to handle increased traffic or data processing demands. Horizontal scaling offers the advantage of improved fault tolerance and increased capacity, as the workload is spread across multiple resources.
+- **Vertical scaling:** Also known as scaling up, vertical scaling involves increasing the capacity of an individual machine or resource. This can be achieved by upgrading the hardware, such as adding more powerful processors, memory, or storage, to handle the growing demands of the geospatial application. Vertical scaling is often suitable for applications with single-node architectures or when the workload cannot be easily distributed across multiple machines.
+
+Both horizontal and vertical scaling have their advantages and considerations. Horizontal scaling provides better scalability and fault tolerance, as it can handle increased traffic and processing by adding more resources. However, it may require additional effort to distribute and synchronize data across multiple nodes. Vertical scaling, on the other hand, simplifies data management as all resources are contained within a single node, but it may have limitations in terms of the maximum capacity a single machine can handle.
+
+In common workflows, a combination of both approaches is used to ensure optimal speed and resource utilization while being able to keep the simplicity of a workflow.
+
+## How to scaling
+
+There are many approaches how to handle scaling properly.
+
+todo: parallel computing section
+
+### Subscription vs. On-Demand usage
+
+**Subscription:** A subscription model involves a fixed, recurring payment made by the user to access and utilize the cloud platform's services over a specific period, typically monthly or annually. Under a subscription model, users typically commit to a predetermined level of resources and pay a regular fee regardless of the actual usage during that period. This model often provides cost predictability and may offer discounts or benefits for long-term commitments. Users can usually choose from various options and combinations of resources (eg. GPU, CPU, disk storage combinations).
+
+Advantages of the Subscription Model:
+
+- Cost Predictability: Users have a clear understanding of the ongoing costs as they pay a fixed fee.
+- Potential Cost Savings: Subscriptions may offer discounts or cost benefits for longer-term commitments.
+- Continuous Service Access: Users have continuous access to the subscribed services without the need for frequent renewal or payment management.
+
+**On-Demand Usage:** In an on-demand model, users pay for the cloud platform's services based on actual usage and consumption. Users are charged on a pay-as-you-go basis, where they pay for the resources or services they utilize in a given period. There are no long-term commitments or fixed fees. This model offers flexibility and scalability, allowing users to scale resources up or down as per their needs.
+
+Advantages of On-Demand Usage:
+
+- Flexibility: Users have the flexibility to adjust resources based on their varying demands, scaling up or down as required.
+- Cost Efficiency: Users only pay for what they use, making it suitable for workloads with unpredictable or fluctuating resource needs.
+- No Long-Term Commitments: On-demand models do not require users to commit to a specific period or predefined resource levels, allowing for agility and quick adjustments.
+
+Choosing between subscription and on-demand models depends on various factors, including the nature of your workloads, budget considerations, and usage patterns. Based on this (and data availability) users can choose a platform that suits their needs best. Reviewing the pricing details is an important part before selecting a working environment.
+
+### Cost of scalability
+
+Direct examples of computing on a workflow
+(todo: based on actual workflow)
+
+### Memory consumption
+
+limitations and
+
+### Difference between platform usage and cloud directly
+
+TODO: Is this covered already in the platform lesson? Yes
+Using platforms removes complexity and adds abstraction layers
+
+
+## What to avoid and what are the limitations
+
+While scaling is providing many options and is essential for achieving results on a larger scale, there are some limitations to keep in mind and activities to even avoid.
+
+Costs: One of the main characteristics to consider are costs of computing. Scaling resources dynamically can lead to increased costs, especially if not properly managed. It is essential to monitor resource usage and set appropriate maximum scaling policies to ensure cost optimization. Failure to do so may result in unnecessary provisioning of resources, leading to higher expenses. The purchase of many computational resources can be easy, but very costly. Code optimization is important to ensure there are no memory leaks, unnecessary data storage, and other expensive operations.
+
+Data Access: In geospatial cloud workflows, one of the big challenges lies in data access and optimal data storage. The easy trap is in loading large portions of unnecessary data without applying correct filters ahead. Such data volumes can lead to more requirements on RAM or disk space resulting in higher costs of processing or longer times (and more computational time) just to load the data.
+
+Accessing data as files: Geospatial data are stored in many formats as discussed in this lesson and some are more appropriate to access in the cloud. The opportunity of first evaluating metadata before loading the whole dataset is great for saving time and money.
+
+Latency and Data Transfer: In distributed and scaled-out architectures, managing data transfer and minimizing latency can be crucial. Moving data between services or instances across different locations can introduce network overhead and impact application performance. Efficient data caching, or data partitioning strategies can help mitigate these challenges.
+
+Scaling Limits in the platform: While cloud platforms offer high scalability, there are still practical limits to consider. Every service or resource has its scalability limits, such as maximum instance count, storage capacity, or network throughput. It is important to understand these limitations and design your programs and applications accordingly.
+
+To mitigate these challenges and limitations, it's advisable to thoroughly plan and architect your application for scalability, leverage cloud-native tools and services, monitor resource usage and costs, and regularly test and optimize your scaling strategies. Additionally, staying updated with the latest advancements in cloud technologies and best practices will help you navigate the complexities of cloud-native scaling more effectively.
+
+
+#### Animated Content: Tiling and application (drag and drop)
+
+
+
+## Quiz
+What are cloud native data formats in EO and GIS
+
+ [(X)] Cloud native data formats in EO should be compatible to cloud services (APIs, http requests, cloud storage), enable fast viewing and access to spatial sub regions.
+ [( )] Cloud native data formats in EO are exclusively designed to be compressed as much as possible, so that the least amount of storage space is necessary.
+ [( )] Cloud native data formats in EO have to be human readable when you open them in a text editor.
+
+Connect the cloud native data format and it's predecessor to it's spatial data type (https://www.ogc.org/blog-article/towards-a-cloud-native-geospatial-standards-baseline/, (https://cholmes.medium.com/an-overview-of-cloud-native-vector-c223845638e0)
+
+- [[raster] [n-dimensional raster] [vector]]
+- [( ) ( ) (X) ] Shapefile
+- [( ) (X) ( ) ] Zarr
+- [( ) ( ) (X) ] GeoParquet/Arrow
+- [( ) ( ) (X) ] Flatgeobuff
+- [(X) ( ) ( ) ] GeoTIFF
+- [( ) (X) ( ) ] NetCDF
+- [(X) ( ) ( ) ] Cloud Optimized GeoTIFF (COG)
+
+
+Lazy-loading is essential when working with large data collections, why?
+
+## Compression Quiz
+
+Consider a cloud provider, that should decide if compressing the data on its storage would let it spare money.
+
+If the compression process of a COG takes 0.05 CPU hour every 1 GB of data, the total amount of COGs on the storage takes 1200 TB and one CPU hour costs 0.05€ for the first 10000 CPU hour and then 0.03€ for the rest, how much would the compression process cost?
+
+ [( )] 3000€
+ [( )] 1200€
+ [(X)] 2000€
+ [( )] 2043€
+
+Solution:
+- 1 TB = 1000 GB following the international standard, see https://en.wikipedia.org/wiki/Gigabyte.
+- The amount of data on the storage is 1200 TB * 1000 GB/TB = 1200000 GB
+- If to compress 1 GB of data takes 0.05 CPU hour, to compress 1200000 GB it will take: 1200000 GB * 0.05 CPU hour / GB = 60000 CPU hour
+- The first 10000 CPU hour cost 10000 CPU hour * 0.05 €/CPU hour = 500€
+- The rest is 50000 CPU hour and it will cost 50000 CPU hour * 0.03 €/CPU hour = 1500 €
+- The total is 500 € + 1500 € = 2000 €
+
+In the same scenario, now we also consider the storage maintanance cost. If each TB of storage has a maintenance cost of 0.5 € every month, how much time would it take before the compression would let the cloud provider spare money? Consider a compression rate of 70%.
+
+ [( )] ~ 9 months
+ [( )] ~ 10 months
+ [(X)] ~ 11 months
+ [( )] ~ 12 months
+
+Solution:
+- Without compression, every month the storage would cost: 1200 TB * 0.5 €/TB = 600 €
+- With a compression rate of 70%, the data would use 1200 TB * 0.7 = 840 TB of disk space
+- With compression, every month the storage would cost: 840 TB * 0.5 €/TB = 420 €
+- With compression, each month it's possible to spare 180 €.
+- Since the compression process costs 2000 €, and each month costs 180 € less with it, it would take 2000 € / 180 €/month ~= 11 months to start to spare money.
+
+
+Calculate the volume of data needed for the snow workflow according to the collections, time steps, bands, resolutions, spatial extent selected in the previous chapter, value types, format, compression estimate, etc. (this only if we define snow workflow already here) - Don't use snow workflow -> Set that information
+
diff --git a/_sources/3.1_data_processing/3.1_data_processing.md b/_sources/3.1_data_processing/3.1_data_processing.md
new file mode 100644
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+# 3.1 Data Processing
+
+## Learning Objectives
+
+- Carry out an EO workflow on a cloud platform
+- Select suitable data
+- Chain processes to form an EO processing chain/workflow
+- Visualize the results
+
+## Introduction
+
+In this lecture we are going to combine the knowledge and hands-on experience we have gathered so far to create a full EO workflow on a cloud platform.
+We will
+
+- define a research question,
+- choose and load the necessary data sources,
+- define the data cube to our needs,
+- use functions to process the data,
+- visualize the result
+- and track the resources we are consuming on the platform.
+
+## Case Study: Snow Cover in the Alps
+
+[](https://youtu.be/bE_yz0VCLRE)
+> Video content in cooperation with [Matteo Dall'Amico](https://waterjade.com/en/about-us/) (MobyGIS - Waterjade) and [Federico Di Paolo](https://waterjade.com/en/about-us/) (MobyGIS - Waterjade).
+> *“How much snow is stored in that mountain, when will it melt?” We answered this question using EO cloud platforms! Have a look: [EO4Alps Snow](https://waterjade.com/eo4alps-snow/)*
+
+### Research Question
+
+Snow serves as a water reservoir and is thus important for any hydrological management activity, such as irrigation planning, drink water supply or hydro power generation. Knowing precisely, when and where snow is present is a critical source of information for these acitivities. Satellite earth observation plays an important role in describing the snow cover, both globally and in local mountain ranges. This is due to it's ability to sense information throughout space (complete coverage of the globe) and time (repeated measurements). Our goal is to create a time series of the snow covered area of the catchment of interest. We will use this time series to compare it to the run off at the main outlet of the catchment. And study the relationship between snow dynamics and runoff.
+
+### Approach
+
+In this exercise we are going to derive the snow cover in an alpine catchment using Sentinel-2 data. Sentinel-2 carries an optical sensor, it is measuring the reflected light of the earths surface in differenct wavelenghts. At a 20 m spatial resolution and at a 6 day repeat rate. We are using the Green and SWIR bands to calculate the Normalized Difference Snow Index (NDSI). It is calculated as follows:
+
+$$ NDSI = \\frac {GREEN - SWIR} {GREEN + SWIR} $$
+
+Snow typically has very high visible (VIS) reflectance and very low reflectance in the shortwave infrared (SWIR), a characteristic used to detect snow by distinguishing between snow and most cloud types. The NDSI expresses this phenomenon as a formula. It results in a value between -1 and 1. The higher the value is, the more probable it is that the surface is covered with snow. In order to create a binary snow map we apply a threshold of $NDSI < 0.4$. This is a commonly used value for discriminating snowcovered and snow free areas. Then we spatially aggregate the snow free and snow covered pixels in the catchment area by summing them up. In order to get the snow covered area of the catchment we multiply the number of snow covered pixels by the pixel resolution. Additionally, we have to deal with cloud cover. We use the Sentinel-2 cloud mask that is provided with the data and exclude all images that have a cloud cover over 25 % in our study area. Ideally we should fill the gaps the clouds generate, since they are introducing uncertainty. Nevertheless, for a first try our approach should be good enough to get a general idea about the snow cover in our area of interest. In the end we receive a time series with the snow covered area in the catchment.
+The approach we are using is very basic. There are many assumptions and simplifications involved. A critical analysis of the workflow and possible improvements follows in Section [3.3 Validation](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/3.3_validation/3.3_validation.md#critically-analyse-a-workflow).
+
+### Workflow Description
+
+- get data:
+ - `load_collection()`
+- calculate ndsi:
+ - `filter_bands()`, `reduce_dimension()`
+ - creates a -1 to 1 map, 1 signifies high probability of snow
+- create binary snow classification (by threshold):
+ - `gt()`, `mask()`,
+ - create a binary snow classification: 0 = no snow, 1 = snow
+- cloud masking:
+ - `eq()`, `mask()`
+ - create a binary cloud mask using the S2 scene classification
+ - Apply the mask to the binary snow map: 0 = no snow, 1 = snow, NA = cloud
+ - This gives us the **cloudfree snow covered area** for our catchment as an image time series (x, y, time, sca)
+- catchment statistics - cloud pixels, no snow pixels, snow pixels:
+ - `eq()`, `gt()`, `merge_cubes()`, `aggregate_spatial()`, `sum()`
+ - create 3 binary data cubes: pixel in catchment, pixel cloud, pixel snow.
+ - merge the 3 cubes: x, y, time, band (catchment, cloud, snow)
+ - aggregate_spatial: sum up the values to get the total number of pixels per time step per band (catchment, cloud, snow)
+ - calculate the percentages of clouds and snow per time step.
+- filter timeseries according to cloud coverage:
+ - filter the dates that have cloud coverages > 25%
+- Plot the **time series of the snow covered area** in the catchment.
+
+#### Animated Content: Embed Process Graph
+
+## Exercise
+
+Now we have covered the most important topics of our use case in theory. Let's move on to produce some results!
+
+> :warning: The applied workflow is a simple approach used for educational reasons to learn how to use EO cloud platforms.
+
+[Exercise 3.1 Processing](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/3.1_data_processing/exercises/31_data_processing.ipynb)
+
+## Quiz
+
+What is the city at the outlet of the catchment? _Answer in the exercise: 31_data_processing.ipynb section **'Region of Interest'**_
+
+ [(x)] Meran
+ [( )] Innsbruck
+ [( )] Grenoble
+
+How many images are available in the time range ("2018-02-01" and "2018-06-30")? _Answer in the exercise: 31_data_processing.ipynb section **'Calculate Catchment Statistics'**, might require you to run some additional code blocks_
+
+ [( )] 0-20
+ [( )] 21-40
+ [(x)] 41-60
+
+How many pixels are in the spatially aggregated data cube? (time * x * y * bands) _Answer in the exercise: 31_data_processing.ipynb section **'Calculate Catchment Statistics'**_
+
+ [( )] 50,000,000 - 100,000,000
+ [(x)] 100,000,001 - 200,000,000
+ [( )] 200,000,001 - 500,000,000
+
+How many resources did the computation of the data cube take? _Answer in the exercise: 31_data_processing.ipynb section **'Calculate Catchment Statistics'**_
+
+ [(x)] 0 - 20
+ [( )] 21 - 100
+ [( )] 101 - 200
+
+How many snow covered pixels are there across all time steps? _Answer in the exercise: 31_data_processing.ipynb section **'Calculate Catchment Statistics'**_
+
+ [( )] 10,000,000 - 50,000,000
+ [(x)] 50,000,001 - 100,000,000
+ [( )] 100,000,001 - 200,000,000
+
+At which time step is the maximum snow cover reached? _Answer in the exercise: 31_data_processing.ipynb section **'Calculate Catchment Statistics'**_
+
+ [( )] 2018-02-13
+ [( )] 2018-02-21
+ [(x)] 2018-03-08
+
+What is the number of snow covered pixels on that date? _Answer in the exercise: 31_data_processing.ipynb section **'Calculate Catchment Statistics'**_
+
+ [( )] 4,000,001 - 6,000,000
+ [( )] 0 - 2,000,000
+ [(x)] 2,000,001 - 4,000,000
+
+What does that represent in area (km2)? _Answer in the exercise: 31_data_processing.ipynb section **'Calculate Catchment Statistics'**_
+
+ [(x)] 201 - 400
+ [( )] 0 - 200
+ [( )] 401 - 600
diff --git a/_sources/3.1_data_processing/3.1_exercises/31_data_processing.ipynb b/_sources/3.1_data_processing/3.1_exercises/31_data_processing.ipynb
new file mode 100644
index 0000000..60cc87e
--- /dev/null
+++ b/_sources/3.1_data_processing/3.1_exercises/31_data_processing.ipynb
@@ -0,0 +1,2126 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0fbba1f4-9e0b-4c47-9e5c-19dbae956a0c",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c6aed2f-1493-4636-a124-03c81b28bc52",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# 3.1 Data Processing\n",
+ "In this exercise we will build a complete EO workflow on a cloud platform; from data access to obtaining the result. In this example we will analyse snow cover in the Alps.\n",
+ "\n",
+ "We are going to follow these steps in our analysis:\n",
+ "\n",
+ "- Load satellite collections\n",
+ "- Specify the spatial, temporal extents and the features we are interested in\n",
+ "- Process the satellite data to retrieve snow cover information\n",
+ "- aggregate information to get catchment statistics over time\n",
+ "- Visualize and analyse the results\n",
+ "\n",
+ "More information on the openEO Python Client: https://open-eo.github.io/openeo-python-client/index.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cfe1892d-f9f9-4e9a-8d82-db000c59b6ce",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Libraries"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c7a11bc4-0c90-4357-ae72-0075ec479f7f",
+ "metadata": {},
+ "source": [
+ "We start by creating the shared folders and data files needed to complete the exercise using the following shell commands"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "630caf52-0967-43b0-aa89-4900310c7ffb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "!cp -r $DATA_PATH/31_results/ $HOME/\n",
+ "!cp -r $DATA_PATH/31_data/ $HOME/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "1abf270c-d73e-4109-854c-3cf36a1b36d7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# platform libraries\n",
+ "import openeo\n",
+ "\n",
+ "# utility libraries\n",
+ "from datetime import date\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "import rioxarray\n",
+ "import json\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import geopandas as gpd\n",
+ "import leafmap.foliumap as leafmap"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f9d64c64-39db-4869-917c-ed24bef18204",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Connect to a cloud platform\n",
+ "Connect to the Copernicus Dataspace Ecosystem. Being connected allows for data discovery."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "86afd551-857e-4129-a2ee-39a933255f34",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "conn = openeo.connect('https://openeo.dataspace.copernicus.eu/')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0ed52c6b-9279-4563-8b1d-b51b8bdf8d63",
+ "metadata": {},
+ "source": [
+ "And login. Being logged in allows to use the full range of functionality including processing!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "bc9dbe7a-ff09-439c-9aef-4004fed80820",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Authenticated using refresh token.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "conn.authenticate_oidc()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d8bfd388-c854-49f1-88b7-4879d5f5c2e1",
+ "metadata": {},
+ "source": [
+ "Check if the login worked."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "49e8aa86-c476-413c-a186-6f8df29b7429",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "conn.describe_account()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e95d456-a831-4cca-ab1f-5a2ec8805291",
+ "metadata": {},
+ "source": [
+ "## Region of Interest"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a324185-d64a-4e69-9601-8f63af0805ac",
+ "metadata": {},
+ "source": [
+ "Our region of interest is the Val Passiria Catchment in the South Tyrolian Alps (Italy). Let's load the catchment area."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "b1679451-2868-4109-ab17-38532a8033e3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "catchment_outline = gpd.read_file('31_data/catchment_outline.geojson')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "3cd3057b-39f8-4644-886d-bdbad43c6cb5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "center = (float(catchment_outline.centroid.y), float(catchment_outline.centroid.x))\n",
+ "m = leafmap.Map(center=center, zoom=10)\n",
+ "m.add_vector('31_data/catchment_outline.geojson', layer_name=\"catchment\")\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8d2e24bb-ae0a-4033-bebf-66ca9aa4e5d1",
+ "metadata": {},
+ "source": [
+ "**Quiz hint: Look closely at the end of the displayed catchment area to identify the outlet**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "284cd7b6-e646-4950-b747-4e1a734f148b",
+ "metadata": {},
+ "source": [
+ "## Inspect Metadata\n",
+ "We need to set the following configurations to define the content of the data cube we want to access:\n",
+ "- dataset name\n",
+ "- band names\n",
+ "- time range\n",
+ "- the area of interest specifed via bounding box coordinates\n",
+ "- spatial resolution\n",
+ "\n",
+ "To select the correct dataset we can first list all the available datasets."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "7a3592c5-f078-48c0-b039-270fe99f48d6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['SENTINEL3_OLCI_L1B', 'SENTINEL3_SLSTR', 'SENTINEL_5P_L2', 'SENTINEL2_L1C', 'SENTINEL2_L2A', 'SENTINEL1_GRD', 'COPERNICUS_30', 'LANDSAT8_L2']\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(conn.list_collection_ids())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "958ff432-b8cb-45b5-b6c4-68c1e0f92640",
+ "metadata": {},
+ "source": [
+ "We want to use the Sentinel-2 L2A product. It's name is `'SENTINEL2_L2A'`. \n",
+ "\n",
+ "We get the metadata for this collection as follows. This is an important step to familiarize yourself with the data collection (e.g. learn the band names)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "7bf6babb-457b-4a09-81d5-092b92c32d5d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ "{'assets': {'thumbnail': {'href': 'https://www.esa.int/var/esa/storage/images/esa_multimedia/images/2022/07/tallinn_estonia/24383281-1-eng-GB/Tallinn_Estonia_pillars.jpg',\n",
+ " 'roles': ['thumbnail'],\n",
+ " 'title': 'Sentinel 2 L2A',\n",
+ " 'type': 'image/png'}},\n",
+ " 'cube:dimensions': {'bands': {'type': 'bands',\n",
+ " 'values': ['B01',\n",
+ " 'B02',\n",
+ " 'B03',\n",
+ " 'B04',\n",
+ " 'B05',\n",
+ " 'B06',\n",
+ " 'B07',\n",
+ " 'B08',\n",
+ " 'B8A',\n",
+ " 'B09',\n",
+ " 'B11',\n",
+ " 'B12',\n",
+ " 'WVP',\n",
+ " 'AOT',\n",
+ " 'SCL',\n",
+ " 'sunAzimuthAngles',\n",
+ " 'sunZenithAngles',\n",
+ " 'viewAzimuthMean',\n",
+ " 'viewZenithMean']},\n",
+ " 't': {'extent': ['2015-07-04T00:00:00Z', None], 'type': 'temporal'},\n",
+ " 'x': {'axis': 'x',\n",
+ " 'extent': [-180, 180],\n",
+ " 'reference_system': {'$schema': 'https://proj.org/schemas/v0.2/projjson.schema.json',\n",
+ " 'area': 'World',\n",
+ " 'bbox': {'east_longitude': 180,\n",
+ " 'north_latitude': 90,\n",
+ " 'south_latitude': -90,\n",
+ " 'west_longitude': -180},\n",
+ " 'coordinate_system': {'axis': [{'abbreviation': 'Lat',\n",
+ " 'direction': 'north',\n",
+ " 'name': 'Geodetic latitude',\n",
+ " 'unit': 'degree'},\n",
+ " {'abbreviation': 'Lon',\n",
+ " 'direction': 'east',\n",
+ " 'name': 'Geodetic longitude',\n",
+ " 'unit': 'degree'}],\n",
+ " 'subtype': 'ellipsoidal'},\n",
+ " 'datum': {'ellipsoid': {'inverse_flattening': 298.257223563,\n",
+ " 'name': 'WGS 84',\n",
+ " 'semi_major_axis': 6378137},\n",
+ " 'name': 'World Geodetic System 1984',\n",
+ " 'type': 'GeodeticReferenceFrame'},\n",
+ " 'id': {'authority': 'OGC', 'code': 'Auto42001', 'version': '1.3'},\n",
+ " 'name': 'AUTO 42001 (Universal Transverse Mercator)',\n",
+ " 'type': 'GeodeticCRS'},\n",
+ " 'step': 10,\n",
+ " 'type': 'spatial'},\n",
+ " 'y': {'axis': 'y',\n",
+ " 'extent': [-56, 83],\n",
+ " 'reference_system': {'$schema': 'https://proj.org/schemas/v0.2/projjson.schema.json',\n",
+ " 'area': 'World',\n",
+ " 'bbox': {'east_longitude': 180,\n",
+ " 'north_latitude': 90,\n",
+ " 'south_latitude': -90,\n",
+ " 'west_longitude': -180},\n",
+ " 'coordinate_system': {'axis': [{'abbreviation': 'Lat',\n",
+ " 'direction': 'north',\n",
+ " 'name': 'Geodetic latitude',\n",
+ " 'unit': 'degree'},\n",
+ " {'abbreviation': 'Lon',\n",
+ " 'direction': 'east',\n",
+ " 'name': 'Geodetic longitude',\n",
+ " 'unit': 'degree'}],\n",
+ " 'subtype': 'ellipsoidal'},\n",
+ " 'datum': {'ellipsoid': {'inverse_flattening': 298.257223563,\n",
+ " 'name': 'WGS 84',\n",
+ " 'semi_major_axis': 6378137},\n",
+ " 'name': 'World Geodetic System 1984',\n",
+ " 'type': 'GeodeticReferenceFrame'},\n",
+ " 'id': {'authority': 'OGC', 'code': 'Auto42001', 'version': '1.3'},\n",
+ " 'name': 'AUTO 42001 (Universal Transverse Mercator)',\n",
+ " 'type': 'GeodeticCRS'},\n",
+ " 'step': 10,\n",
+ " 'type': 'spatial'}},\n",
+ " 'description': 'SENTINEL-2 is a wide-swath, high-resolution, multi-spectral imaging mission, supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas. The SENTINEL-2 Multispectral Instrument (MSI) samples 13 spectral bands: four bands at 10 metres, six bands at 20 metres and three bands at 60 metres spatial resolution.\\n The level 2A data is atmospherically corrected using Sen2Cor.',\n",
+ " 'extent': {'spatial': {'bbox': [[-180, -56, 180, 83]]},\n",
+ " 'temporal': {'interval': [['2015-07-04T00:00:00Z', None]]}},\n",
+ " 'id': 'SENTINEL2_L2A',\n",
+ " 'keywords': ['COPERNICUS',\n",
+ " 'ESA',\n",
+ " 'Orthoimagery',\n",
+ " 'Sentinel-2',\n",
+ " 'MSI',\n",
+ " 'Level-2A',\n",
+ " 'Radiometry',\n",
+ " 'Plant Resource',\n",
+ " 'TOC'],\n",
+ " 'license': 'proprietary',\n",
+ " 'links': [{'href': 'https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice',\n",
+ " 'rel': 'license',\n",
+ " 'type': 'application/pdf'},\n",
+ " {'href': 'https://documentation.dataspace.copernicus.eu/Data/Sentinel2.html',\n",
+ " 'rel': 'alternate',\n",
+ " 'type': 'application/html'},\n",
+ " {'href': 'https://dataspace.copernicus.eu/browser/?zoom=11&lat=45.36638&lng=12.49832&themeId=DEFAULT-THEME&visualizationUrl=https%3A%2F%2Fsh.dataspace.copernicus.eu%2Fogc%2Fwms%2F28b654e7-8912-4e59-9e58-85b58d768b3a&datasetId=S2_L2A_CDAS&fromTime=2023-02-07T00%3A00%3A00.000Z&toTime=2023-02-07T23%3A59%3A59.999Z&layerId=1_TRUE_COLOR&demSource3D=%22MAPZEN%22&cloudCoverage=10',\n",
+ " 'rel': 'browser',\n",
+ " 'title': 'CDSE Data-Browser'},\n",
+ " {'href': 'https://catalogue.dataspace.copernicus.eu/resto/api/collections/Sentinel2/search.json?cloudCover=[0,10]&productType=S2MSI2A&startDate=2022-06-11&completionDate=2022-06-22&maxRecords=10',\n",
+ " 'rel': 'opensearch',\n",
+ " 'title': 'Opensearch product query'},\n",
+ " {'href': 'https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/product-types/level-2a',\n",
+ " 'rel': 'about',\n",
+ " 'title': 'User guide'},\n",
+ " {'href': 'https://services.sentinel-hub.com/ogc/wmts/7d34803f-511c-4caf-9438-6d72f32c8174',\n",
+ " 'rel': 'wmts',\n",
+ " 'title': 'Web Map Tile Service',\n",
+ " 'wmts:dimensions': {'warnings': True},\n",
+ " 'wmts:layer': 'TRUE-COLOR'},\n",
+ " {'href': 'https://openeo.dataspace.copernicus.eu/openeo/1.2/collections',\n",
+ " 'rel': 'root'},\n",
+ " {'href': 'https://openeo.dataspace.copernicus.eu/openeo/1.2/collections',\n",
+ " 'rel': 'parent'},\n",
+ " {'href': 'https://openeo.dataspace.copernicus.eu/openeo/1.2/collections/SENTINEL2_L2A',\n",
+ " 'rel': 'self'}],\n",
+ " 'mission': 'Sentinel-2',\n",
+ " 'name': 'SENTINEL2_L2A',\n",
+ " 'providers': [{'name': 'ESA',\n",
+ " 'roles': ['producer'],\n",
+ " 'url': 'https://esa.int/'},\n",
+ " {'name': 'CDSE',\n",
+ " 'roles': ['processor', 'host'],\n",
+ " 'url': 'https://dataspace.copernicus.eu/'}],\n",
+ " 'stac_extensions': ['https://stac-extensions.github.io/datacube/v2.2.0/schema.json',\n",
+ " 'https://stac-extensions.github.io/eo/v1.1.0/schema.json'],\n",
+ " 'stac_version': '0.9.0',\n",
+ " 'summaries': {'constellation': ['sentinel-2'],\n",
+ " 'eo:bands': [{'aliases': ['IMG_DATA_Band_B01_60m_Tile1_Data'],\n",
+ " 'center_wavelength': 0.4427,\n",
+ " 'common_name': 'coastal',\n",
+ " 'full_width_half_max': 0.021,\n",
+ " 'gsd': 60,\n",
+ " 'name': 'B01',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_B02_10m_Tile1_Data'],\n",
+ " 'center_wavelength': 0.4924,\n",
+ " 'common_name': 'blue',\n",
+ " 'full_width_half_max': 0.066,\n",
+ " 'gsd': 10,\n",
+ " 'name': 'B02',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_B03_10m_Tile1_Data'],\n",
+ " 'center_wavelength': 0.5598,\n",
+ " 'common_name': 'green',\n",
+ " 'full_width_half_max': 0.036,\n",
+ " 'gsd': 10,\n",
+ " 'name': 'B03',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_B04_10m_Tile1_Data'],\n",
+ " 'center_wavelength': 0.6646,\n",
+ " 'common_name': 'red',\n",
+ " 'full_width_half_max': 0.031,\n",
+ " 'gsd': 10,\n",
+ " 'name': 'B04',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_B05_20m_Tile1_Data'],\n",
+ " 'center_wavelength': 0.7041,\n",
+ " 'full_width_half_max': 0.015,\n",
+ " 'gsd': 20,\n",
+ " 'name': 'B05',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_B06_20m_Tile1_Data'],\n",
+ " 'center_wavelength': 0.7405,\n",
+ " 'full_width_half_max': 0.015,\n",
+ " 'gsd': 20,\n",
+ " 'name': 'B06',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_B07_20m_Tile1_Data'],\n",
+ " 'center_wavelength': 0.7828,\n",
+ " 'full_width_half_max': 0.02,\n",
+ " 'gsd': 20,\n",
+ " 'name': 'B07',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_B08_10m_Tile1_Data'],\n",
+ " 'center_wavelength': 0.8328,\n",
+ " 'common_name': 'nir',\n",
+ " 'full_width_half_max': 0.106,\n",
+ " 'gsd': 10,\n",
+ " 'name': 'B08',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_B8A_20m_Tile1_Data'],\n",
+ " 'center_wavelength': 0.8647,\n",
+ " 'common_name': 'nir08',\n",
+ " 'full_width_half_max': 0.021,\n",
+ " 'gsd': 20,\n",
+ " 'name': 'B8A',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_B09_60m_Tile1_Data'],\n",
+ " 'center_wavelength': 0.9451,\n",
+ " 'common_name': 'nir09',\n",
+ " 'full_width_half_max': 0.02,\n",
+ " 'gsd': 60,\n",
+ " 'name': 'B09',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_B11_20m_Tile1_Data'],\n",
+ " 'center_wavelength': 1.6137,\n",
+ " 'common_name': 'swir16',\n",
+ " 'full_width_half_max': 0.091,\n",
+ " 'gsd': 20,\n",
+ " 'name': 'B11',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_B12_20m_Tile1_Data'],\n",
+ " 'center_wavelength': 2.2024,\n",
+ " 'common_name': 'swir22',\n",
+ " 'full_width_half_max': 0.175,\n",
+ " 'gsd': 20,\n",
+ " 'name': 'B12',\n",
+ " 'offset': 0,\n",
+ " 'scale': 0.0001,\n",
+ " 'type': 'int16',\n",
+ " 'unit': '1'},\n",
+ " {'aliases': ['IMG_DATA_Band_WVP_10m_Tile1_Data'], 'name': 'WVP'},\n",
+ " {'aliases': ['IMG_DATA_Band_AOT_20m_Tile1_Data'], 'name': 'AOT'},\n",
+ " {'aliases': ['IMG_DATA_Band_SCL_20m_Tile1_Data'], 'name': 'SCL'},\n",
+ " {'aliases': ['S2_Level-2A_Tile1_Metadata##0', 'SAA'],\n",
+ " 'name': 'sunAzimuthAngles',\n",
+ " 'unit': 'deg'},\n",
+ " {'aliases': ['S2_Level-2A_Tile1_Metadata##1', 'SZA'],\n",
+ " 'name': 'sunZenithAngles',\n",
+ " 'unit': 'deg'},\n",
+ " {'aliases': ['S2_Level-2A_Tile1_Metadata##2',\n",
+ " 'VAA',\n",
+ " 'approximateViewAzimuth'],\n",
+ " 'name': 'viewAzimuthMean',\n",
+ " 'unit': 'deg'},\n",
+ " {'aliases': ['S2_Level-2A_Tile1_Metadata##3',\n",
+ " 'VZA',\n",
+ " 'approximateViewZenith'],\n",
+ " 'name': 'viewZenithMean',\n",
+ " 'unit': 'deg'}],\n",
+ " 'eo:cloud_cover': {'maximum': 100, 'minimum': 0},\n",
+ " 'gsd': [10],\n",
+ " 'instrument': ['msi'],\n",
+ " 'platform': ['sentinel-2a', 'sentinel-2b']},\n",
+ " 'title': 'Sentinel-2 L2A'}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "conn.describe_collection(\"SENTINEL2_L2A\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ed91226f-a9ea-45d5-9663-98ed56fb067f",
+ "metadata": {},
+ "source": [
+ "## Define a workflow\n",
+ "We will define our workflow now. And chain all the processes together we need for analyzing the snow cover in the catchment.\n",
+ "\n",
+ "- Load a data cube with specific filters\n",
+ "- Calculate the Normalized Difference Snow Index\n",
+ "- Classify snow and no-snow using a threshold yielding the Snow Covered Area\n",
+ "- Create and apply a cloud mask to remove cloudy pixels\n",
+ "- Visualize one date of the snow map and crop it to the exact catchment outline\n",
+ "- Calculate catchment statistics to get a timeseries on snow cover and cloud cover\n",
+ "- Filter the time series by the cloud percentage and visualize the time series graph\n",
+ "- "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f786a79c-897d-4858-83c0-af9d4c4efede",
+ "metadata": {},
+ "source": [
+ "### Define the data cube\n",
+ "We define all extents of our data cube. We use the catchment as spatial extent. As a time range we will focus on the snow melting season 2018, in particular from Febraury to June 2018. We are only interested in the green and short wave infrared band, band 3 and 11. And we directly remove time slices with a cloud cover >= 90 %."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "139b411c-b401-4af8-9f7a-d046e24e00e1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "minx 11.020833\n",
+ "miny 46.653599\n",
+ "maxx 11.366667\n",
+ "maxy 46.954167\n",
+ "Name: 0, dtype: float64"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bbox = catchment_outline.bounds.iloc[0]\n",
+ "bbox"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "2a136b78-6eba-42df-a71b-f1b86c4a27ea",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from openeo.processes import lte\n",
+ "collection = 'SENTINEL2_L2A'\n",
+ "spatial_extent = {'west':bbox[0],'east':bbox[2],'south':bbox[1],'north':bbox[3],'crs':4326}\n",
+ "temporal_extent = [\"2018-02-01\", \"2018-06-30\"]\n",
+ "bands = ['B03', 'B11', 'SCL']\n",
+ "properties={\"eo:cloud_cover\": lambda x: lte(x, 90)}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "450db68c-ffdc-4037-9885-045afec86637",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Load the data cube\n",
+ "We have defined the extents we are interested in. Now we use these definitions to load the data cube."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "7d89755c-ef86-464f-a5e0-187bd44dfd53",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "s2 = conn.load_collection(collection,\n",
+ " spatial_extent=spatial_extent,\n",
+ " bands=bands,\n",
+ " temporal_extent=temporal_extent,\n",
+ " properties=properties)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f93a67e6-6746-4de7-9142-635eee9a1196",
+ "metadata": {},
+ "source": [
+ "### NDSI - Normalized Difference Snow Index\n",
+ "The Normalized Difference Snow Index (NDSI) is computed as:\n",
+ "\n",
+ "$$ NDSI = \\frac {GREEN - SWIR} {GREEN +SWIR} $$\n",
+ "\n",
+ "We have created a Sentinel-2 data cube with bands B03 (green) and B11 (SWIR). We will use the green and SWIR band to calculate a the NDSI. This process is reducing the band dimension of the data cube to generate new information, the NDSI."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "26cd85ae-fddf-42c5-975a-1e911420e063",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "green = s2.band(\"B03\")\n",
+ "swir = s2.band(\"B11\")\n",
+ "ndsi = (green - swir) / (green + swir)\n",
+ "ndsi"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "746ef0f4-9c9f-4884-8806-730d91261acb",
+ "metadata": {},
+ "source": [
+ "### Creating the Snow Map\n",
+ "So far we have a time series map of NDSI values. We are intereseted in the presence of snow though. Ideally in a binary classification: snow and no snow.\n",
+ "To achieve this we are setting a threshold of 0.4 on the NDSI. This gives us a binary snow map."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "591d44b1-20d1-447a-a956-291495f7a1c1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "snowmap = ( ndsi > 0.4 ) * 1.0 # the addition of \"* 1.00\" is a workaround for a backend specific implementation problem. Once solved on the CDSE openEO backend it could be removed\n",
+ "snowmap"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1b4ae963-05f1-48d0-937f-f162783f3fe3",
+ "metadata": {},
+ "source": [
+ "### Creating a cloud mask\n",
+ "\n",
+ "We are going to use the Scene Classification of Sentinel-2, called the \"SCL\" band, for creating a cloud mask and then applying it to the NDSI. The values we are interested in are: `8 = cloud medium probability`, `9 = cloud high probability`, `3 = cloud shadow`\n",
+ "\n",
+ "Here is more information on the Scene Classification of Sentinel-2: https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm-overview"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "99556d13-fae5-4726-a656-979a1945e7c1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scl_band = s2.band(\"SCL\")\n",
+ "cloud_mask = ( (scl_band == 8) | (scl_band == 9) | (scl_band == 3) ) * 1.0\n",
+ "cloud_mask"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b2219e0-12c1-487f-b95c-cd96743f866d",
+ "metadata": {},
+ "source": [
+ "The SCL layer has a ground sample distance of 20 meter while the other bands have 10 meter GSD"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e7d0d18-2c39-4b4f-b493-d0370d7f1c92",
+ "metadata": {},
+ "source": [
+ "### Applying the cloud mask to the snowmap\n",
+ "We will mask out all pixels that are covered by clouds. This will result in: 0 = no_snow, 1 = snow, 2 = cloud"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "8e188e75-57d6-4140-920d-937bea779934",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "snowmap_cloudfree = snowmap.mask(cloud_mask,replacement=2) # replacement is null by default\n",
+ "snowmap_cloudfree"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "61be5bad-fce6-46b3-85f3-cbfc15d309f0",
+ "metadata": {},
+ "source": [
+ "### Mask Polygon: From Bounding Box to Shape\n",
+ "We have a cloud masked snow map data cube now. In order to keep only pixels within the exact chatchment boundaries we mask to the outline of the catchment. Values outside of the boundaries are set to NA."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "09b8c9ff-0f11-43ae-81c7-c323365fe3ac",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/svg+xml": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "catchment_outline['geometry'][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "c6cc66f6-0f09-46e4-b687-f42703da647f",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "snowmap_cloudfree_masked = snowmap_cloudfree.mask_polygon(catchment_outline['geometry'][0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ce3b6b14-7f09-456d-9162-8c5f688c67e5",
+ "metadata": {},
+ "source": [
+ "### Visualize one time step of the timeseries\n",
+ "Let's have a first look at a time slice of our snow map. So far we have not computed anything. We have only defined a set of functions that are going to be applied in sequence. This makes up our workflow or processing graph. \n",
+ "To reduce the data volume which we are going to download we are only selecting one time step of our data cube.\n",
+ "\n",
+ "**In order to start the processing we have to tell the cloud platform specifically that we want to execute our workflow. In this case we want to start the processing directly without registering a job on the backend. This solution is good for small amounts of data. For larger processing tasks batch jobs are preferred (we'll do that later).**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "891968e3-ddfe-4dad-bba7-af22f8a5f16e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "snowmap_cloudfree_1d = snowmap_cloudfree_masked.filter_temporal('2018-02-10', '2018-02-12')\n",
+ "snowmap_cloudfree_1d.download('31_results/snowmap_cloudfree_1d.nc')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25a5d46c-4778-4c8a-bdbe-8041c7530f12",
+ "metadata": {},
+ "source": [
+ "Once the processing is done on the cloud and the data is downloaded we can load the file into our working environment and plot it!\n",
+ "\n",
+ "The area of interest is spread across two S2 tiles. This is visibile in the northern part of the plot because we chose one specific acquisition date where there is not data available for the northern tile."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "405e1ee1-febf-4ec6-83d4-5bc644756169",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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4LEHI0kQQBEEQhGbcunULNWrUwIEDB9ChQwfBOv/5z3+wdetWnD171lg2cuRInDx50pikNzk5GUVFRdixY4exTo8ePVCtWjWsX7/ethchAq2esxK9Xo8bN26gSpUqVm24ShAEQXgOjDHcu3cP4eHhsjZ7tpaHDx+ipKREdT+MMbNnm6+vr6yM+oWFhQAgGZObmZmJbt26mZR1794dK1euxKNHj1CxYkVkZmaa7U7QvXt3LFy4UOZVaA+JJiu5ceMG6tSp4+hpEARBEC5EXl6e7M2jlfLw4UNERgSg4GaZ6r4CAgJw//59k7Lp06cjJSVFsh1jDBMmTEC7du0QExMjWq+goAChoaEmZaGhoSgtLcXt27dRs2ZN0ToFBQXKLkZDSDRZiWGrjry8PFlbSBAEQRCeS1FREerUqSO6zZMWlJSUoOBmGa4eq4fAKtZbs4ru6RERd8Xs+SbHyjR69GicOnUKhw8ftliXb8kyRAtxy4XqONK7Q6LJSgwfWmBgIIkmgiAIQhb2eOAHVNEhoIr14+hh3fPt9ddfx9atW3Hw4EGL1rSwsDAzi9HNmzdRoUIF436NYnX41id7QqvnCIIgCMKNKGN61YcSGGMYPXo0Nm7ciL179yIyMtJim8TERKSnp5uU7dq1C61bt0bFihUl6yQlJSman5aQpYkgCIIg3Ag9GPSwfmG80rajRo3Cl19+iS1btqBKlSpG61BQUBAqVaoEAJg8eTKuX7+ONWvWAChfKbdkyRJMmDABI0aMQGZmJlauXGmyKm7s2LHo0KEDPvjgA/Tu3RtbtmzB7t27Zbn+bAVZmgjCxejq9YLxIAiCcDTLli1DYWEhOnXqhJo1axqPtLQ0Y538/Hzk5uYa30dGRmL79u3Yv38/WrZsiXfffReLFy/Gc889Z6yTlJSEr776CqtXr0bz5s2RmpqKtLQ0xMfH2/X6uFCeJispKipCUFAQCgsLKaaJsCt8sZSu/8ZBMyEIQi72eGYYxrhxvrbqQPDwRtfo+SYAuecIwoUg6xJBEJYoYwxlKuwhatq6O+SeIwgXQUwwkZAiCIKwD2RpIggXgIQRQRBysXcguCdBliaCcHLkCCYSVQRBGNCDoUzFQaJJHBJNBOHEKBFDJJwIgiBsC4kmgnAjSDgRBGFwz6k5CGFINBGEm0HCiSA8G8PqOTUHIQwFghOEG8IVTpTHiSAIQhtINBGEk6KVxYiSYRKEZ6H/36GmPSEMuecIwsMg9x1BuDdqVs4ZDkIYEk0E4YGQcCII96WMqT8IYUg0EYSHQsKJIAhCGRTTRBBOiL0EDQWME4T7QTFNtoNEE0E4GY6yAFHAOEG4B3roUAadqvaEMOSeIwgnwplcZs40F4IgCGeALE0E4SSQSCEIQgv0rPxQ054QhkQTQTgBziqYDPMiVx1BuA5lKt1zatq6OySaCMLBOKtg4iI0RxJSBEF4GhTTRBCEVbiC2CMIT8RgaVJzEMI4VDSlpKRAp9OZHGFhYaL1N27ciK5duyIkJASBgYFITEzEzp07zept2LAB0dHR8PX1RXR0NDZt2mRWZ+nSpYiMjISfnx/i4uJw6NAhTa+NIOTg6sLD1edPEO6InulUH4QwDrc0NW3aFPn5+cbjl19+Ea178OBBdO3aFdu3b8exY8fQuXNn9OrVCydOnDDWyczMRHJyMgYNGoSTJ09i0KBB6N+/P44ePWqsk5aWhnHjxmHKlCk4ceIE2rdvj549eyI3N9em10oQXNxFcLjLdRAEQVhCxxhzWJx8SkoKNm/ejKysLKv7aNq0KZKTk/HOO+8AAJKTk1FUVIQdO3YY6/To0QPVqlXD+vXrAQDx8fFo1aoVli1bZqzTpEkT9OnTB3PmzJE1blFREYKCglBYWIjAwECr5094Ju4oNCjGiSDEscczwzDGgV9rIaCK9TaR+/f06BhznZ5vAjjc0pSdnY3w8HBERkZiwIABuHz5suy2er0e9+7dQ3BwsLEsMzMT3bp1M6nXvXt3ZGRkAABKSkpw7NgxszrdunUz1hGiuLgYRUVFJgdBWIM7CibAfa+LIFyNMnipPghhHLp6Lj4+HmvWrMHjjz+O3377De+99x6SkpJw+vRpPPbYYxbbz5s3Dw8ePED//v2NZQUFBQgNDTWpFxoaioKCAgDA7du3UVZWJllHiDlz5mDGjBlKLo8gAAD6goYm73feKP83Km2kA2ZjWyIXzUPO2ImOngZBeDRMZVwSo5gmURwqJ3v27InnnnsOzZo1Q5cuXbBt2zYAwOeff26x7fr165GSkoK0tDTUqFHD5JxOZ/qBM8bMyuTU4TJ58mQUFhYaj7y8PItzJJwPvoCx9Vhi47mjYDIQuWieo6dAEARhE5wqT5O/vz+aNWuG7OxsyXppaWkYNmwYvvnmG3Tp0sXkXFhYmJnF6ObNm0bLUvXq1eHt7S1ZRwhfX1/4+voquRzCyTAIGH1BQ3iFSX/HtBqLIAjC3lByS9vhVI7L4uJinD17FjVr1hSts379egwdOhRffvklnnnmGbPziYmJSE9PNynbtWsXkpKSAAA+Pj6Ii4szq5Oenm6sQ7gffBFjS1Ej1XdU2ki3tjIRBOF4ypiX6oMQxqGWpkmTJqFXr16oW7cubt68iffeew9FRUUYMmQIgHKX2PXr17FmzRoA5YJp8ODBWLRoERISEozWokqVKiEoKAgAMHbsWHTo0AEffPABevfujS1btmD37t04fPiwcdwJEyZg0KBBaN26NRITE7FixQrk5uZi5Eh6mLkj5e4i08/2UvJym1icDGNdSl5uUk5CiSAIwvVxqGi6du0aXnzxRdy+fRshISFISEjAkSNHEBERAQDIz883yZ306aeforS0FKNGjcKoUaOM5UOGDEFqaioAICkpCV999RWmTp2KadOmISoqCmlpaYiPjzfWT05Oxp07dzBz5kzk5+cjJiYG27dvN45LuA9i8TV/ixjlgctiq8QuLkgQ6N8zaDD+iMn1EwThOPTQQa/CkaQH7dgrhkPzNLkylKfJMfBFUIPxR0ze77yRZfLeGvHCtxIJwe3XMAcSDX9DK+gIwhR75mnaeioK/lW8re7nwb0yPNv8Ej3fBCDHJeHS8IUKXyRdSl4uSwRJ9SFnDiSYCIIg3B+nWj1HEEoRsvJEpZnHFF1KXq5IDHHrUnwSQRCuhNpg7jJyQIlCoolwaexh4SGRRBCEK1Ee02R92gA1bd0dEk2EW0JChyAIgtAaEk0EQRAE4UboVe4fR6vnxKFAcIL4Hw3GHzFbjUcQBOFqOCK55cGDB9GrVy+Eh4dDp9Nh8+bNkvWHDh0KnU5ndjRt2tRYJzU1VbDOw4cPFc9PK0g0EcT/oFVw2kH7zxGE49DDS/WhlAcPHqBFixZYsmSJrPqLFi1Cfn6+8cjLy0NwcDBeeME0D15gYKBJvfz8fPj5+Smen1aQe44gCIIgCFX07NkTPXv2lF0/KCjIuJMHAGzevBl//PEHXnnlFZN6Op0OYWFhms1TLWRpIlwGsl4QBEFYpozpVB9AebJM7lFcXGyzOa9cuRJdunQx25nj/v37iIiIQO3atfGPf/wDJ06csNkc5ECiiXAJSDARBEHIo+x/geBqDgCoU6eO0SIUFBSEOXPm2GS++fn52LFjB4YPH25S3rhxY6SmpmLr1q1Yv349/Pz80LZtW2Rna7tnqBLIPUc4PSSYCIIg7E9eXp7JNiq+vr42GSc1NRVVq1ZFnz59TMoTEhKQkPB3nGnbtm3RqlUrfPzxx1i8eLFN5mIJEk2EU0OCiSAIQhl65gW9iozg+v9lBA8MDLT53nOMMaxatQqDBg2Cj4+PZF0vLy+0adOGLE0EQRAEQWgD18VmXXv75Wk6cOAALl68iGHDhlmsyxhDVlYWmjVrZoeZCUOiiXBayMpEEAThGty/fx8XL140vs/JyUFWVhaCg4NRt25dTJ48GdevX8eaNWtM2q1cuRLx8fGIiYkx63PGjBlISEhAw4YNUVRUhMWLFyMrKwuffPKJza9HDBJNhFNCgokgCMI69IBxBZy17ZXy888/o3Pnzsb3EyZMAAAMGTIEqampyM/PR25urkmbwsJCbNiwAYsWLRLs8+7du3j11VdRUFCAoKAgxMbG4uDBg3jiiSesmKE2kGginA4STARBENZjbYJKbnuldOrUCYyJu/VSU1PNyoKCgvDnn3+KtlmwYAEWLFigeC62hFIOEARBEARByIAsTYRTQVYmgiAIdVi7fxy3PSEMiSbCaSDBRBAEoR49dNBDTUyT9W3dHRJNBEEQBOFGkKXJdtCdIQjCJpDlkCAId4NEE+FxNBh/xNFT8BhIOBGE/dFq7znCHLozhNOQM3aiXca5uCDBciVCM0g4EYR90TOd6oMQhkQTQRA2h4QTQRDuAIkmgiDsAgkngrAPepWuOTWJMd0dWj1HEARBEG6EnnlBr2IFnJq27g7dGYIgCIIgCBmQpYkgCIIg3Igy6FCmIkGlmrbuDokmgiAIgnAjyD1nO+jOEIQH0GD8EcpPRRAEoRKyNBGEB+AMuanslYeLIDydMqhzsZVpNxW3gyxNBOEGOLsViQQTQdgPg3tOzUEIQ3eGcCro4WodzmBJEoM+U4KwL4YNe9UchDB0ZwjCgTi7hYggCIL4G4ppIggH4swWIi0gKxNB2B8GHfQqYpoYpRwQhSxNBOFh2Mu6RYKJIBwDuedsB90ZgvAw3N26RRAEYStINBEEoQn8XFC0QS9BOAY906k+CGEopolwOnLGTqQHrgtCFiyCcA7K4IUyFTYRNW3dHbozhFNC8TAEQRCEs0GiiSA0gtIHmEMWQ4KwP+Sesx0kmginxVWsTQaxdHFBAu3xRhCEw9HDS/VBCEN3hnBqXEE4kViSJnLRPLI4EQThFjhUNKWkpECn05kcYWFhovXz8/Px0ksvoVGjRvDy8sK4cePM6jx69AgzZ85EVFQU/Pz80KJFC3z//feqxiUch6s8bC8uSDAGQhtEFEEQhCMoYzrVByGMw1fPNW3aFLt37za+9/b2Fq1bXFyMkJAQTJkyBQsWLBCsM3XqVKxbtw6fffYZGjdujJ07d6Jv377IyMhAbGysVeMSjsFVBBNfIBneNxh/hFaUcYhcNM8lLIcE4eqojUuimCZxHC6aKlSoINvKU69ePSxatAgAsGrVKsE6a9euxZQpU/D0008DAP79739j586dmDdvHtatW2fVuIT9cRXBBPy91J4vnrjlfPHkCEHFnR+JOYJwXxjzgl5FVm9GGcFFkSWaFi9erLjjV155BVWqVLFYLzs7G+Hh4fD19UV8fDxmz56N+vXrKx7PQHFxMfz8/EzKKlWqhMOHD6sat7i4GMXFxcb3RUVFVs+RcG+ExBJfJDlCRDmDUCJrE0EQrows0TRu3DjUrl1btgsrLy8P//jHPyyKpvj4eKxZswaPP/44fvvtN7z33ntISkrC6dOn8dhjj8kai0/37t0xf/58dOjQAVFRUdizZw+2bNmCsrIyVePOmTMHM2bMsGpOhDJcycpkCYMYkuOy467C03JsgiA8izLoUKZi0101bd0dHWOMWark5eWFgoIC1KhRQ1anVapUwcmTJxVbjB48eICoqCi8+eabmDBhgmTdTp06oWXLlli4cKFJ+a1btzBixAh8++230Ol0iIqKQpcuXbB69Wr8+eefVo8rZGmqU6cOCgsLERgYqOg6CXFcXTDJDQAXEjNau/EsteWft5fIIksT4YkUFRUhKCjIps8Mwxiv7O8PnwAfq/spuV+C1Z2+puebALIcl9OnT0dAQIDsTt9++20EBwcrnoy/vz+aNWuG7OxsxW0NhISEYPPmzXjw4AGuXr2Kc+fOISAgAJGRkarG9fX1RWBgoMlBaIurCyYAZi44/qo6LoY0Bfx0Bdz3ckSMmFCzZNGyZiyCIAhPRrZoqly5suxOJ0+ejKpVqyqeTHFxMc6ePYuaNWsqbsvHz88PtWrVQmlpKTZs2IDevXvbZVzCOpxNMFmbMkBoJR3fNSeVksAaAcPvT+g1PwicOxcxq5etcLbPmiDcDf3/AsHVHIQwDr0zkyZNwoEDB5CTk4OjR4/i+eefR1FREYYMGQKgXHwNHjzYpE1WVhaysrJw//593Lp1C1lZWThz5ozx/NGjR7Fx40ZcvnwZhw4dQo8ePaDX6/Hmm2/KHpewL874EBUTEpaSWPLdXUJ9yMFSPaF5CGUm5wokJavnbG11csbPnCDcBT10qg+lHDx4EL169UJ4eDh0Oh02b94sWX///v1m+RJ1Oh3OnTtnUm/Dhg2Ijo6Gr68voqOjsWnTJsVz0xLFounOnTsYNWoUoqOjUb16dQQHB5scSrh27RpefPFFNGrUCP369YOPjw+OHDmCiIgIAOXJLHNzc03axMbGIjY2FseOHcOXX36J2NhYY3oBAHj48CGmTp2K6Oho9O3bF7Vq1cLhw4dNLF+WxiXsh7M9PKUsQFKWGS7880JuOUtt5CAl7LjnxdyDlqAEnQRByOXBgwdo0aIFlixZoqjd+fPnkZ+fbzwaNmxoPJeZmYnk5GQMGjQIJ0+exKBBg9C/f38cPXpU6+nLRlYgOJeePXvi0qVLGDZsGEJDQ6HTmSpST7HW2COoz91xNsEkhZB7i/veUlsxi481fQrlg7JlXJKtA8QpMJzwBOwZCP7S3pdUB4J/+eSXVs9Vp9Nh06ZN6NOnj2id/fv3o3Pnzvjjjz9Ew3mSk5NRVFSEHTt2GMt69OiBatWqYf369YrnpQWKk1sePnwYhw8fRosWLWwxH8IDcCWxBJjnW5ISP0Lwt1fh/mvNXPh9S81HCzGlZr4EQdgftXFJ9oxpio2NxcOHDxEdHY2pU6eic+fOxnOZmZkYP368Sf3u3bubrZq3J4pFU+PGjfHXX3/ZYi6EG+NqQomLkLtNKJ7IUgZwbl9CsU9imcX59eTOU47QkSuGbCmcDN8NsjgRhHPBT+Ls6+sLX19fTfquWbMmVqxYgbi4OBQXF2Pt2rV46qmnsH//fnTo0AEAUFBQgNDQUJN2oaGhKCgo0GQO1qBYNC1duhRvvfUW3nnnHcTExKBixYom58lVRXBxZrGkhQgQSlwp5i4TEkVCViixOvzXlvoVE1HccZSu0iMIwvnRQ+Xec/8LBK9Tp45J+fTp05GSkqJmakYaNWqERo0aGd8nJiYiLy8PH330kVE0ATALAWKMmZXZE8U2uKpVq6KwsBBPPvkkatSogWrVqqFatWqoWrUqqlWrZos5EoRNsDb4mr9KjYuU2JEK0Fbq8pOan5xAdX4qAqHUBPbGmQU2QbgSTOXKOfY/0ZSXl4fCwkLjMXnyZJvOOyEhwSRfYlhYmJlV6ebNm2bWJ3uiWDQNHDgQPj4++PLLL7Fnzx7s3bsXe/fuxb59+7B3715bzJEgnAqhhJVC7jZL+ZK474XcX3wBxB9DbINgqXlzX4tZ2kg4EYRro2c61QcAs4TOWrnmxDhx4oRJvsTExESkp6eb1Nm1axeSkpJsOg8pFLvnfv31V5w4ccLErEYQngjXHQfISy0g1J77Wo5g4Ysxa1fPOau7jTb1JQjX4/79+7h48aLxfU5ODrKyshAcHIy6deti8uTJuH79OtasWQMAWLhwIerVq4emTZuipKQE69atw4YNG7BhwwZjH2PHjkWHDh3wwQcfoHfv3tiyZQt2796Nw4cP2/36DCi2NLVu3Rp5eXm2mAtBuBRyN97lvpdj4ZHjYhOai5DbTQlyMpYTBOH8OCIj+M8//2zMowgAEyZMQGxsLN555x0A5nkXS0pKMGnSJDRv3hzt27fH4cOHsW3bNvTr189YJykpCV999RVWr16N5s2bIzU1FWlpaYiPj1d5h6xHcZ6mb775BikpKXjjjTfQrFkzs0Dw5s2bazpBZ4XyNFnG010tYluXiAV885ErnLRc1SY0N3tbpMjKRLgj9szT1HvXP1HR3/o8TY8elGBLt1X0fBNAsXsuOTkZAPDPf/7TWKbT6YwR7WVlZdrNjiBsjL3yD8kJuBZzs1mao1SqA6VY4y7UEhJMBEE4M4ptcDk5OWbH5cuXjf8ShCthK8EkR8AIxULxXXNK3G22vBZ7QIKJILTBEXvPeQqKLU20PxvhDtjawsRPJyCnHn9+QuViiCXUtPYa5c5fK0gwEYR2cFfAWdueEEaWpWnr1q149OiR7E63b99OWcMJj0FKVCjZ6NdSckp+XaHNefnjCqUmUIqzrrIjCIKwN7ICwb29vVFQUICQkBBZnQYGBiIrKwv169dXPUFnhQLBLePJgeBiQkWr7UjUpBpwRsjSRLg79gwE7/n9CNWB4Dt6fEbPNwFkuecYYxg6dKjsxFYPHz5UNSmCcFe0cgu6k2AiCEJbyD1nO2SJpiFDhijqdODAgaROPRxPtjJZwl4r9lwJSmhJEIQrIEs0rV692tbzIAi3QG6CSDXCSWx/O0dBIpAgnAuyNNkOxavnCMISnmxl4osHW6w+E0pJ4EjRQoKJIJwLBqhKG6Ao47WHQaKJ0BRPFkwG+EJGKPmkGiHF7YcEC0EQfMjSZDuUbzBDECKQYCpHLEElP4mlUvgb9RIEQRD2hSxNhCaQYBJGLOmkATniSUgsabl1CkEQ7gVZmmwHWZoIt8fRVhn++ELJLpVu3KtF0kqCINwTg2hScxDCWGVp2rNnD/bs2YObN29Cr9ebnFu1apUmEyNcB2e3MjlTkLSYlUlsjlzLklYxUQRBEIR1KLY0zZgxA926dcOePXtw+/Zt/PHHHyYH4Vk4u2ByJviuNKXCh++S474nEUUQhAGyNNkOxZam5cuXIzU1FYMGDbLFfAgXggSTZeSKGbHYJClLldh5KasVP5M4xUQRhPvBmA5MhfBR09bdUSyaSkpKkJSUZIu5EC4ECSbLyLUsGcqlgsal0hfw+7bkAlS7io8gCMJTUeyeGz58OL788ktbzIVwEUgwyUPIfSaVfkDM0iTUj1ASTX4bIVFFrjyCcH/00Kk+CGEUW5oePnyIFStWYPfu3WjevDkqVqxocn7+/PmaTY4gXB0hgaLU4sOPYbI0hpAbzhaZyQmCcE4o5YDtUCyaTp06hZYtWwIAfv31V5NzOh3daHeHrEzysCR0lPYjNyUB39UnZVnir8YjVx1BEIQ0OsYYbTNjBUVFRQgKCkJhYSECAwMdPR27QIJJG/iB2IbXUnWtRSxFgaVyR5EzdqJDxycIW2GPZ4ZhjCc2jUUFf1+r+yl9UIwf+y7yqOebXFSJpmvXrkGn06FWrVpazskl8DTRRIJJPZYEkKVVb0r6kjOOWB/OsKqOxBPhbthTNLXeOE61aPq530KPeb4pQbF7Tq/X47333sO8efNw//59AECVKlUwceJETJkyBV5elGScIAB5wkaOMJFyz/HP8d1tltx6YpYmR1ucIhfNI+FEEFZCKQdsh2LRNGXKFKxcuRLvv/8+2rZtC8YYfvjhB6SkpODhw4eYNWuWLeZJEC4FX8iotdzIFUBSm/pKjc9t72jBZICEE0EQzoZi91x4eDiWL1+OZ5991qR8y5YteO2113D9+nVNJ+iskHuOsBdiQkbMwsQtk9NWqA9HueikxiQBRbgy9nTPtfq/CfBW4Z4re1CM48/P95jnmxIUW5p+//13NG7c2Ky8cePG+P333zWZFEF4CpYyeIvVF0szwF9JJ7SyztJmwVwXnb2Fk9RYWgl3El+Eu8MAqFniRavDxFEcgNSiRQssWbLErHzJkiVo0aKFJpMinA960NgGOQkoxaxIfAztxNx03HxNUmkILI3jaNSuKCSrKUEQ1qLY0jR37lw888wz2L17NxITE6HT6ZCRkYG8vDxs377dFnMkCLeDn2pATuC2WHuptpbO8XFGkcRHizlSvBThzuihg05FVm/KCC6OYktTx44dceHCBfTt2xd3797F77//jn79+uH8+fNo3769LeZIEC6PmOXH8J4rfiztPyfUl6UYJcIcsjgR7oph9ZyagxCGkltaiacFggP0kFGDktggqfQC3PNy4pKsEUxirj8l5a4EWZwIe2DPQPDm30yCd2UVgeB/FuPUCx951PNNLrIsTadOnYJerze+ljoIgihHKi5JaBNffl0p5KYPEKsvtsGv0iSari6YAPoxQLgfhr3n1ByEMLIsTV5eXigoKECNGjXg5eUFnU4HoWY6nQ5lZWU2maiz4YmWJoAeMPbAmoSUYlnDLcU8KcHd96kjixNhS+xpaWqa9oZqS9Pp5A897vkmB1mB4Dk5OQgJCTG+JgjCcfBFkpQ4kop5Ujqm0Gt3goLDCYKwhCzRFBERYXx99epVJCUloUIF06alpaXIyMgwqUu4HzljJ5K1SSP4woePVM4lIasP/185rjW56QWcPQ2BVpBwItwB2kbFdigOBPf29kZ+fj5q1KhhUn7nzh3UqFGD3HMeAgkn7VDj7hIKCpc6b63FyZ2Fki0g4UXwsad7rsn6/6h2z5198QOPfb5JoTjlAGMMOp25Cr1z5w78/f0V9ZWSkgKdTmdyhIWFidbPz8/HSy+9hEaNGsHLywvjxo0zq/Po0SPMnDkTUVFR8PPzQ4sWLfD999+b1Vu6dCkiIyPh5+eHuLg4HDp0SNHcPR16KGiHEkEiN/kkfx857ntL4/GDwkkwKSdy0TzJQ25bgrAGRwSCHzx4EL169UJ4eDh0Oh02b94sWX/jxo3o2rUrQkJCEBgYiMTEROzcudOkTmpqqplG0Ol0ePjwoeL5aYXs5Jb9+vUDUB7sPXToUPj6/q1iy8rKcOrUKSQlJSmeQNOmTbF7927je29vb9G6xcXFCAkJwZQpU7BgwQLBOlOnTsW6devw2WefoXHjxti5cyf69u2LjIwMxMbGAgDS0tIwbtw4LF26FG3btsWnn36Knj174syZM6hbt67iayAIW8FPYilVj+++E7NCWSoX2n5Fqj9COWJuQL5QInch4So8ePAALVq0wCuvvILnnnvOYv2DBw+ia9eumD17NqpWrYrVq1ejV69eOHr0qPFZDQCBgYE4f/68SVs/Pz/N5y8X2e65V155BQDw+eefo3///qhUqZLxnI+PD+rVq4cRI0agevXqsgdPSUnB5s2bkZWVpWzWADp16oSWLVti4cKFJuXh4eGYMmUKRo0aZSzr06cPAgICsG7dOgBAfHw8WrVqhWXLlhnrNGnSBH369MGcOXNkje/p7jkD9GtYO5TuQye2r5xU/0JtxcYQW41HaItBFEn9XyLh5PrY0z33+BdvqXbPXRj4vtVz1el02LRpE/r06aOoXdOmTZGcnIx33nkHQLmlady4cbh7967iOdgK2Zam1atXAwDq1auHN954A5UrV9ZkAtnZ2QgPD4evry/i4+Mxe/Zs1K9f3+r+iouLzVRopUqVcPjwYQBASUkJjh07hrfeesukTrdu3ZCRkWH1uAShFqmUAWJwXW5iliKxdvyxpTboJcFkO+T88PCEHyckDLWDMXXB3I5Iea3X63Hv3j0EBweblN+/fx8REREoKytDy5Yt8e6775pYouyN4pimwYMH4/r162bl2dnZuHLliqK+4uPjsWbNGuzcuROfffYZCgoKkJSUhDt37iidlpHu3btj/vz5yM7Ohl6vR3p6OrZs2YL8/HwAwO3bt1FWVobQ0FCTdqGhoSgoKBDtt7i4GEVFRSaHp+MJf8gdhZBg4scjSaUdEIIbo8SPW+LHQAnNhSBsCf09cT74z7zi4mKbjTVv3jw8ePAA/fv3N5Y1btwYqamp2Lp1K9avXw8/Pz+0bdsW2dnZNpuHJRSLpqFDhwpaZI4ePYqhQ4cq6qtnz5547rnn0KxZM3Tp0gXbtm0DUO4CtJZFixahYcOGaNy4MXx8fDB69Gi88sorZrFS/GB2sQB3A3PmzEFQUJDxqFOnjtVzJAgp+LmVuFYgS1uscNsJvZeTLVyqb4KwJSSctEGrvefq1Klj8tyTG76ilPXr1yMlJQVpaWkmK/MTEhLw8ssvo0WLFmjfvj2+/vprPP744/j4449tMg85KBZNJ06cQNu2bc3KExISrIpN4uLv749mzZqpUpEhISHYvHkzHjx4gKtXr+LcuXMICAhAZGQkAKB69erw9vY2syrdvHnTzPrEZfLkySgsLDQeeXl5Vs+RIORiSSxx4Se1lBJYQtuqONMGv7ZI0Em4BiSc1MM0OAAgLy/P5Lk3efJkzeealpaGYcOG4euvv0aXLl0k63p5eaFNmzauZWnS6XS4d++eWXlhYaHqHE3FxcU4e/YsatasqaofoDy6vlatWigtLcWGDRvQu3dvAOVB63FxcUhPTzepn56eLrn6z9fXF4GBgSaHJ0N/2OyDkhVrUsHdloSX4bw9hInYWELiSM6cCfeD/r44B/xnHnfVvBasX78eQ4cOxZdffolnnnnGYn3GGLKysjTRCNaiWDS1b98ec+bMMRFIZWVlmDNnDtq1a6eor0mTJuHAgQPIycnB0aNH8fzzz6OoqAhDhgwBUG7dGTx4sEmbrKwsZGVl4f79+7h16xaysrJw5swZ4/mjR49i48aNuHz5Mg4dOoQePXpAr9fjzTffNNaZMGEC/vvf/2LVqlU4e/Ysxo8fj9zcXIwcOVLp7fBI6A+abeC744TOcbHGbcYXJo4QHnJdify8UkKWsJ03skzqk4hyH+jvjPVo5Z5Twv37943PZ6B8y7WsrCzk5uYCMH+er1+/HoMHD8a8efOQkJCAgoICFBQUoLCw0FhnxowZ2LlzJy5fvoysrCwMGzYMWVlZDn1Wy149Z2Du3Lno0KEDGjVqhPbt2wMADh06hKKiIuzdu1dRX9euXcOLL76I27dvIyQkBAkJCThy5IhxK5b8/HzjDTfAjZo/duwYvvzyS0RERBiD0B8+fIipU6fi8uXLCAgIwNNPP421a9eiatWqxnbJycm4c+cOZs6cifz8fMTExGD79u20BQzhUJS6x4TikuRszKtkA19b5Gbi556Sm7VcSFB1Dwcu3ViOqDT6weOOUJ4qK+H62Kxtr5Cff/4ZnTt3Nr6fMGECAGDIkCFITU01e55/+umnKC0txahRo0xSBBnqA8Ddu3fx6quvoqCgAEFBQYiNjcXBgwfxxBNPWHddGqB4GxUAuHHjBpYsWYKTJ0+iUqVKaN68OUaPHm22VNCd8dQ8TfTrz3bIzcUkhBLBZUkwWVqFpwVy98UT2ohY6pyBnTeyEJU2UjRRp7MjljPLkfO/lLwcABwiUN1BONkzT1P91Cnwqmx9Akj9nw9xeegsj3u+yUGxew4oTyA5e/ZsbNu2Df/3f/+Hd955x6MEE0HYAktbovDrWLvNiVQaA6Fx1bq8+H0osXAJ5Z7ivua654Dy6zGUGR7y3D5dQTABpp8L1yKnZv5K4sOE6kaljXSYRY9+rBHOgmL3HFDujvv0009x+fJlfPPNN6hVqxbWrl2LyMhIxXFNhGuRM3Yi/QGzEXxhwP1XaIsUOakFxBBaPcfvR8yioyRTuNA5uVYxS1u3NBh/BFEYaVKfL5TUiiRHCy0pa5q1fQn1LVSXj5DFD/hbnHYPbynZXi1K/u64g2VKDeXJLdW1J4RR7J7bsGEDBg0ahIEDB2Lt2rU4c+YM6tevj6VLl+K7777D9u3bbTVXp8JT3XMGSDjZDqFYJTkCQ6yekgSYUnPijyH2ILeVNUQqOH7njSzjQ5v7mttO7KFvzRzt4cIUGlNtW7HPS8wdyO+Hf67B+CMm1j7DfdfiPmuNo4WUPd1z9VZNVe2eu/LP9zz2+SaFYtEUGxuL8ePHY/DgwahSpQpOnjyJ+vXrIysrCz169JDMqu1OeLpoAkg42RJrrAtyhRP3vdT4alHysBQSikraWGrLF3qGBz3X3WSowxdd3H65VhWpeVp7PWJiQ46okZqDFFIWRe49AWAWJyZ2nXwxJRRf5ggcKZxINLkHimOazp8/jw4dOpiVBwYGOtWmeoTtcfQvN3eA74YzIOU+EytTGrMiFa9kiKnhxzzJfejJXcUndN1i90QuYv1yX3cPb2l8kAuNs/NGlsnBr7fzRhYuJS+XvH9K4YoVSyJX6PNTG4NmEDrc+2+4fuBv4cOfF7eO2NhqP1Ot8Jgfekyn/iAEURzTVLNmTVy8eBH16tUzKT98+LCqjXYJ14RinOQjFd8j5Q7hv5YqU4rY6jS140g9JMVEmNC9ELNOyJnrpeTlJg96oT4MD/vu4ZKXY7Q+RaWNNImbKl+lJyyQ1LjT+O/liGYhcaN0vO7hLY3XWi4u/6536cZyIPnvOkCWsY3U99iSVc7eGP5eufOPPoppsh2KLU3/+te/MHbsWBw9ehQ6nQ43btzAF198gUmTJuG1116zxRwJwmUREg9yViopdWUYrBNK40ik5sedJ79/sTnIHdOS+1GOSOSLK6EYJ6HAZ2sxWGG4rjvDa+59tFawKK1nKLuUvNzEbch3qQHSnw3fqsh1WXJXKXJjxgz8LaBM4ZYJnXcGIhfNMzkIQg5W5WmaMmUKFixYgIcPHwIo32Jk0qRJePfddzWfoLNCMU2mePIfHSVxQvwAYn5ba2KZ+AgFKdsiRklpvJXSGBtLba2J+RISFAZLiNA5Pvy6hn6FsCR8hT5roc9M6jrlCBJ+fJbQnKUsd39b41qajGkQV5eSl5tYk/grGA3wLWBaB9Nr1ZetLFD2jGmK+Gya6pimqyPepeebAFaJJgD4888/cebMGej1ekRHRyMgIEDruTk1JJrM8WThZAmx4FkDQm4y7jmxdvz2Um34lii1wswSUvE+1lyLVJ9chAK5uee4CLmyLAkRQxvDOEKJNLnn+dcg91rkfEZy5sofy1KwPLcOX+gLuXK5wkoKvutPynLoSNxBNNVd8Y5q0ZT76kx6vglgtWjydEg0CePpwon/MJH7wLdGxNjK8mPtXKSsZHItKob3aufHFTRSCIkrpe4kSyvKbGU1E7PocOdlQI5o4tezVFeorVD6AW5fBquUGNZa7bSERBOJJilkiaZ+/fohNTUVgYGB6Nevn2TdgIAANG3aFCNHjkRQUJBmE3U2SDSJ48nCSeyXuNyHpxLkBExzzyvpV44VRGwcITGn1LokNJbS+2Wt60qpaBILdOa6rawVofzXcufp6L34+JY2A2JWQEuuOikrqtZuPsA2wsnuoqmSCtH0F4kmMWStngsKCoJOpzO+lqK4uBjLly/HDz/8gK1bt6qfIUE4GVIxTNxVRGIBz/x2WgkovrWDO44S4SG3jpwVXdwyMasb/7wlN6OWCD3ExXI0GbBk4QHK59t9fEvja25fSgWsWBl3ZZuzuLYMlAeUJwALyt9LiSig/FqEvrtC944vlqSEuqfCmA5MRdoANW3dHZu4586cOYM2bdrgwYMHWnftNJClSRxPtjTJxdbxRJbGVuPak4NUjJWYsNN6DtZamvjtuWkGxJJhcmOH+EHlUmMIIRbvI2Z5ciVxwJ03P/kl/zxgWUgJ1eO+txZXtzTVWT5dtaUpb+QMer4JYNWGvQauXbuG69evm5U3atQIGRkZaromCLdGyR/1iwtMl4RLiQvuL3FumZKxtXgIC83BUMZ/LVTfHoi55rirxbqHtzSxLHFfX1yQYCbMuBa/iwsSFAsmQH4KCsMYYm2sGVfpd0sp3DkbEEoNwf+uC1270PzEzimFfvgRYigWTXq9HjNnzixf1hgRgbp166Jq1ap49913odfrAQDe3t5o0aKF5pMlnB/6YyMfoQeIUB01osfSL3SxB6GcuWmFrQSTXMHCD1621M6QF0lKTPGzhWuVq0gsfkduPBv/85YS2YZzQi4xLa4jKm2kMVhfynXLny+/Dr9fRwhw50OnwUEIoVg0TZkyBUuWLMH777+PEydO4Pjx45g9ezY+/vhjTJs2zRZzJAi3RUgQcQ9DmbUWGSE3nFhf/F/3UnFHroKYABJKUKkVBgHFt1pp0a+cYGkpix63rpAYEXsvNh5fVCnFIJ64Y8pZ4CAk6qVi46zBpX8AMg0OQhDFMU3h4eFYvnw5nn32WZPyLVu24LXXXhN017kjFNMkjBZ/aDwhiFNpTIpWwa5SLgyhoHV7YuuxpZbDy21nDVqLMgOW4tKsvZ+WVjLKiSkSqmuL/9NS8XlajadVfJNdY5qWpaiPafp3Cj3fBFBsafr999/RuHFjs/LGjRvj999/12RShGui1S8zdxVMYpYdS23ErATW3iexdkLWJDHLly2wtWAyuN6Uihhr2qhpJ4TcFZeW4pzE4qLkWnMs1eFbR/nvtYiLMiBlhTKc1wKXtDiRpclmKBZNLVq0wJIlS8zKlyxZQnFMHoxL/mFRgdw/yGK/tNUID7GHmaU5iZ0XepDxx+D/epcb8yT1UFaCkHhTMg+ueFFqObLWQiU2ntj4Yi4n/j0XEq98V5thDKl7wxUXclen8cvEvjtC/3LRKi7KEh4b38R06g9CEFl5mrjMnTsXzzzzDHbv3o3ExETodDpkZGQgLy8P27dvt8UcCSfHEwSTWBA1YPqrWmgVkNz++auF+EJF7Je13HJLrhX+dcoReVJ9SgWwS7l/+POXir3ht7P08AdM91CTk3zR0ns+0vmIhK1PagS14TrLx5Weo9i9FIuf08JqI2RxKk/TIHydcqyocl1wlv7vEIRSrMrTdOPGDXzyySc4d+4cGGOIjo7Ga6+9hvDwcMuN3QSKaSrHEwSTAbE/1EpXtykZTyro11JckjXzELsWObEplmJdLF2LkAiVO2ehNlpaGfjWIUt72/EtW5bEmdh9siQ8De/FtiexJHrE7rWQYLfUxhJy/p9oLW60jG9SG9tkz5im2ktmqI5pujZ6usc/34RQZGl69OgRunXrhk8//RSzZs2y1ZwIF8GTBJMBJSJCLmIPKDmuFbFyS5YesTnw6wpdp1JBI2c+UvUsIcfaYKl/voiR48ITs0oZVs0ZzgkJKH47wz2V+5lzrUEAjNnH+Vj6rITi5QxjSVmc5AgnvpgVEma2Fk4ea2lSG5dEMU2iKLY0hYSEICMjAw0bNrTVnFwCT7U0eaJQMiAkZrT6Y2ytlUVJ/3IecPz5GOakZBxuO0suTGuw1l1pab5clMQ9iYktg4CSGwcl1xVmjcvMXtZRof4txTTJtaxJjWONiFeKS1maPtbA0vQ6WZqEUBzTNHjwYKxcuRLvv/++LeZDOCGeLJQA6QeAXAFlSZTI/RWudM5SwbhchK6Lb82w1h2jtJ1cYWUpRssa4Weo1z3cuoBxg1DivpeLEhGk1N0mVs9WyHH7KYld0npe1hC5aJ5NtlexCWqDuV04ELxVq1aK6ut0OmzduhW1atWSV1+ppen111/HmjVr0KBBA7Ru3Rr+/v4m5+fPn6+kO5fFUyxNJJjUxwgpjUuSOmepDWD+YOBbecSEntRDjo+cuCapB5TUPZVjaVHrwpMbX6NFJm9Lgd9C8xGqJ+fzdWak5s89L1THEva6djWiya55mhbNVJ+naew7Lvl88/LywsSJExEQEGCxLmMM77//Ps6cOYP69evL6l+xaOrcubN4Zzod9u7dq6Q7l8UTRJMzCCZXeBgA8ucp95e2tWJNyvIiNl/+WEIxLNy+LFmQuP1wN7O1BkMfWuY7snSP+OfVCCc5gkloTCGsFaHOilSwuaFczKXryOu0VjjZVTQt1EA0jXNd0VRQUIAaNWrIql+lShWcPHnSdqKJKMfdRZMzCCZXRsodx0VpHanxxLA2LkRMXEiJLG5bg9jgiyYlFiZL25DY2s2ktWDiI+fzF6pv6xg4eyDHsqlFnJ0tsEY4kWiyD1evXkXdunWh08lzMebl5SE8PBze3t6y6iuOaeIPptPpULt2bTXdEE4EiSVtkBv7JCRApPoxtJE6LxWALdctJDSHBuOPiM5T6sF9KXm5mXDibnDbPbylWVyXoT9jLp8FwnNWa4HQWnAptYjJCY4Xuv9yLH7OjLXB+obrdrRQdPr4Jg+OaYqIiFBUv06dOorqK84IXlpaimnTpiEoKAj16tVDREQEgoKCMHXqVDx69Ehpd4QTELlonvEg5GPNA9fSL2mlosbSA0TOr3OhQHShcu57/mvuXITGupS83EQoGYhKGykp/PiHJeTcL/44QqixMO28kSUrEFtsTnz491VIsLoaUt9FoTIxF54jceq/lx68jcqpU6eg1+tl1z99+jRKS0tl11dsaRo9ejQ2bdqEuXPnIjExEQCQmZmJlJQU3L59G8uXm/9hJJwTp/5P7wJIrQyS00bsF7TScfl9yRUGWv5a5z7MjeMnS7cRi3UyiitOe4M1SmhckzE5OMoa0T28JRrAshBS41rk32tHW16sQUzQi1lFnfH6nN7i5IHExsaioKAAISEhsuonJiYiKyvLdjFNQUFB+Oqrr9CzZ0+T8h07dmDAgAEoLCxU0p3L4g4xTSSa/kbNQ0epq0FKbMkN5Bbrj/8w5rsBueNxx1T7QBIaj5+ziDuGkMuOPzduELkcQSp0H5UGXVuT5NKAWNC3o6wjzurCE/ouin2HnWneUsgRTnaNaZr3rvqYponTXPL55uXlhVdffRWVK1eWVX/p0qWKVs8ptjT5+fmhXr16ZuX16tWDj4+P0u4IB0BiyRw1f5z5D0ZLliapAGslSFmXuPMQi6HitpMrOMQQsqCZCKUFpvW7h7c0K+P31T28vJ2QVUbKEqEksJ2PFqv0lAZ32wpL3w8D9hYoQlYlPs7khpOD01mcPDgjeIcOHXD+/HnZ9RMTE1GpUiXZ9RVbmmbOnIlz585h9erV8PX1BQAUFxdj2LBhaNiwIaZPn66kO5fFlS1NJJpsg5TVR4m1yFDGRY4AkHpYy4kJUWrpEro+tYHZlrD2YSokbA3WMCmhpDS2iSsUHfngF1oMIHTO8N7RWPN/xNmwJJrsamn6SANL0yTXtDTZGsWiqW/fvtizZw98fX3RokULAMDJkydRUlKCp556yqTuxo0btZupk+GKoonEkmOQI3AsWaDkupnkiDYhYWPpAW/JeiJ0nu+O4yLkmjPUEYpfknroi83D0E6OS08KtS46LdHC3WfpO2UvcaJ2AYMzIiWc7CqaPnxPvWh6Y6pLPd/shWL3XNWqVfHcc8+ZlCldskcQro7Sh4slN4RSwcQ/L+SKUVrO7VdofpZWPCl5mF9KXm4UGHxRUm79EV9JxX8v5baT81DWErn3wlrxI0c8Wupb7DvB/dceSH2/hcQ/IR8dKz/UtCeEUSyaVq9ebYt5EDZGrZXJ0/5wWbpepbE/Sh+ilvoUc4mJPQSF5ioWGM6fl1AdKUuPHKFl2n+WWXl5ELg80chHTUyTnP6FMFqYFsgbxxo3qdw+5M7ZkrCyx/93S65fqTrOiNPFNhGaozhPE+F6aOGWc6U/XFog93rlBP3KEw6mNBgvnX9JzoNVzIrFLZf7gBUSU2ICi/+aP0b38JZGFxw/ySUX7jmueLuUvBw7b2QZ748ca5Jaqw6fqLSRxuvgzlsLC5aSuDKl8D8jw2FNDJwtcdW/N04jmByQp+ngwYPo1asXwsPDodPpsHnzZottDhw4gLi4OPj5+aF+/fqCKYs2bNiA6Oho+Pr6Ijo6Gps2bVI+OQ0h0eTGUMJKaeS6MLhlfKEhJEyE6vEf7tzzaoKbLb0WE0xi8+JjSViJxRDxr40bw3RxQQJ23sgyi3OSIy4vLkgwEypS1yE1/503sowHv5wLVyAZXsuZrxLExIyQpVDtuGLfD1uKNUtIWQwJ1+DBgwdo0aIFlixZIqt+Tk4Onn76abRv3x4nTpzA22+/jTFjxmDDhg3GOpmZmUhOTsagQYNw8uRJDBo0CP3798fRo0ct9v/o0SO88soruHz5stXXJATtPWclzhIIrpUocjf3mxxXlxL3Gr++0kBVS0HZ/HNK58Yfgz83qbglOX2qOW8YTygjOJfu4S0lcztZGtPSQ9awUk4ssJsrxMQ+D3shx23FLeO3FXKnCl2TO/2fdwacJRA84oP34OXnZ3U/+ocPcfU/1geC63Q6bNq0CX369BGt85///Adbt27F2bNnjWUjR47EyZMnkZmZCQBITk5GUVERduzYYazTo0cPVKtWDevXr7c4j6pVq+L48eOyczDJQdXec4RjsIX1yBX/eMqPmVF+ztI4QtYUOTFQYv1o8YCWelhKxWBZeujysYUgjUobCSyAca85Oa41ax76UivhuKKKG4guJpatwVIfUuflCh7+vZeyNgp9TiSmCANFRUUm7319fY2phtSSmZmJbt26mZR1794dK1euxKNHj1CxYkVkZmZi/PjxZnUWLlwoa4y+ffti8+bNmDBhgiZzBmSKpuDgYFy4cAHVq1fHP//5TyxatAhVqlTRbBKEfJzd3WbrP7hiFhs+cq0fUmPI6UttHe71yA22VjMOv0yonRJxZQluX2JurQbjjwhuyCtmIRG6Lq3unUE4WboXlvI7iSEmarnnpeLYxF7z7xP3+yT0/eKWC41LwkkeThPDxEejDXv5K+OnT5+OlJQUFRP7m4KCAoSGhpqUhYaGorS0FLdv30bNmjVF6xQUFMgao0GDBnj33XeRkZGBuLg4+Pv7m5wfM2aM4nnLEk0lJSUoKipC9erV8fnnn+ODDz4g0eQAnF0wAeIPWrV/gK11L4n1IfUglnpoSVkB1MxNyTzk9MO1MEnVFfp8xB6y/DZKH7RibcU+C7F4Mf7nYCluywDXNReVNlKwL8N8+HPmW+y0ysfEv3ZrXGdC3z1uP5YEpZD1k9s3iScnFkdiaJQRPC8vz8Q9p5WVyYBOZyrsDNFC3HKhOvwyMf773/+iatWqOHbsGI4dO2Y2ts1EU2JiIvr06YO4uDgwxjBmzBjRtOOrVq1SPAnCfdEqfkKLP9xSDwcucn/tC53ju9rkzkkrccnvV+w9t1zowcoXVHyxozSuSMhNqKSOmKgU64trCeLWM5QZNtS1JIKFhKcWbjr+OHwBp+R7IPV/TMj6pOT/pFyrpZxzzoZBCEn9GHU5saQxgYGBNou/CgsLM7MY3bx5ExUqVMBjjz0mWYdvfRIjJydHm8lykLV6bt26dXj66adx//596HQ6FBYW4o8//hA8lJCSkgKdTmdyhIWFidbPz8/HSy+9hEaNGsHLywvjxo0TrLdw4UI0atQIlSpVQp06dTB+/Hg8fPjQ6nGdAVewMkkhFl/hjCh9YCmx7sjpRwuU3mfD+EKfkyVLDt8VJNa/0Gv++GJzF7J8Sd1vriXIkKKAi1Bsk1CZ2H3U4rOSE2ukBCk3nOFfS5+D3HGsOedMcMWQmDByacHkgJQDSklMTER6erpJ2a5du9C6dWtUrFhRsk5SUpLtJyiCLEtTaGgo3n//fQBAZGQk1q5da1SCamnatCl2795tfO/t7S1at7i4GCEhIZgyZQoWLBDe7fOLL77AW2+9hVWrViEpKQkXLlzA0KFDAcCkjZJxHY2rCyYurvJHVQ5aPIDEsLdljm+BMPQh9fDmW4bkWoik+uO2E3svJC7E5lo+fhYAU+uTkHvNEMtkKf5LS0uT0tgoa61DhraW2riSpchahMQQ3+rk0oIJjskIfv/+fVy8eNH4PicnB1lZWQgODkbdunUxefJkXL9+HWvWrAFQvlJuyZIlmDBhAkaMGIHMzEysXLnSZFXc2LFj0aFDB3zwwQfo3bs3tmzZgt27d+Pw4cOy53Xt2jVs3boVubm5KCkpMTk3f/58xdepePWc1uauChUqyLby1KtXD4sWLQIg7gbMzMxE27Zt8dJLLxnbvPjii/jxxx+tHpdwH1zloWDrOQoFDnPL5SDmwhMbz1DP0jk5cThi8xDqu/v4lrI25u0e3lJQPCodUwlRaSPRANp9J+W46oTKhD4D7nt3wZIYcnWx5Eh+/vlndO7c2fjesGJtyJAhSE1NRX5+PnJzc43nIyMjsX37dowfPx6ffPIJwsPDsXjxYpNt2pKSkvDVV19h6tSpmDZtGqKiopCWlob4+HhZc9qzZw+effZZREZG4vz584iJicGVK1fAGEOrVq2suk6rUg4cOHAAH330Ec6ePQudTocmTZrgjTfeQPv27RX3lZ2djfDwcPj6+iI+Ph6zZ89WlVOhXbt2WLduHX788Uc88cQTuHz5MrZv344hQ4bYdFxb4U5WJmdAKMjVGWM07DGuUCA3918uYg9T/kNarjtLyqrF78eSgJIjXqwJ3Ba6N3LHE+tPrdCylUVTKNjeXfBIIaRRILgSOnXqBKm0j6mpqWZlHTt2xPHjxyX7ff755/H8888rnxCAyZMnY+LEiZg5cyaqVKmCDRs2oEaNGhg4cCB69OhhVZ+KM4KvW7cOXbp0QeXKlTFmzBiMHj0alSpVwlNPPYUvv/xSUV/x8fFYs2YNdu7cic8++wwFBQVISkrCnTt3lE7LyIABA/Duu++iXbt2qFixIqKiotC5c2e89dZbqsYtLi5GUVGRyUG4NkIuHzXuJVdCLMBZDDFLhJirTKg934Ik57WUm0xLLIkqtd8DIXcjX3hqidTnJFXfVvOxNzljJxoPj8QFYprswdmzZ40GkwoVKuCvv/5CQEAAZs6ciQ8++MCqPhWLplmzZmHu3LlIS0vDmDFjMHbsWKSlpeH999/Hu+++q6ivnj174rnnnkOzZs3QpUsXbNu2DQDw+eefK52Wkf3792PWrFlYunQpjh8/jo0bN+K7774zmZs1486ZMwdBQUHGg5+/gnBNhAK5nSFY3VHWLf4c+NYWoeBrua4cS8HecuZkC7gB4Fred26/UsHztoDbP9dyJCVo7Y2txvRYoUSY4e/vj+LiYgBAeHg4Ll26ZDx3+/Ztq/pU7J67fPkyevXqZVb+7LPP4u2337ZqEgb8/f3RrFkzZGdnW93HtGnTMGjQIAwfPhwA0KxZMzx48ACvvvoqpkyZAi8vc50oZ9zJkyebZBUtKioi4eTEKInJ4LuF3OGXthKElruLCSRDfS78tkIovadKxKtYXbE4Jn45f6Udt8xkRV2yMjcfN7CcO1c+YvFlUm2E+jDUFWsv9d12xHfeFmOSYCrHEYHgzkhCQgJ++OEHREdH45lnnsHEiRPxyy+/YOPGjUhIsO77p1g01alTB3v27EGDBg1Myvfs2aNaRBQXF+Ps2bNWxUYZ+PPPP82Ekbe3Nxhjov5WOeNqmT6eUI+aVURC9dw18FUOQoJJKPZLqL7hPBe5wlPovst1Jwm1AWC2d50QXEEkKI5k9iMHoUB7sXrc+vzXlhC6F2IrEMXiwqz97ou1tff/KRJMHDTKCO7qzJ8/H/fv3wdQnmro/v37SEtLQ4MGDURX4FtC8Ya9y5Ytw7hx4/DPf/4TSUlJ0Ol0OHz4MFJTU7Fo0SL861//kt3XpEmT0KtXL9StWxc3b97Ee++9hwMHDuCXX35BRESE2RJFAMjKygIADB8+HI0aNcIbb7wBHx8fREdHAyi/MfPnz8eKFSsQHx+Pixcv4t///jfi4uKQlpYma1w52GPzRQoC/xuxZeCW4nDkWI880bokhJwl6fy6ciwplsaQY1WS49aTsxGvUF+G92IbCisRTXJW+nERExZqv5NC/UqtDHSV778rCyN7btgbmTJb9Ya9OSlvO3xDemdEsWgCgE2bNmHevHnG3YkNq+d69+6tqJ8BAwbg4MGDuH37NkJCQpCQkIB3333XKICGDh2KK1euYP/+/X9PWCB9ekREBK5cuQIAKC0txaxZs7B27Vpcv34dISEh6NWrF2bNmoWqVavKGlcOtv4PQILpb5SsbvNki5E1qBGUSu+1HDFhbVyZoY2QcBITPTtvZBm3UzG8l9tWC5R+R+UKKUsB+fxzzvSjwpVFkSVINNmfV155BS+//DKefPJJ2VuvWMIq0UTY/j9AV68X6MH/P0gIaYtWQkiuJU9qHC6WrEli7iYx+LFLQu8NcPel45bZCmdxCSuxEmo9Ln88dxZMgH1FU/3p6kXT5RmuL5qeffZZ7Nq1C4899hgGDBiAQYMGoWXLlqr6VLx6jrA9ZGUyRSgYGbDPyip3Q+tYMCX1hdIOCJ0X61MoDkdqXpZEj5iAsgfOIJgciacJJrtDKQcAAFu3bkVBQQGmT5+OY8eOIS4uDtHR0Zg9e7bRO6UUsjRZia1+NZCFSRiheAy6T9ribLFdUq46pS48fn0hq5LSLU20wB73W87/F0d+9p4imOxqaXpHA0vTTNe3NPG5du0a1q9fj1WrViE7OxulpaWK+yBLE+ES8FdvOdPD3RkRWt2mJMGhLeHnEOJbn/jnxfqQ+z0QcoV1D29pPAxEpY00qQPYzgIlFhBu6TNSsrqQO5ZW1kWt8RTBZHfY32kHrDncxdLE5dGjR/j5559x9OhRXLlyBaGhoVb1Q6LJyfA0MWBpCTZhHXyRyS9zFFKr7qRcd4Y63H/lfkek0iMAf+dT4o91cUGC5pYnQ79cK5pYugHDv0LnneGzJJwYcs8Z2bdvH0aMGIHQ0FAMGTIEVapUwbfffou8vDyr+rPaPVdSUoKcnBxERUWhQgWrtrBzaWxlaqV4pr9xNneRO+GIeyu15N0eGNxvBuuRUPJJZ8bd/i94mpXJru65qbPhrcI9V/bwIS6/5/ruudq1a+POnTvo3r07Bg4ciF69esFPxX0BrLA0/fnnnxg2bBgqV66Mpk2bGnctHjNmDN5//31Vk/F03FkwybEoucrDyx1wxQzQBiuNoR9rXGdcwbTzRpaJm0+re2Irl54r/f+wZDX0NMFkd8jSBAB45513cOPGDWzevBkvvPCCasEEWCGaJk+ejJMnT2L//v0mE+jSpYsxeSRByEXIjST2nvAMdt7Iws4bWSauKKHvAt91JiVWuGLJ8J7rluM/5NV892wRTG7POD4psaMkPk5sviSYbI+aeCa1W7A4E6+++iqqVaumaZ+KRdPmzZuxZMkStGvXziRZVHR0tMlmeATBhQQQYcDSA9cQoC1UT8qCoYVYsdZ9KLUNi1rs7UKU+r9KMVWEp6NYNN26dQs1atQwK3/w4IFmGTc9EU9wzVky2ROuiZLPVM3nb21bvpXJIMqEVmRaO4ZQEk2thBM/9YI1K+jsidT/c7IyEa6OYtHUpk0bbNu2zfjeIJQ+++wzJCYmajczD8KdBRPgPNmPCW0RShMg122jNIGmnMB1SyLFcF5oub+Wgp4rztQgtD2QrTJ4aynAxD5fEkx2hGKabIbiZW9z5sxBjx49cObMGZSWlmLRokU4ffo0MjMzceDAAVvM0a1xd8FkgMSSeyG2hYpBfEiJHSVboYi1E0IoQWX52KaWHykBYgvrjaUta8TaKL0vav6P2fr/Jwkm+6I2LsldYppsgWJLU1JSEn744Qf8+eefiIqKwq5duxAaGorMzEzExcXZYo6EjbCVed9Z3QaE9nBzDUll7ubXERIpYlunGOrIcXfxBRO3fynLjy1dx2KxWWIWGSnBJCRQnQFnmQdB2BraRsVKtMi54a5WJsqv5JnwH5z8JI5iaGXlEVoRx49n4s5L7L3WyHExqhnfFf6vkaXJvnmaGrw1G96+KvI0FT/ExfddP0+TLVBsafL29sbNmzfNyu/cuQNvb29NJuUJuKtgItwbsTgmKTeclNVEywe+QRhxLTDcFAOGMfnzEbOQaYFQ1m9L9eWeo+2ECFEopslmKI5pEjNMFRcXw8fHR/WEPAESTO6NO1vapNxJcgSBvZOZyl3BJhSfpQVirjn+uPzX/LnxX2uZV8rWkJWJcCdki6bFixcDKF8t99///hcBAQHGc2VlZTh48CAaN26s/QwJh6DmAeLMf8DtgSdcP9/tJsfFJcdVJwZX/AjFJgmd5waFX1yQACyQtirx3YRaBoeL9SUWxM0XR0rur9h7wnOgQHDbITumKTIyEgBw9epV1K5d28QV5+Pjg3r16mHmzJmIj4+3zUydDGv902RlItwFa0WQNW3ERJOlnEhCm/FKBayLBaMLLf/XCjnCRol1ytmEElmayrFnTFPDN9THNGV/SDFNQigOBO/cuTM2btyoeWpyV8Oa/wAkmAhXwBoLhbOtntp5IwtRaSMFzwm5tS4lLzfZwFfq+uXEQEm5zqREm1h9OUH1ziaWABJMXEg0uQeKA8H37dvn8YKJINwZqQeznBgda8ZTC9fSdHFBgnEbFn7sj5B7rMH4IyZB5NascBMbQ8rFx3cLSgV2SwWSc9s5UxoCEkyOg/aesx2KA8EB4Nq1a9i6dStyc3NRUlJicm7+/PmaTIwgCMchZnGxZDWxxmWn5iFvcM9FpY1EAwgn3ORjaX5i7eW0E6on5j7jCiExt6AcnNE9R4LJwahdAUeiSRTFomnPnj149tlnERkZifPnzyMmJgZXrlwBYwytWrWyxRwJgrAT3Ae4HCuJWB/24u8s4OZCT+y1Evh9yG0DWBZZluajJMmlpTk7g5AiCHdAsWiaPHkyJk6ciJkzZ6JKlSrYsGEDatSogYEDB6JHjx62mCNBEHZC6mHMfYg7iwsIgEkskhh8ISiFQYRZg9yEntYiZgEDxMWa0HtbQlYmJ4AsTTZDcUzT2bNnMWTIEABAhQoV8NdffyEgIAAzZ87EBx98oPkECYKwP3zLjDNbKvgr5/jJJPmxTZbEDD+lgTVxQmoEE1/gyLXqiX1ehs/SmmSYSq+DBJNzQDFNtkOxpcnf3x/FxcUAgPDwcFy6dAlNmzYFANy+fVvb2REE4RC4lhlnCi7mwhVLDcYfMdusl5uOgO/GM7TRah5yAsnlIBVPJYRQDBff4qRmPkrakmByIsjSZDMUi6aEhAT88MMPiI6OxjPPPIOJEyfil19+wcaNG5GQ4Ly/RgmCkI+l4GVngC+IAOG8TQZBE5U2ElhQXqbldUhtBMxFTXyRUFuuBUmqjT0gwUR4Cordc/PnzzcmsExJSUHXrl2RlpaGiIgIrFy5UvMJEtI400OMcD6s+X5IPdzVuuls5eYzCBfuv9w8TVyLmS3/z1jK1yQn+JuPWJoEsfZCKQyc1VpI2Ajae85mKE5uSZRDyS0JR2PLbTLkrgCTW8cWbQ3w3XL8/g3YKzBbacZxKeRmKbfUB3ceWkNWJnnYM7ll4zHqk1ueW0zJLYWwKk8TAJSUlODmzZvQ6/Um5XXr1lU9KYJQiy0FhbOgxQNYLB5G7L1hXDWiSmshwxdM9lzlZ20uJTn1LeWa4vbrDC46gvAEFLvnLly4gPbt26NSpUqIiIhAZGQkIiMjUa9ePeP+dIQ5ZGWyDwY3hLsLJmsQCuq2JtBbKvu10NJ+7oNdC8Ek5jZ0ZNwV3yVmKSmokv4s1bOUz4k/JxJSHoCD3HNLly5FZGQk/Pz8EBcXh0OHDonWHTp0KHQ6ndlhWFgGAKmpqYJ1Hj58aN0ENUCxpemVV15BhQoV8N1336FmzZrQ6XS2mJfbQGLJvrirWNJSCAo90PliU86DVSp+R8oCIhbULBehuo4SAvx7JzYPKcuX2s9V7nfDUuA44T6oTRtgTdu0tDSMGzcOS5cuRdu2bfHpp5+iZ8+eOHPmjKAHatGiRXj//feN70tLS9GiRQu88MILJvUCAwNx/vx5kzI/P+tdj2pRLJqysrJw7NgxNG7c2BbzcStIMBFaobVgksrDJMfdJlYuJpZskfDR0J9UTJOtEbO6Ke3D0uerNss3WV8JWzN//nwMGzYMw4cPBwAsXLgQO3fuxLJlyzBnzhyz+kFBQQgKCjK+37x5M/744w+88sorJvV0Oh3CwsJsO3kFKHbPRUdHUz4mD4HM+O6LmAVIzso5uau3+OVaxzUZhABXMLmSMFAyVyl3n5x+bHVfKAjcSdHIPVdUVGRyGHI08ikpKcGxY8fQrVs3k/Ju3bohIyND1pRXrlyJLl26ICIiwqT8/v37iIiIQO3atfGPf/wDJ06ckNWfrVAsmj744AO8+eab2L9/P+7cuWN2Uwn3QWvLAOFc8LNmC50zwI+xEXPNyXFDabEMXmppvaPgZyYXghtfZK31x1oBZK/geMIJ0Eg01alTx2gRCgoKErQYAeWJrcvKyhAaGmpSHhoaioKCAovTzc/Px44dO4xWKgONGzdGamoqtm7divXr18PPzw9t27ZFdna2vPtgAxS757p06QIAeOqpp0zKGWPQ6XQoKyvTZmZuQM7YiS7vonOlX+6EZfhB2VIWI6m2QuVisUZCVhGtH9xCIs/e4sBg8RILgue+55bZ6/+Y1uORlcn9ycvLM0k54OvrK1mfH+Ns0AWWSE1NRdWqVdGnTx+T8oSEBJOk2W3btkWrVq3w8ccfY/HixTKuQHsUi6Z9+/bZYh5uizsIJ8K+2Dr+hC9itHCbibmPpILF1WDpHtkihkoIoTEsBarzVyy64g8TEkzOje5/h5r2QHkQtpw8TdWrV4e3t7eZVenmzZtm1ic+jDGsWrUKgwYNgo+Pj2RdLy8vtGnTxrUsTR07drTFPNwaEk6eib3dL5bGtcaqxD8vlNKBf14s2FwrASNXiNlDOCnJUWVNegdHYeuEmISNsfPecz4+PoiLi0N6ejr69u1rLE9PT0fv3r0l2x44cAAXL17EsGHDLE+LMWRlZaFZs2bKJqghskTTqVOnEBMTAy8vL5w6dUqybvPmzTWZGEG4OrZ42MixToiJGLllcoLBxeYld47WINeqw60vpw9bzQ0Qjw1zdqTmSVYm58cRKQcmTJiAQYMGoXXr1khMTMSKFSuQm5uLkSNHAgAmT56M69evY82aNSbtVq5cifj4eMTExJj1OWPGDCQkJKBhw4YoKirC4sWLkZWVhU8++cSq69ICWaKpZcuWKCgoQI0aNdCyZUvodDoI7b5CMU3ikLWJ0AJLgkisvlRdOYLJkuVBzpJ5a7DkgjPATzugJF5LLGhdbe4osTquIpwIQgnJycm4c+cOZs6cifz8fMTExGD79u3G1XD5+fnIzc01aVNYWIgNGzZg0aJFgn3evXsXr776KgoKChAUFITY2FgcPHgQTzzxhM2vRwxZe89dvXoVdevWhU6nw9WrVyXr8pcLuiu09xxhL4TcXGLBxlyXlJIVWkrqGcYSai8krtRYdsTEnFLXm5QL0dEIXZMzCyuyNFmHPfeea/ov9XvPnf6U9p4TQpaliSuEPEUU2QJ3sjY5+x92V0JpckOpOkrbyOlLjrVKSDBpGfStpFxobkJtnEEwAeaCkP5fEZqgJqaJEEWWaNq6davsDp999lmrJ0O4DvSHXTvkWBe4deQKLG4qALluPLH3YmVauLak5mRJiCmxGNnLwiRnDPr/QxCuiSzRxM+dwI9p4uZhoJgmwpOx5Yo5OSvR+Cu0+MJJKwuhpfHVIubqU5rCQGyVn1g9W4pAbl+uujqNXHOugSMCwT0FWRnB9Xq98di1axdatmyJHTt24O7duygsLMT27dvRqlUrfP/997aer8tDf3TcG1s9AOUsVZcb/KxUADhimTzfqsYXgnLnJKeNkPuRP7ZcxNIKGPoTW2HnKqkICBdBo4zghDmyAsG5xMTEYPny5WjXrp1J+aFDh/Dqq6/i7Nmzmk7QWVET1OcucU2EbeC715QEaBuwVboDeySM1AruijqhTX2lLG/WWpy0EK7OZnmiH3raYM9A8JgRs+HtoyIQvOQhfv2MAsGFULz33KVLl0x2JjYQFBSEK1euKOorJSUFOp3O5JDazTg/Px8vvfQSGjVqBC8vL4wbN06w3sKFC9GoUSNUqlQJderUwfjx4/Hw4UOTOkuXLkVkZCT8/PwQFxeHQ4cOKZq7GuiPECGFNcLEYMngxzxpHYztrHmHhGK9uCKJL5gAYSuUWouPVFtu30Kfl5x4NXuRM3ai8SBcD4N7Ts1BCKNYNLVp0wbjxo1Dfn6+saygoAATJ060KndC06ZNkZ+fbzx++eUX0brFxcUICQnBlClT0KJFC8E6X3zxBd566y1Mnz4dZ8+excqVK5GWlobJkycb66SlpWHcuHGYMmUKTpw4gfbt26Nnz55mOSRsCf0xIqRQ8gC1ldVCTEQIua5s9bBXcg9sbQGTe61yYqcslTtylR/9bXIDyD1nMxSLppUrV+LmzZuIiIhAgwYN0KBBA9StWxf5+flYuXKl4glUqFABYWFhxiMkJES0br169bBo0SIMHjxY0NoFAJmZmWjbti1eeukl1KtXD926dcOLL76In3/+2Vhn/vz5GDZsGIYPH44mTZpg4cKFqFOnDpYtW6Z4/moQ+uPkKq4Pwr4IpRMwYCtXnNQ5obxJjsQW90AqGagU/M9GTnwU39IlJ82DLSDBRBDSKN57rmHDhjh58iR2796Nc+fOgTGG6OhodOnSRdZuxnyys7MRHh4OX19fxMfHY/bs2ahfv77ifgy0a9cO69atw48//ognnngCly9fxvbt2zFkyBAAQElJCY4dO4a33nrLpF23bt2QkZEh2m9xcTGKi4uN74uKiqyeoxTOYp4nnAOhVWP2epDKEUSOFksGxESklq42oc9AjoDir+AT+vzkWqBsCQkm94FWz9kORaKptLQUfn5+yMrKQrdu3dCtWzdVg8fHx2PNmjV4/PHH8dtvv+G9995DUlISTp8+jccee8yqPgcMGIBbt26hXbt2YIyhtLQU//73v40i6fbt2ygrKzPbeTk0NNRsh2Yuc+bMwYwZM6yaE0FohVwRIBXgLPUg1totJCS85OYx4lpdlMyFvwpO6Dx3DKX9isV3yZ2T0Ht7QuLIA7Dzhr2ehCLRVKFCBURERGiWi6lnz57G182aNUNiYiKioqLw+eefY8KECVb1uX//fsyaNQtLly5FfHw8Ll68iLFjx6JmzZqYNm2asR7fKsYYk7SUTZ482WRORUVFqFOnjlVz5OJOWcIJ2yP3QSt3RZhYO0tL8+UitsTemnZawBeFcvMzcUWcYSWe3NV03H/Fxhd6rwYSRh4OiSaboTimaerUqZg8eTJ+//13zSfj7++PZs2aITs72+o+pk2bhkGDBmH48OFo1qwZ+vbti9mzZ2POnDnQ6/WoXr06vL29zaxKN2/eNLM+cfH19UVgYKDJQRDOipgIkGtd4Qei81/b2kpibZ4kKaTiiSxZpLgYVuKJue+EVjHy6/BfO9LyRBCEfBTHNC1evBgXL15EeHg4IiIi4O/vb3L++PHjVk+muLgYZ8+eRfv27a3u488//4SXl6kW9Pb2BmMMjDH4+PggLi4O6enp6Nu3r7FOeno6evfubfW4BOFMWLOCi4uQFcSaOTgq5kls/tyYIr4oElvBJiZo+NfHd9spiVMiwURoCcU02Q7Foom/pYoaJk2ahF69eqFu3bq4efMm3nvvPRQVFRmDtidPnozr169jzZo1xjZZWVkAgPv37+PWrVvIysqCj48PoqOjAQC9evXC/PnzERsba3TPTZs2Dc8++yy8vb0BABMmTMCgQYPQunVrJCYmYsWKFcjNzcXIkSM1uzaCcGXUCh417YXcYmoQsggJxSXxg7T52cjFRJJQ4kyCcCjknrMZijOCa8mAAQNw8OBB3L59GyEhIUhISMC7775rFEBDhw7FlStXsH//fmMbobijiIgIY2LN0tJSzJo1C2vXrsX169cREhKCXr16YdasWahataqxzdKlSzF37lzk5+cjJiYGCxYsQIcOHWTPXevsrhTXRDgTjrAQWSuQHGHRUpLd2xGuN4ppcj7smRG8xWD1GcFPrqGM4EJYLZqOHTuGs2fPQqfTITo6GrGxsVrPzamx5X8AElCEI9FagLjS1ivWZmM3IGbVItFE2FM0tRw0S7Voylo7hUSTAIpF082bNzFgwADs378fVatWBWMMhYWF6Ny5M7766ivJ5JTuhD3+AxggEUXYEjmB0K4ieuwF/55IZfQWq2NLSDQ5H3YVTS9rIJrWkWgSQvHquddffx1FRUU4ffo0fv/9d/zxxx/49ddfUVRUhDFjxthijgRB2BAxS4nQCjqp4GZnCnAWSh4pNj8lc+Rn6ua3FwsCp0BvgnAPFIum77//HsuWLUOTJk2MZdHR0fjkk0+wY8cOTSdHlEO/Ggl7wV/mz90yRWjDWbH2BtRaqKwRNGJWMy2sZWKr5aQsS2SpI+wNbdhrOxSvntPr9ahYsaJZecWKFaHX6zWZFEEQjkfIbSeWDdseySiV1rXHCkCh9AVCK/CEUhwQhM2g1XM2Q7Gl6cknn8TYsWNx48YNY9n169cxfvx4PPXUU5pOjiAI+yFkMRHaSNaZ3HB8LM2DO38xUbTzRhYAacFmqMOvZ2klnbPcJ4IgrEOxaFqyZAnu3buHevXqISoqCg0aNEBkZCTu3buHjz/+2BZzJCQgsz9hC8SyWgsJKym3mK3mJobUJrtCrw3/ckWQVM4lQ72oNPOcbpa2ZSEIe0HuOduh2D1Xp04dHD9+HOnp6Th37hwYY4iOjkaXLl1sMT+Ch9Cu6YTnYMvl69ZmALe3QBAbT8xyJOd+yU1OGZU2ElhAoohwcsg9ZzMUW5oMdO3aFa+//jrGjBlDgsmOkEjybKyxZEhtmit2zpIryZbWJTV9WhPHpPReCq0w5Lv9CMKRkKXJdlglmg4cOIBevXqhQYMGaNiwIZ599lkcOnRI67kRHGgFHcHF0oNZaHsQoXPcMn4dKfEkJbrUYm1ySbFrEHvPby/23tJcpe6tIyxS9LeCIGyHYtG0bt06dOnSBZUrV8aYMWMwevRoVKpUCU899RS+/PJLW8yRIAgFcFd0ccUP95zhNfdfoezVQpYVIRzpMpaylhleiwW489tfXJCAS8nLzfrjxjxxy/j3UmjVnNgcbQEJJgLA3+45NQchiOKM4E2aNMGrr76K8ePHm5TPnz8fn332Gc6ePavpBJ0Ve2YEN0CZwQkuXHEklhqAe14qFojbRuy8pTpql/fbon++WBRa9i8kCq2Zh6PdciSYnBt7ZgSP6z8LFSr6Wd1P6aOHOPY1ZQQXQrGl6fLly+jVq5dZ+bPPPoucnBxNJkUQhGXkJE+U40azlPiRa7GSqmONaJBriZEjmKTcbFzxqEbcWLoHjoIEE0HYB8WiqU6dOtizZ49Z+Z49e1CnTh1NJkUQhHz4VhS54kBry4g1okFujiNrx5fjWuSfF7JGUY4lwqVgTP1BCKI45cDEiRMxZswYZGVlISkpCTqdDocPH0ZqaioWLVpkizkSBCGBkKtJTnZsV1g2r8btxxeRcuuLnbO3CCUIa1G7Ao5Wz4mj2NL073//G1999RV++eUXjBs3DmPHjsWvv/6KtLQ0/Otf/7LFHAmCsADXuiQUlO0KAkkINXFMUgHtQmVy9s0TC/S2dq5a4KqfLUG4IooDwYlyKBCccBa0sKR4EnKEpJCFSyrY3hmguCbnxp6B4K2fe091IPjPG6ZSILgAit1zP/30E/R6PeLj403Kjx49Cm9vb7Ru3VqzyREEYRmpPErc80JiwdUtUdYg5bpUskULt76S2CmCsDU6ffmhpj0hjGL33KhRo5CXl2dWfv36dYwaNUqTSRHy8KQHHaEMIdeUUC4hbpmnPOz51yommLj3yVKST6k6BEG4D4rdcwEBATh16hTq169vUp6Tk4PmzZvj3r17mk7QWXGEew4gFx0hjZL8TNx6YuW2jNlRm9tJzXhKLUpibZRYmKxNy6AEctE5L/Z0z7Xpo94999Nmcs8JodjS5Ovri99++82sPD8/HxUqKPb2EQrJGTvReBDuh9qVYoCpOJCb/4i/d5pQVnGt0apPuekVhLZUsbQNCheh+8AdW6x/w3ul27NYA/2oIgDH7T23dOlSREZGws/PD3FxcZLbq+3fvx86nc7sOHfunEm9DRs2IDo6Gr6+voiOjsamTZusm5xGKFY5Xbt2xeTJk7FlyxYEBQUBAO7evYu3334bXbt21XyChDg5Yye6zB9Je/zKdgesvUdimcHlIrWVirOiJh5L7X3ml0lZ5PgCVs7Yau5/5KJ59KPK01Gba8mKtmlpaRg3bhyWLl2Ktm3b4tNPP0XPnj1x5swZ1K1bV7Td+fPnTaxZISEhxteZmZlITk7Gu+++i759+2LTpk3o378/Dh8+bBZXbS8Uu+euX7+ODh064M6dO4iNjQUAZGVlITQ0FOnp6R6T4NJR7jk+riKaCHFsIVKstVRYcsvZ26UmF6XzsuRq07q9pdV4tvgOkHByLuzpnnvi2XdVu+d+3DpN0Vzj4+PRqlUrLFu2zFjWpEkT9OnTB3PmzDGrv3//fnTu3Bl//PEHqlatKthncnIyioqKsGPHDmNZjx49UK1aNaxfv17ZRWmEYvdcrVq1cOrUKcydOxfR0dGIi4vDokWL8Msvv3iMYCLcHznuGym3jtoHsBqEAr6VzEOoPt99Zw1aB0pzg6/teb+lguzFsBQwbgvLHv2g8ly0cs8VFRWZHMXFxYLjlZSU4NixY+jWrZtJebdu3ZCRkSE519jYWNSsWRNPPfUU9u3bZ3IuMzPTrM/u3btb7NOWWBWE5O/vj1dffVXruRBW4EouOmeG+0tfyI3CTR4pJJCUPATF3DRaWRuEkltq1Z8abBUXZck9JtZG6DOXwhkSWSqFXHUeCvvfoaY9YGYImT59OlJSUsyq3759G2VlZQgNDTUpDw0NRUFBgeAQNWvWxIoVKxAXF4fi4mKsXbsWTz31FPbv348OHToAAAoKChT1aQ8octsNcAbhJPcPs6PnyYcvhuRaQ7hB0vy+5IzHDxDWSjDxBZm1OYnE+pa7Ks/WCN1Dw2uhenKSVVpCi+t1RLwYCSfCWvLy8kzcc76+vpL1dTqdyXvGmFmZgUaNGqFRo0bG94mJicjLy8NHH31kFE1K+7QHJJrcBEcKJyV/kJ1B4InlJpJ6mIm5rPh98gOqpfoQegirfagKiTkhK5gcASA0F7mr8rSGL4ospUPg3wcpoajFtYh93mqFmlaQcPIstNp7LjAwUFZMU/Xq1eHt7W1mAbp586aZpUiKhIQErFu3zvg+LCxMdZ9aozimiXBeHPFH0ZoxHf3H21KiQiG4sSuWYn6E+rImDYDSc2LzERtXrnCyVcoBJQjNQWpufNEoFftkTayVtQJY7j3XCm5fjv6xQtgRw+o5NYcCfHx8EBcXh/T0dJPy9PR0JCUlye7nxIkTqFmzpvF9YmKiWZ+7du1S1KfWkKXJzbCnJUeN+HEGixMfuckL5VqDlLqwxGKlhFxuUi49IeuZEguRowWSllhaDWgtSlfKSc1HqI5WcPty9I8Vwr2ZMGECBg0ahNatWyMxMRErVqxAbm4uRo4cCQCYPHkyrl+/jjVr1gAAFi5ciHr16qFp06YoKSnBunXrsGHDBmzYsMHY59ixY9GhQwd88MEH6N27N7Zs2YLdu3fj8OHDDrlGwIqUA0OHDsU///lPE5+jJ+IsKQfEsLUgsdcfYEcIK6lAcDltrckfJBWELtaX0DjWxPbw2yqdu7PhyHgrKeHNn5dYXaXiXA4kmByPPVMOJPacqTrlQOaOdxTPdenSpZg7dy7y8/MRExODBQsWGLXC0KFDceXKFezfvx8AMHfuXKxYsQLXr19HpUqV0LRpU0yePBlPP/20SZ//93//h6lTp+Ly5cuIiorCrFmz0K9fP6uvTS2KRdNzzz2Hbdu2oU6dOnjllVcwZMgQ1KpVy1bzc1qcXTQB4oLDXf6A2kpQWXrgylllZ4uHtqV+rRFrroajA9DlIGflpKV6Ym2tFVHu8n/elbGraOqhgWj6Xrlo8gQUiyYAuHPnDtatW4fU1FT8+uuv6NKlC4YNG4bevXujYsWKtpin0+EKoskTcLQlSuxBZq14kiOMLFmK5AQkE6aouUeWViRaI3QtWR6VQILJOSDR5B5YJZq4nDhxAqtWrcJ///tfBAQE4OWXX8Zrr72Ghg0bajVHp4REk/NgD+EkFF9krSvOVkjlfXKG+bkzYqLGUmyT0GstIcHkPNhTNCV1Vy+aMnaSaBJC1eq5/Px87Nq1C7t27YK3tzeefvppnD59GtHR0ViwYIFWcyQISezxYBB6sDlq+bgYQqvJnGHlmycjJ6WF0OdFnxmhCj1TfxCCKF499+jRI2zduhWrV6/Grl270Lx5c4wfPx4DBw5ElSpVAABfffUV/v3vf2P8+PGaT5gghFAinKyxTGllCRBbDaclzibmlOKK7kQ5sUpyA/Bd/fMjnACNMoIT5ii2NNWsWRMjRoxAREQEfvzxR/z8888YOXKkUTAB5XvDiG3ARxCOxhrLlFQuH8ODzlK+HzGrgzV5gsT65c5JTj16QKvH2jgmfn0STATh/CiOaVq7di1eeOEF+PlZ7y91ByimyfXRKhZKLDZFbKm5AaUWCbU4e0oBJSkU5PRlzTWqtf4pzQyuZjWdHCimyXmwZ0xT2y4zUKGCipim0of4Yfd0er4JoNg9N2jQIFvMgyDsjlYJNvlbdgDi7jypfEn2tDIotY7YA0v3Q4mgUZo2wlDGtRoK9SUn15LSz5asS4TmWJHV26w9IYjq1XOeClma3ActV9+J5W4y4EixImVpcoZVdlKxPdZk9ZaT0FPJ3KzN8i2VsJQ/RzmiTAlkaXIe7GppeipFvaVpTwo93wQg0WQlJJrcG1u47vjl9gx4dnbXHBc5OYqUZjcXEiNy8mFZaqdVugAlmcCVxD6RaHIe7Cma2j2pXjQd3kuiSQjae44gBLDWdce3GsiJZ7KHeHJ2ocRFjqVFLKie7yJVakHjulrFcnHJjUVTYnmSI774rkNb5XYi3ABaPWczVOVpIgh3xtpVdtx/pTKGc+sR5cjJayRWX0gwKRFL3HZCn6NUWy2C+oX6kRKQalddEgShHBJNBCGBte4NqYel4ZwrWX/shVAwPf+1WH0llhsx659QUD+/nZRLkC9k+P0JBZpbmhNBKEXHmOqDEIbccwRhASHhZMl1J2blsBQoLtXWljhiTCnEVs5x4c+Xa12SI7iEsNRO7n2Scr9KrawkCE3Q/+9Q054QxKGiKSUlBTNmzDApCw0NRUFBgWD9/Px8TJw4EceOHUN2djbGjBmDhQsXmtTp1KkTDhw4YNb26aefxrZt26walyD4yLJAjRUv6+r1gkmxowO1nUkwAeKr5SzF86i5DrEYKG4Z32JkTf9icVcEQTg/Drc0NW3aFLt37za+9/b2Fq1bXFyMkJAQTJkyRXRvu40bN6KkpMT4/s6dO2jRogVeeMH0IaVkXILQmnT9N+jq9QI9MEUQE0xi7i01Yokfv8QdS6pvJYJHSIwpCTJXAgkxQq2Ljdxz4jg8pqlChQoICwszHiEhIaJ169Wrh0WLFmHw4MEICgoSrBMcHGzSX3p6OipXrmwmmpSMSxC2IF3/jcl7qXgZT0PoXvBFjCVRIxRjZGlM7mt7uM/UuBLFIMFEGFfPqTkIQRwumrKzsxEeHo7IyEgMGDAAly9f1rT/lStXYsCAAfD391c1bnFxMYqKikwOglBLztiJJJagfEsZoZQAcvrj1xFbrSdkfRJqozRo3FIKBWeLLSNcFENGcDUHIYhDRVN8fDzWrFmDnTt34rPPPkNBQQGSkpJw584dTfr/8ccf8euvv2L48OGqx50zZw6CgoKMR506dTSZI0Gk678xOTwRucHVQm47tXFMQhYtbrmYKLMkzMTydAmlNDC81tK1RoktCUJ7HCqaevbsieeeew7NmjVDly5djIHan3/+uSb9r1y5EjExMXjiiSdUjzt58mQUFhYaj7y8PE3mSBB8PF1A8bEkUOS64ITioPgxRXJSRQj1aSmPk1g5WZUIW6Bj6g9CGIcHgnPx9/dHs2bNkJ2drbqvP//8E1999RVmzpypybi+vr7w9fVVPS+CUIIhYNxTEBISUjFLUu/lnLMkWsTSGkjVVWIpEkuqaWksS5CVycOhDXtthsNjmrgUFxfj7NmzqFmzpuq+vv76axQXF+Pll1+267gEoTWeZHGyxk3Hb2+N1Ufu2EL1rF3Nx09rIDcVgaW+STARhO1wqGiaNGkSDhw4gJycHBw9ehTPP/88ioqKMGTIEADlLrHBgwebtMnKykJWVhbu37+PW7duISsrC2fOnDHre+XKlejTpw8ee+wxxeMSBOGciFmiuFYeqa1V5Fqt+OfkbO8iFqsk1a/YnKRcd1J9k2AiAECnV38QwjjUPXft2jW8+OKLuH37NkJCQpCQkIAjR44gIiICQHkyy9zcXJM2sbGxxtfHjh3Dl19+iYiICFy5csVYfuHCBRw+fBi7du2yalyCcDYM1iZ3dtVxA6EtbZtiKbO6nDxLQv1y2/PHs3UMEmUIJzSD3HM2Q8cY3R1rKCoqQlBQEAoLCxEYGOjo6RAegruJJjlCSU4dueOonafhtdZo1TdZmpwXezwzDGN0emIKKlTws7qf0tKH2P/jLHq+CUCiyUpINBGOwt2EkxSWRIRUXJBYmaXxxPpwBcsPiSbnxa6iqY0GouknEk1CONXqOYIgPBuxzW0N54Tq8+txUWpdEqovZuVyBRFFeCa0jYrtcKrVcwRBWMYdV9NZCqKWCpq2JsmltZvt8ufqTJCViSBsD4kmgiCcBkvCSKiONWJGrL0Q/FxKtO0N4fTQNio2g9xzBEE4HLVboajpw1J7ufmTCMJpYADUpA0gzSQKWZoIgnAKlLq9DBYfOTmWuIdUHal+uONy/+XPyRK2cKORa47gYohpUnMQwtDqOSuh1XOEo3GHVXRy9m7Ton8DctIaKJ2PXKHHFzZdvV7QxGpFgsk1sOfquSdj30IFbxWr58oeYu+J9+n5JgC55wjCRXHlfensFRsktvpObKNeS/ORytckVi4magwB/ZGL5klfhAQkmAhBGFQmt9RsJm4HuecIwoVx1ZV0csSJNecsYW3Wbb7gknLzcZEjaqwVPiSYCFEoENxmkGgiCBfHVYWTFNaKKqk94uRk3baU9kAuDcYfUSRqlAogEkwE4RhINBEE4VCsyZkk1l7onNR5bnv+fnX8dnLdiTljJ1olZHPGTjQ5pOoRhCR6DQ5CEAoEtxIKBCecEVeNcdIafm4lLmIr7pRat0i8EEqwZyD4UzFvooK3r9X9lJYVY8+vcxXPdenSpfjwww+Rn5+Ppk2bYuHChWjfvr1g3Y0bN2LZsmXIyspCcXExmjZtipSUFHTv3t1YJzU1Fa+88opZ27/++gt+ftYHuquBLE0EQTgUKZcav57SmCQ5bjZLW7VwkWMJIghPJC0tDePGjcOUKVNw4sQJtG/fHj179kRubq5g/YMHD6Jr167Yvn07jh07hs6dO6NXr144ceKESb3AwEDk5+ebHI4STACJJoJwK5w5vklMkIjtNSenrdA5tWkMxMYioUS4DA4IBJ8/fz6GDRuG4cOHo0mTJli4cCHq1KmDZcuWCdZfuHAh3nzzTbRp0wYNGzbE7Nmz0bBhQ3z77bcm9XQ6HcLCwkwOR0KiiSDcDGcVTtYIIv55OQkl5QR88/u3FAvlrPeUIASxs2gqKSnBsWPH0K1bN5Pybt26ISMjQ1Yfer0e9+7dQ3BwsEn5/fv3ERERgdq1a+Mf//iHmSXK3pBoIgg3xJUe8mq2JpEK7FbSRuzcxQUJLnUvCUJLioqKTI7i4mLBerdv30ZZWRlCQ0NNykNDQ1FQUCBrrHnz5uHBgwfo37+/saxx48ZITU3F1q1bsX79evj5+aFt27bIzs62/qJUQsktCYJwOEqEjBhCFiOpeiZiaKysIQjCNVCba+l/bevUqWNSPH36dKSkpIg20+l0vG6YWZkQ69evR0pKCrZs2YIaNWoYyxMSEpCQ8PePoLZt26JVq1b4+OOPsXjxYjlXojkkmgiCcDhabqdiJoiEIJFEuDN6AJa1inR7AHl5eSar53x9hVfkVa9eHd7e3mZWpZs3b5pZn/ikpaVh2LBh+Oabb9ClSxfJul5eXmjTpo1DLU3kniMIN8WV3EpqtlThCy5Xum6CsAVabdgbGBhocoiJJh8fH8TFxSE9Pd2kPD09HUlJSaLzXL9+PYYOHYovv/wSzzzzjMXrYowhKysLNWvWVHA3tIUsTQRBuDQklgjC8UyYMAGDBg1C69atkZiYiBUrViA3NxcjR44EAEyePBnXr1/HmjVrAJQLpsGDB2PRokVISEgwWqkqVaqEoKAgAMCMGTOQkJCAhg0boqioCIsXL0ZWVhY++eQTx1wkSDQRBOEGkGAiCA4axTQpITk5GXfu3MHMmTORn5+PmJgYbN++HREREQCA/Px8k5xNn376KUpLSzFq1CiMGjXKWD5kyBCkpqYCAO7evYtXX30VBQUFCAoKQmxsLA4ePIgnnnjC+mtTCWUEtxLKCE64Cu6YJZxEEuFq2DMjeJeocaozgu++tJCebwKQpYkg3Jx0/TcuIZxICBEE4eyQaCIIwu6QQCIIG+IA95ynQKvnCMIDcCaR4kxzIQj3RG02cBJNYpBoIggPwRnEijPMgSAIwlrIPUcQHoKj45pIMBGEnSD3nM0g0UQQhOaQQCIIB6JX6WLTk2gSg0QTQXgAtrIykTgiCMKTINFEEG6Oo91yBEHYGaYvP9S0JwQh0UQQbgwJJoLwQCimyWaQaCIIN8XWgolccwThpFBMk82glAME4YaQhYkgCEJ7yNJEEC4OCSSCIEwg95zNINFEEC4MCSaCIMxgUCmaNJuJ20HuOYJwUUgwEQRB2BeyNBGEC+JowURB4AThxJB7zmaQaCIIF8PRgokgCCdHrwegIteSnvI0iUHuOYJwIZxBMJGViSAIT4UsTQRByIYEE0G4AOSesxkkmgjCRXC0lYkEE0G4CCSabAa55wiCsAgJJoIgCLI0EQRhARJMBOFi0DYqNsOhlqaUlBTodDqTIywsTLR+fn4+XnrpJTRq1AheXl4YN26cWZ1OnTqZ9anT6fDMM8+Y1Fu6dCkiIyPh5+eHuLg4HDp0SOvLIwiXhwQTQbgejOlVH4QwDnfPNW3aFPn5+cbjl19+Ea1bXFyMkJAQTJkyBS1atBCss3HjRpP+fv31V3h7e+OFF/6OB0lLS8O4ceMwZcoUnDhxAu3bt0fPnj2Rm5ur+fURhKtCgokgXBTGyq1F1h4U0ySKw91zFSpUkLQucalXrx4WLVoEAFi1apVgneDgYJP3X331FSpXrmwimubPn49hw4Zh+PDhAICFCxdi586dWLZsGebMmWPNZRCEW0GCiSAIwhyHW5qys7MRHh6OyMhIDBgwAJcvX9a0/5UrV2LAgAHw9/cHAJSUlODYsWPo1q2bSb1u3bohIyNDtJ/i4mIUFRWZHAThjpBgIggXx7B6Ts1BCOJQ0RQfH481a9Zg586d+Oyzz1BQUICkpCTcuXNHk/5//PFH/Prrr0aLEgDcvn0bZWVlCA0NNakbGhqKgoIC0b7mzJmDoKAg41GnTh1N5kgQzkK6/hsSTAThDuj16g9CEIe653r27Gl83axZMyQmJiIqKgqff/45JkyYoLr/lStXIiYmBk888YTZOZ1OZ/KeMWZWxmXy5MkmcyoqKiLhRLg8JJIIgiDk4/CYJi7+/v5o1qwZsrOzVff1559/4quvvsLMmTNNyqtXrw5vb28zq9LNmzfNrE9cfH194evrq3peBOEskGAiCDeFqUw5QO45URwe08SluLgYZ8+eRc2aNVX39fXXX6O4uBgvv/yySbmPjw/i4uKQnp5uUp6eno6kpCTV4xKEK0CCiSDcF6bXqz4IYRwqmiZNmoQDBw4gJycHR48exfPPP4+ioiIMGTIEQLlLbPDgwSZtsrKykJWVhfv37+PWrVvIysrCmTNnzPpeuXIl+vTpg8cee8zs3IQJE/Df//4Xq1atwtmzZzF+/Hjk5uZi5MiRtrlQgtAArYQOCSaCIAjrcKh77tq1a3jxxRdx+/ZthISEICEhAUeOHEFERASA8mSW/NxJsbGxxtfHjh3Dl19+iYiICFy5csVYfuHCBRw+fBi7du0SHDc5ORl37tzBzJkzkZ+fj5iYGGzfvt04LkG4KySYCMIDIPeczdAxRnfHGoqKihAUFITCwkIEBgY6ejqEh6JkE18STAThOOzxzDCM8aRvf1TQ+VjdTykrwd7ir+n5JoBTxTQRBGEbSDARBEGox6lWzxEEoT0kmAjCw2AMgIpgbnJAiUKiiSDcGBJMBOF5MD0D01kvfChqRxxyzxGECyOVxZsEE0F4KEyv/rCCpUuXIjIyEn5+foiLi8OhQ4ck6x84cABxcXHw8/ND/fr1sXz5crM6GzZsQHR0NHx9fREdHY1NmzZZNTetINFEEG4AXzyRYCIIwp6kpaVh3LhxmDJlCk6cOIH27dujZ8+eZivgDeTk5ODpp59G+/btceLECbz99tsYM2YMNmzYYKyTmZmJ5ORkDBo0CCdPnsSgQYPQv39/HD161F6XZQatnrMSWj1HEARByMWeq+c66fqigq6i1f2UskfYzzYpmmt8fDxatWqFZcuWGcuaNGmCPn36YM6cOWb1//Of/2Dr1q04e/assWzkyJE4efIkMjMzAZSnByoqKsKOHTuMdXr06IFq1aph/fr11l6eKsjSRBAEQRDuhJ3dcyUlJTh27Bi6detmUt6tWzdkZGQItsnMzDSr3717d/z888949OiRZB2xPu0BBYJbicFAV1RU5OCZEARBEM6O4VlhD+dOKR6pym1ZinLRwn++ie3Bevv2bZSVlZnt3xoaGmq2z6uBgoICwfqlpaW4ffs2atasKVpHrE97QKLJSu7duwcAqFOnjoNnQhAEQbgK9+7dQ1BQkE369vHxQVhYGA4XbFfdV0BAgNnzbfr06UhJSRFto9PpTN4zxszKLNXnlyvt09aQaLKS8PBw5OXloUqVKqo+wKKiItSpUwd5eXkUGyUC3SNp6P5Yhu6RZegeSaP2/jDGcO/ePYSHh9tgduX4+fkhJycHJSUlqvsSEidCViYAqF69Ory9vc0sQDdv3jSzFBkICwsTrF+hQgXjnrFidcT6tAckmqzEy8sLtWvX1qy/wMBA+kNlAbpH0tD9sQzdI8vQPZJGzf2xlYWJi5+fH/z8/Gw+DhcfHx/ExcUhPT0dffv2NZanp6ejd+/egm0SExPx7bffmpTt2rULrVu3RsWKFY110tPTMX78eJM6SUlJNrgKeZBoIgiCIAhCFRMmTMCgQYPQunVrJCYmYsWKFcjNzcXIkSMBAJMnT8b169exZs0aAOUr5ZYsWYIJEyZgxIgRyMzMxMqVK01WxY0dOxYdOnTABx98gN69e2PLli3YvXs3Dh8+7JBrBEg0EQRBEAShkuTkZNy5cwczZ85Efn4+YmJisH37dkRERAAA8vPzTXI2RUZGYvv27Rg/fjw++eQThIeHY/HixXjuueeMdZKSkvDVV19h6tSpmDZtGqKiopCWlob4+Hi7X58BEk0OxtfXF9OnTxf1FRN0jyxB98cydI8sQ/dIGro/lnnttdfw2muvCZ5LTU01K+vYsSOOHz8u2efzzz+P559/XovpaQIltyQIgiAIgpABJbckCIIgCIKQAYkmgiAIgiAIGZBoIgiCIAiCkAGJJoIgCIIgCBmQaJLB9evX8fLLL+Oxxx5D5cqV0bJlSxw7dsx4fujQodDpdCZHQkKCSR+dOnUyqzNgwACTOn/88QcGDRqEoKAgBAUFYdCgQbh7965JndzcXPTq1Qv+/v6oXr06xowZY5b99ZdffkHHjh1RqVIl1KpVCzNnzrTpfkeW7g8AnD17Fs8++yyCgoJQpUoVJCQkmCw/LS4uxuuvv47q1avD398fzz77LK5du2bSh6veH0Cbe+TJ3yH+dRuODz/80FjH079Dcu6RO3+HAMv36P79+xg9ejRq166NSpUqoUmTJli2bJlJH+7+PSJUwghJfv/9dxYREcGGDh3Kjh49ynJyctju3bvZxYsXjXWGDBnCevTowfLz843HnTt3TPrp2LEjGzFihEmdu3fvmtTp0aMHi4mJYRkZGSwjI4PFxMSwf/zjH8bzpaWlLCYmhnXu3JkdP36cpaens/DwcDZ69GhjncLCQhYaGsoGDBjAfvnlF7ZhwwZWpUoV9tFHHzns/ly8eJEFBwezN954gx0/fpxdunSJfffdd+y3334z1hk5ciSrVasWS09PZ8ePH2edO3dmLVq0YKWlpS59f7S8R578HeJec35+Plu1ahXT6XTs0qVLxjqe/h2Sc4/c9Tsk9x4NHz6cRUVFsX379rGcnBz26aefMm9vb7Z582ZjHXf+HhHqIdFkgf/85z+sXbt2knWGDBnCevfuLVmnY8eObOzYsaLnz5w5wwCwI0eOGMsyMzMZAHbu3DnGGGPbt29nXl5e7Pr168Y669evZ76+vqywsJAxxtjSpUtZUFAQe/jwobHOnDlzWHh4ONPr9ZJztAY59yc5OZm9/PLLoufv3r3LKlasyL766itj2fXr15mXlxf7/vvvGWOue38Y0+YeMebZ3yE+vXv3Zk8++aTxPX2HzOHfI8bc9zvEmLx71LRpUzZz5kyTslatWrGpU6cyxtz/e0Soh9xzFti6dStat26NF154ATVq1EBsbCw+++wzs3r79+9HjRo18Pjjj2PEiBG4efOmWZ0vvvgC1atXR9OmTTFp0iTcu3fPeC4zMxNBQUEmmU4TEhIQFBSEjIwMY52YmBiTDR+7d++O4uJiowk6MzMTHTt2NEnA1r17d9y4cQNXrlxRfT/4WLo/er0e27Ztw+OPP47u3bujRo0aiI+Px+bNm411jh07hkePHqFbt27GsvDwcMTExJhcuyveH0Cbe2TAE79DfH777Tds27YNw4YNM5Z5+neIj9A9MuCO3yFA3j1q164dtm7diuvXr4Mxhn379uHChQvo3r07APf/HhHqIdFkgcuXL2PZsmVo2LAhdu7ciZEjR2LMmDHG/XMAoGfPnvjiiy+wd+9ezJs3Dz/99BOefPJJFBcXG+sMHDgQ69evx/79+zFt2jRs2LAB/fr1M54vKChAjRo1zMavUaOGcZfngoICs92dq1WrBh8fH8k6hvf83aK1wNL9uXnzJu7fv4/3338fPXr0wK5du9C3b1/069cPBw4cMM7Lx8cH1apVM5s397pc8f4A2twjwHO/Q3w+//xzVKlSxezaPfk7xEfoHgHu+x0C5N2jxYsXIzo6GrVr14aPjw969OiBpUuXol27dsa5ufP3iFAPbaNiAb1ej9atW2P27NkAgNjYWJw+fRrLli3D4MGDAZTvuWMgJiYGrVu3RkREBLZt22b8gzRixAiTOg0bNkTr1q1x/PhxtGrVCkB5ICcfxphJuTV12P8CC4XaqsXS/dHr9QCA3r17G3eqbtmyJTIyMrB8+XJ07NhRtG8trl1OHVveH0C7e+Sp3yE+q1atwsCBA2Xt5O4p3yE+YvfIXb9DgLx7tHjxYhw5cgRbt25FREQEDh48iNdeew01a9ZEly5dRPt2l+8RoR6yNFmgZs2aiI6ONilr0qSJyaomoTYRERHIzs4WrdOqVStUrFjRWCcsLAy//fabWb1bt24Zf32EhYWZ/QL5448/8OjRI8k6Blch/1eNFli6P9WrV0eFChUk64SFhaGkpAR//PGH2by51+WK9wfQ5h4J4SnfIS6HDh3C+fPnMXz4cJNyT/8OcRG7R0K4y3cIsHyP/vrrL7z99tuYP38+evXqhebNm2P06NFITk7GRx99ZJy3O3+PCPWQaLJA27Ztcf78eZOyCxcuGHduFuLOnTvIy8tDzZo1ReucPn0ajx49MtZJTExEYWEhfvzxR2Odo0ePorCwEElJScY6v/76K/Lz8411du3aBV9fX8TFxRnrHDx40GRp665duxAeHo569erJv3CZWLo/Pj4+aNOmjWSduLg4VKxYEenp6cbz+fn5+PXXX02u3RXvD6DNPRLCU75DXFauXIm4uDi0aNHCpNzTv0NcxO6REO7yHQIs36NHjx7h0aNH8PIyfex5e3sbrb3u/j0iNMCeUeeuyI8//sgqVKjAZs2axbKzs9kXX3zBKleuzNatW8cYY+zevXts4sSJLCMjg+Xk5LB9+/axxMREVqtWLVZUVMQYK19OPmPGDPbTTz+xnJwctm3bNta4cWMWGxtrtoy1efPmLDMzk2VmZrJmzZoJLmN96qmn2PHjx9nu3btZ7dq1TZax3r17l4WGhrIXX3yR/fLLL2zjxo0sMDDQZstYLd0fxhjbuHEjq1ixIluxYgXLzs5mH3/8MfP29maHDh0y1hk5ciSrXbs22717Nzt+/Dh78sknBZf5utr90eoeefp3iLHyJdqVK1dmy5YtE+zH079DjEnfI3f+DjEm7x517NiRNW3alO3bt49dvnyZrV69mvn5+bGlS5ca67jz94hQD4kmGXz77bcsJiaG+fr6ssaNG7MVK1YYz/3555+sW7duLCQkhFWsWJHVrVuXDRkyhOXm5hrr5Obmsg4dOrDg4GDm4+PDoqKi2JgxY8xyOd25c4cNHDiQValShVWpUoUNHDiQ/fHHHyZ1rl69yp555hlWqVIlFhwczEaPHm2yZJUxxk6dOsXat2/PfH19WVhYGEtJSbHpElap+2Ng5cqVrEGDBszPz4+1aNHCJC8KY4z99ddfbPTo0Sw4OJhVqlSJ/eMf/zC5h4y57v1hTP09ou8QY59++imrVKmSWV4hA/Qdkr5H7v4dYszyPcrPz2dDhw5l4eHhzM/PjzVq1IjNmzfPZF7u/j0i1KFjjNKPEgRBEARBWIJimgiCIAiCIGRAookgCIIgCEIGJJoIgiAIgiBkQKKJIAiCIAhCBiSaCIIgCIIgZECiiSAIgiAIQgYkmgiCIAiCIGRAookgHIROp8PmzZsBAFeuXIFOp0NWVpZD56SGgoICdO3aFf7+/qhatardxx86dCj69Olj0zHs9TkNHToUOp3O5DtiLSkpKca+Fi5cqMn8CMJTIdFEEE5AnTp1kJ+fj5iYGE37rVevnt0elAsWLEB+fj6ysrJw4cIFu4zJZdGiRUhNTdWsPyERZqvPSYgePXogPz8fPXv2VNXPpEmTkJ+fj9q1a2s0M4LwXCo4egIE4c48evQIFStWtFjP29sbYWFhdpiR7bh06RLi4uLQsGFDTfstKSmBj4+PxXpBQUGajiuEPT8nX19fTcYKCAhAQEAAvL29NZgVQXg2ZGkiXJJbt24hLCwMs2fPNpYdPXoUPj4+2LVrl2i7a9euYcCAAQgODoa/vz9at26No0ePGs8vW7YMUVFR8PHxQaNGjbB27VqT9rm5uejduzcCAgIQGBiI/v3747fffjOeT0lJQcuWLbFq1SrUr18fvr6+YIwhOzsbHTp0gJ+fH6Kjo012UQfM3T779++HTqfDnj170Lp1a1SuXBlJSUkmu7hfunQJvXv3RmhoKAICAtCmTRvs3r3beL5Tp064evUqxo8fb3TPGMjIyECHDh1QqVIl1KlTB2PGjMGDBw8k77nUvalXrx42bNiANWvWQKfTYejQoYJ9GKw3M2bMQI0aNRAYGIh//etfJju9d+rUCaNHj8aECRNQvXp1dO3aFQBw4MABPPHEE/D19UXNmjXx1ltvobS01KxvA4wxzJ07F/Xr10elSpXQokUL/N///Z/JfE6fPo1nnnkGgYGBqFKlCtq3b49Lly4hJSUFn3/+ObZs2WK8d/v37xd0z1maV6dOnTBmzBi8+eabCA4ORlhYGFJSUiTvtRCGsb/++mu0b98elSpVQps2bXDhwgX89NNPaN26NQICAtCjRw/cunVLcf8EQcjAsVvfEYT1bNu2jVWsWJH99NNP7N69e6xBgwZs7NixovXv3bvH6tevz9q3b88OHTrEsrOzWVpaGsvIyGCMMbZx40ZWsWJF9sknn7Dz58+zefPmMW9vb7Z3717GGGN6vZ7Fxsaydu3asZ9//pkdOXKEtWrVinXs2NE4xvTp05m/vz/r3r07O378ODt58qRxx/NOnTqxEydOsAMHDrDY2FgGgG3atIkxxlhOTg4DwE6cOMEYY2zfvn0MAIuPj2f79+9np0+fZu3bt2dJSUnGsbKystjy5cvZqVOn2IULF9iUKVOYn58fu3r1KmOsfFPR2rVrs5kzZ7L8/HyWn5/PGCvfJDQgIIAtWLCAXbhwgf3www8sNjaWDR06VPTeWbo3N2/eZD169GD9+/dn+fn5opvqDhkyhAUEBLDk5GT266+/su+++46FhISwt99+21inY8eOLCAggL3xxhvs3Llz7OzZs+zatWuscuXK7LXXXmNnz55lmzZtYtWrV2fTp0836bt3797G92+//TZr3Lgx+/7779mlS5fY6tWrma+vL9u/fz9jjLFr166x4OBg1q9fP/bTTz+x8+fPs1WrVrFz586xe/fusf79+7MePXoY711xcbHZ5yRnXh07dmSBgYEsJSWFXbhwgX3++edMp9OxXbt2id5v/rUw9vd3xHBNZ86cYQkJCaxVq1asU6dO7PDhw+z48eOsQYMGbOTIkWZ9RkREsAULFoiOSRCEZUg0ES7Na6+9xh5//HE2cOBAFhMTw/766y/Rup9++imrUqWK2a7uBpKSktiIESNMyl544QX29NNPM8YY27VrF/P29jbZ8fz06dMMAPvxxx8ZY+WiqWLFiuzmzZvGOjt37mTe3t4sLy/PWLZjxw5Zomn37t3GNtu2bWMAJK8xOjqaffzxx8b3Qg/KQYMGsVdffdWk7NChQ8zLy0u0b0v3hjHGevfuzYYMGSI6N8bKxUBwcDB78OCBsWzZsmUsICCAlZWVMcbKRUbLli1N2r399tusUaNGJjvAf/LJJybtuELj/v37zM/PzyiIDQwbNoy9+OKLjDHGJk+ezCIjI1lJSYnoXMWEi+FzkjOvjh07snbt2pn006ZNG/af//xH8j6Jjf3f//7XWLZ+/XoGgO3Zs8dYNmfOHNaoUSOzPkk0EYR6yD1HuDQfffQRSktL8fXXX+OLL76An5+faN2srCzExsYiODhY8PzZs2fRtm1bk7K2bdvi7NmzxvN16tRBnTp1jOejo6NRtWpVYx0AiIiIQEhIiEm/devWNQnETUxMlHV9zZs3N76uWbMmAODmzZsAgAcPHuDNN980ziEgIADnzp1Dbm6uZJ/Hjh1DamqqMdYlICAA3bt3h16vR05OjmAbS/dGCS1atEDlypWN7xMTE3H//n3k5eUZy1q3bm02fmJioomLsW3btrh//z6uXbtmNsaZM2fw8OFDdO3a1eQ616xZg0uXLgEo/z60b99eVsyZGHLnxf0cgfLP0vA5KoXbV2hoKACgWbNmJmXW9k0QhDQUCE64NJcvX8aNGzeg1+tx9epVs4cTl0qVKlnsj/vwA8rjYgxl3NdidQDA39/f7LylccTgPtANbfR6PQDgjTfewM6dO/HRRx+hQYMGqFSpEp5//nmT+CAh9Ho9/vWvf2HMmDFm5+rWrSvaTureaIGleyg0vtC8gL/v0bZt21CrVi2Tc76+vgDkfR8sIXdefGGm0+mMc1SK0HeCX2Zt3wRBSEOWJsJlKSkpwcCBA5GcnIz33nsPw4YNMwnK5tO8eXNkZWXh999/FzzfpEkTHD582KQsIyMDTZo0AVBuVcrNzTWxiJw5cwaFhYXGOkIY2t24ccNYlpmZKesapTh06BCGDh2Kvn37olmzZggLC8OVK1dM6vj4+KCsrMykrFWrVjh9+jQaNGhgdoitUrN0b5Rw8uRJ/PXXX8b3R44cQUBAgOSS+OjoaGRkZJgI0IyMDFSpUsVMFBnq+/r6Ijc31+waDZbC5s2b49ChQ3j06JHgmEL3Tu28CIJwbUg0ES7LlClTUFhYiMWLF+PNN99EkyZNMGzYMNH6L774IsLCwtCnTx/88MMPuHz5MjZs2GAUMG+88QZSU1OxfPlyZGdnY/78+di4cSMmTZoEAOjSpQuaN2+OgQMH4vjx4/jxxx8xePBgdOzY0cydxKVLly5o1KgRBg8ejJMnT+LQoUOYMmWK6utv0KABNm7ciKysLJw8eRIvvfSSmYWhXr16OHjwIK5fv47bt28DAP7zn/8gMzMTo0aNQlZWFrKzs7F161a8/vrromNZujdKKCkpwbBhw3DmzBns2LED06dPx+jRo+HlJf7n6LXXXkNeXh5ef/11nDt3Dlu2bMH06dMxYcIEwXZVqlTBpEmTMH78eHz++ee4dOkSTpw4gU8++QSff/45AGD06NEoKirCgAED8PPPPyM7Oxtr1641rlCsV68eTp06hfPnz+P27duC4krpvAiCcG3ofzXhkuzfvx8LFy7E2rVrERgYCC8vL6xduxaHDx/GsmXLBNsY0hHUqFEDTz/9NJo1a4b333/fmL+mT58+WLRoET788EM0bdoUn376KVavXo1OnToB+DuDd7Vq1dChQwd06dIF9evXR1pamuRcvby8sGnTJhQXF+OJJ57A8OHDMWvWLNX3YMGCBahWrRqSkpLQq1cvdO/eHa1atTKpM3PmTFy5cgVRUVHGOKvmzZvjwIEDyM7ORvv27REbG4tp06YZY6aEsHRvlPDUU0+hYcOG6NChA/r3749evXpZXIJfq1YtbN++HT/++CNatGiBkSNHYtiwYZg6dapom3fffRfvvPMO5syZgyZNmqB79+749ttvERkZCQB47LHHsHfvXty/fx8dO3ZEXFwcPvvsM6Ora8SIEWjUqBFat26NkJAQ/PDDD5rMiyAI10XHhAIuCIIgbMDQoUNx9+5d1VuDCPHiiy/C29sb69at07xve2OL+1SvXj2MGzcO48aN06xPgvA0yNJEEIRLU1paijNnziAzMxNNmzZ19HQ047vvvkNAQAC+++47Vf3Mnj0bAQEBFldVEgRhGbI0EQRhN2xhQcnKykJSUhI6d+6MdevWoVq1apr17Shu3ryJoqIiAOXpCfirCZXw+++/Gxc/hISE2GW7GYJwV0g0EQRBEARByIDccwRBEARBEDIg0UQQBEEQBCEDEk0EQRAEQRAyINFEEARBEAQhAxJNBEEQBEEQMiDRRBAEQRAEIQMSTQRBEARBEDIg0UQQBEEQBCEDEk0EQRAEQRAy+H9pzQXSPXlk1QAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "xr.open_dataarray('31_results/snowmap_cloudfree_1d.nc',decode_coords=\"all\")[0].plot.imshow()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "914a28f1-1a9b-4647-8411-a3ff2821e59f",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Calculate Catchment Statistics\n",
+ "\n",
+ "We are looking at the snow cover of a region over time and want to extract aggregated catchment statistics on snow cover and cloud cover. We do this by counting all the pixels in the catchment, counting the pixels that are covered by snow and the pixels covered by clouds.\n",
+ "\n",
+ "Ultimately we are interested in the **snow covered area (SCA)** within the catchment. We count all snow covered pixels within the catchment for each time step. Multiplied by the pixel size that would be the snow covered area. The snow pixel count divided by the total number of pixels in the catchment is the percentage of pixels covered with snow. We will use this number.\n",
+ "\n",
+ "We need to make sure that the information content meets our expected quality. Therefore, we calculate the **cloud percentage** for the catchment for each timestep. We use this information to filter the timeseries. All timesteps that have a cloud coverage of over 25% will be discarded.\n",
+ "\n",
+ "We are going to \n",
+ "- Get number of pixels in the catchment: total, clouds, snow.\n",
+ "- Combine the three aggregated pixel counts into one data cube.\n",
+ "- Calculate cloud and snow percentages\n",
+ "- Filter cloudy time steps with the cloud percentage\n",
+ "- Plot the resulting time series\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "79beb8ce-39f3-4d96-837a-bec0b169725d",
+ "metadata": {},
+ "source": [
+ "**Quiz hint: remember the pixel counts here for the final exercise**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d9233ac-286e-432d-8581-0b53e7c0b63d",
+ "metadata": {},
+ "source": [
+ "### Count pixels and aggregate spatially to the catchment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "0704eac4-5fd2-4a6f-87b3-0689f669dc04",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# number of all pixels\n",
+ "n_catchment = ((snowmap_cloudfree > -1) * 1.0).add_dimension(name=\"bands\",type=\"bands\",label=\"n_catchment\")\n",
+ "\n",
+ "# number of cloud pixels (no function needed, mask already created before)\n",
+ "n_cloud = cloud_mask.add_dimension(name=\"bands\",type=\"bands\",label=\"n_cloud\")\n",
+ "\n",
+ "# number of snow pixels\n",
+ "n_snow = ((snowmap_cloudfree == 1) * 1.0).add_dimension(name=\"bands\",type=\"bands\",label=\"n_snow\")\n",
+ "\n",
+ "# combine the binary data cubes into one data cube\n",
+ "n_catchment_cloud_snow = n_catchment.merge_cubes(n_cloud).merge_cubes(n_snow)\n",
+ "\n",
+ "# aggregate to catchment\n",
+ "n_pixels = n_catchment_cloud_snow.aggregate_spatial(geometries = catchment_outline['geometry'][0], reducer = 'sum')\n",
+ "n_pixels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ff976399-bea0-477f-a014-5cf6a961e674",
+ "metadata": {},
+ "source": [
+ "### Register a batch job for processing\n",
+ "We are starting the processing now with a batch job. This registers our job on the backend in our user space and assigns further information to the job, such as an ID, the job status, the process graph and further metadata. First we specifiy the end of our process graph with `save_result()` and specifiy the format (since we aggregated over the spatial dimension we will receive three arrays of data. So JSON is a suitable format). Then we create the batch job and start it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "09d15142-b402-4b85-a98c-e3aa61d793c8",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0:00:00 Job 'j-240215519a4341dca10085027af0ef92': send 'start'\n",
+ "0:00:18 Job 'j-240215519a4341dca10085027af0ef92': created (progress N/A)\n",
+ "0:00:24 Job 'j-240215519a4341dca10085027af0ef92': created (progress N/A)\n",
+ "0:00:30 Job 'j-240215519a4341dca10085027af0ef92': created (progress N/A)\n",
+ "0:00:39 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:00:49 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:01:08 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:01:24 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:01:44 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:02:08 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:02:38 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:03:16 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:04:12 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:05:11 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:06:20 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:07:21 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:08:30 Job 'j-240215519a4341dca10085027af0ef92': running (progress N/A)\n",
+ "0:09:30 Job 'j-240215519a4341dca10085027af0ef92': finished (progress N/A)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Define the end of the process graph and the output format\n",
+ "n_pixels_json = n_pixels.save_result(format=\"JSON\")\n",
+ "# Create a batch job\n",
+ "job = n_pixels_json.create_job(title=\"n_pixels_json\")\n",
+ "# start the job and wait till it finishes\n",
+ "job.start_and_wait()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cc1be5d0-c0bd-4bad-8c7f-ee8ca0f50c9b",
+ "metadata": {},
+ "source": [
+ "Now we can check the status of our job. We can download the result once the job has finished."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "2b2e726b-1c43-45e8-b0a4-e55a960c543a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'finished'"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "job.status()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "4350f435-c8ef-4c15-a461-4745d66677d0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "if job.status() == \"finished\":\n",
+ " results = job.get_results()\n",
+ " results.download_files(\"31_results/\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7533cc88-0e3b-4e36-b633-16ac2f0a2277",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "**Quick hint: take a look at the job description: e.g. `job.describe_job()`**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa996a8d-ced1-4641-b8b8-4b5badab028d",
+ "metadata": {},
+ "source": [
+ "### Load the resulting time series\n",
+ "Let's load the result. It contains the total number of pixels in the catchment, number of cloud and snow pixels."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "74764134-ea52-4837-a635-eb8e65119b12",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# load the result\n",
+ "with open(\"31_results/timeseries.json\",\"r\") as file:\n",
+ " n_pixels_json = json.load(file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "e022c407-f8a2-4fae-8869-4283cbd1916f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('2018-02-16T00:00:00Z', [[4201607.0, 1809140.0, 1479720.0]]),\n",
+ " ('2018-04-17T00:00:00Z', [[4201607.0, 821551.0, 2215174.0]]),\n",
+ " ('2018-05-07T00:00:00Z', [[4201607.0, 2175370.0, 710901.0]])]"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# check the first 5 entries to check the data structure.\n",
+ "list(n_pixels_json.items())[:3] # careful unsorted dates due to JSON format"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b8d2f970-9980-4ca0-b476-869ababf2b7a",
+ "metadata": {},
+ "source": [
+ "**_Quick hint: what is the length of the time series JSON?_**\n",
+ "`len(n_pixels_json)`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0101fa0a-44ac-4ece-b555-e29f6b4167da",
+ "metadata": {},
+ "source": [
+ "Now we do some data wrangling to get a structured data frame."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "1ec6fdfe-329f-4688-887d-9dc8fa6bd492",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
n_catchment_vals
\n",
+ "
n_cloud_vals
\n",
+ "
n_snow_vals
\n",
+ "
\n",
+ "
\n",
+ "
time
\n",
+ "
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+ "
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+ "
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+ "
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+ " \n",
+ " \n",
+ "
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+ "
2018-02-06 00:00:00+00:00
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+ "
1489702.0
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+ "
253542.0
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+ "
619519.0
\n",
+ "
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+ "
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+ "
2018-02-08 00:00:00+00:00
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+ "
4201607.0
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+ "
183099.0
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+ "
2907006.0
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+ "
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+ "
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+ "
2018-02-11 00:00:00+00:00
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+ "
4201607.0
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+ "
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+ "
2943057.0
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+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " n_catchment_vals n_cloud_vals n_snow_vals\n",
+ "time \n",
+ "2018-02-06 00:00:00+00:00 1489702.0 253542.0 619519.0\n",
+ "2018-02-08 00:00:00+00:00 4201607.0 183099.0 2907006.0\n",
+ "2018-02-11 00:00:00+00:00 4201607.0 60947.0 2943057.0"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a Pandas DataFrame to hold the values\n",
+ "dates = [k for k in n_pixels_json]\n",
+ "n_catchment_vals = [n_pixels_json[k][0][0] for k in n_pixels_json]\n",
+ "n_cloud_vals = [n_pixels_json[k][0][1] for k in n_pixels_json]\n",
+ "n_snow_vals = [n_pixels_json[k][0][2] for k in n_pixels_json]\n",
+ "\n",
+ "data = {\n",
+ " \"time\":pd.to_datetime(dates),\n",
+ " \"n_catchment_vals\":n_catchment_vals,\n",
+ " \"n_cloud_vals\":n_cloud_vals,\n",
+ " \"n_snow_vals\":n_snow_vals\n",
+ " }\n",
+ "df = pd.DataFrame(data=data).set_index(\"time\")\n",
+ "# Sort the values by date\n",
+ "df = df.sort_values(axis=0,by=\"time\")\n",
+ "df[:3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "15b4d8c7-e2e4-45e2-a456-d5a75503cace",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "55525116.0"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.n_snow_vals.sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1bad0cdc-9ae9-4dfc-a6d7-e7079bcd947a",
+ "metadata": {},
+ "source": [
+ "### Calculate the cloud percentage for filtering time steps\n",
+ "Divide the number of cloudy pixels by the number of total pixels = cloud percentage"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "ba2372ff-410f-47fb-849e-b8d008baf320",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
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+ "
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+ "
n_catchment_vals
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+ "
n_cloud_vals
\n",
+ "
n_snow_vals
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+ "
perc_cloud
\n",
+ "
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+ "
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+ "
time
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+ "
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+ "
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+ "
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+ "
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+ "
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+ " \n",
+ " \n",
+ "
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+ "
2018-02-06 00:00:00+00:00
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+ "
1489702.0
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+ "
253542.0
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619519.0
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+ "
17.019646
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+ "
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+ "
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+ "
2018-02-08 00:00:00+00:00
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+ "
4201607.0
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183099.0
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2907006.0
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+ "
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+ "
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+ "
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+ "
2018-02-11 00:00:00+00:00
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+ "
4201607.0
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+ "
60947.0
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+ "
2943057.0
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+ "
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+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " n_catchment_vals n_cloud_vals n_snow_vals \\\n",
+ "time \n",
+ "2018-02-06 00:00:00+00:00 1489702.0 253542.0 619519.0 \n",
+ "2018-02-08 00:00:00+00:00 4201607.0 183099.0 2907006.0 \n",
+ "2018-02-11 00:00:00+00:00 4201607.0 60947.0 2943057.0 \n",
+ "\n",
+ " perc_cloud \n",
+ "time \n",
+ "2018-02-06 00:00:00+00:00 17.019646 \n",
+ "2018-02-08 00:00:00+00:00 4.357833 \n",
+ "2018-02-11 00:00:00+00:00 1.450564 "
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "perc_cloud = df[\"n_cloud_vals\"].values / df[\"n_catchment_vals\"].values * 100\n",
+ "df[\"perc_cloud\"] = perc_cloud\n",
+ "df[:3]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25cf5fd6-1820-4d1f-9bfe-2e37b015124a",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "**Quick hint: The sum of the n_catchment_vals should give an overall idea of the total number of pixels in the datacube for the whole time-series** `df.n_catchment_vals.sum()`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cc1c0b71-d210-4fa6-8f75-95def5cc2917",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "**Quick hint: a filter of the snow values can give an idea of when the maximum snow cover occurred** `df.where(df.n_snow_vals == df.n_snow_vals.max())`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0561cf75-b96a-4687-a57d-1d6eb0b22232",
+ "metadata": {},
+ "source": [
+ "**Quick hint: a simplified approach for converting from pixel count to square kilometres is to use this simplified formula::**\n",
+ "\n",
+ "${{Area (km^2)} = (\\frac{Spatial resolution (meters/pixel)^2}{1,000,000})\\times{\\text{Total pixel count}}}$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "af1331f9-b440-4f0e-a161-00cc00ffcd25",
+ "metadata": {},
+ "source": [
+ "Plot the timeseries and the cloud threshold of 25%. If the cloud cover is higher the timestep will be excluded later on.\n",
+ "\n",
+ "Plot the **cloud percentage** with the threshold."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "fdea8b55-0cb5-4985-9913-55ac66a63d94",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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kxYWNKA2K57aAIAjCAKR0IcVbqROdSEVWRjo0Fg9g6srdmH9YNQBg6/4h8xZElCwUwBBEkRCN8Xhrdz+e39aJt3b3IxqjtlQ9kFJCAoCZTMjbV2QBTCIDU1eRDGC27FWvgyEIuVAJiSCKgHXtXbj7hR3oGk7Oommq8mDF8rmq/TmIVPyCiDdPAJMoGxXTVOpgJIphf1yjUlfhxoLD4lOLPzgwjEg0BkcRaX0I60NHG0EUOGxKsjh4AYDu4QCuXbsF69q7TFpZcSI5gCnCElLfWLyF2mnnUOV1YnZdOSrcDvjDUezsGTV5dUSpQQEMQRQw0RiPu1/YkXE+DXvs7hd2UDlJI6IxXhjQmK+ENKMIS0hC+ajcDY7jYLNxmMfKSPuGzFsYUZJQAEMQBcy7HQOTMi9ieABdwwG82zFg3KKKGJZ9AbLPQmK0prVSFwNi/QtjfqKMtHUf6WAIYyENDEEUML2j2YMXJc8jcsNaqAHA7ch9/1df4YbHaUMgHEPnoF/QxBQymQOYagDANsrAIBrj8W7HAHpHA6iviE/npgGX+kEBDEEUMPUVHk2fR+TGL+pAymd3b7NxmFFThp09o9jTP168AUxLNQDgs75xDI6HMKXMlemlRQ8J6Y2HSkgEUcAsbq1BU1X24IRD/CS6uLXGuEUVMck5SNLM1thU6mLpROoTecAwqn0uzKqLB2fbStQPhoT05kABDEEUMHYbhyuXtGb8HcsPrFg+l9LYGsECmFyTqMUwL5iOIhHyZsrAAMD8ltLVwZCQ3jwogCGIAufj7nj7anqM0ljlwZpLF1D6WkMmQhEA0jMwrBOpWIY6Ci686QFMCXcikZDePEgDQxAFTO9IAM9v6wQA3LbsSPzsbztRX+HGg9+cTwJCHQhI9IBhFFsJKWsGhgl59w8hGuNL6rgjIb15UAaGIAqYJ97ei3CUx8IZU3Dy7DoA8bLSibNrS+oiYhT+ULwdWm4Jad/ABCIF3krN87wQwEwtTw1gjmyogM9lx1gwgt2HxsxYnmmQkN48KIAhiALFH4pi7dt7AQDfXdIKryv+dRZ7lRDaIreE1FjpgdthQyTG4+BQYd+Bj4eiwrGVHsA47DYcN70KQOnNRWJC+my3CySk1w8KYAiiQPnz1gMYnAijpcaLs45pFLICYq8SQlukTKIWY7MVz1Rqln0pc9lR5p6sPkga2g0ZuSzTsds4rFg+N+PvSEivLxTAEEQBwSZOP7e1Ew+/sgsA8O2TWmG3ccJFNRiJIUYdD7rglxnAAKKRAkUSwKTrXxhssOPW/aWVgQGAZW1NWHPpApSnBXYkpNcXEvESRIGQySiLAwTjMLGwNBCJwueir7fWTITkiXgB0VDHvsIW8uYLYOYlDO0+7R3DSCCMSo/TqKVZgmVtTVi/owf/b0tcVH/m0Q347WULKfOiI5SBIYgCIJtRFg/g5j9uw7r2LngcyYsqlZH0QUkGhjnwFnor9aFEF022AKauwo2WGi94Hni/RA3txDonmw0UvOgMBTAEYXFyGWUx2O/ZfB4S8upDQFEGJmFmV+gBTAYX3nQWlKgOhnFw2C/8f28iY0XoBwUwBGFx5Bhlse6YAAUwuqCkhMREvPsHJgrajTVfCQlIzkUqRUfeWIxHlygD0ztCAYzeUABDEBZHjlEWK21MUAlJF5SUkJqrvHA5bAhHeRwc8ud/gUWRFMAIQt4h8HzhBmtK6BsLIiTy+jk0Giy5bWA0FMAQhMWRY5TlcVErtZ7IbaMG4q3Uh9UUfit131gIQO4A5uimSrgdNgxNhItm/pNUOhPB6dTyuKg+FI1haCJs5pKKHgpgCMLiyDHKYhdW0sDog5ISEiDqRCrgkQJCBqY8e0Dtcthw7LS4oV2p6WBYADOztgzVvngHFulg9IUCGIKwOGKjrPQgJt0oiwUwpIHRByUlJCAp5N1boFmJWIxHX5ZBjukkBzuWlg6GlQenTfGiPrGNaP6RvlAAQxAFADPKqq9MvXikG2WxzABlYPTBrzADM2NqYZvZDfnDiCQEyLWJEkk2SrUTqXMwHsA0V3uFsi8JefWFnK4IokBY1taE46dX48SfvQIOwJNXnYDFralDG5PjBAp7cKBVYYGh1FlIDCuVkKIxHu92DKB3NID6Co+kqeWsfDTF54TTnvu+lwl5P+4ewUQoUjKGip2JDqTmai96El2DVELSl9I4sgiiSGBdDj6XHSfOnjrp96SB0ReWgZE6jZohTKXuj7dSm2VwlsnNuanKgxXL5+a0u5fSgcRorPKgqcqDruEA3t8/jBNn16pfeAHANDDTq704MBgPVKmEpC9UQiKIAkLQYGTJAJAGRl+UamCaq71w2jmEojF0DZvTSp3Nzbl7OIBr127BuvaurK89NJbbhTedUpyLxDQwKSUkysDoCgUwBFFABMLxDIzbkSWAoTZqXWHbVW5ZxG7j0JJopd5rQhkpl5sze+zuF3ZkNdpLdiBJC2CYkLdUdDBjwQiG/fGW6eZqjyDiPUQaGF2hAIYgCoh8IlIPlZB0IxyNCUJWuRkYAGg1cSq1HDfnTMgpIQHiAGawJMzcWPal0uNAhcdJXUgGQQEMQRQQ+YzUSAOjH+Jt6nHJP3XOYAGMCa3UctycMyE3gDmmuQpOO4e+sRAODBau+7BUWAfStCnxLFt9JZWQjIACGIIoIFgA43Fm/uoKs5CohKQ5LPtlt3Fw5enEycTMqeZ1Islxc87EIYkeMAyP0465zXFDu1Lwg2EC3mnV8e3HMjAToSjGghHT1lXsUABDEAWEP5y7C4aNEqBZSNojlO+cdnCc/C4iwczOhBKSHDfnTEhx4U0nOdhxSPpCCxTBxK7aCwAocztQlvgu9o5QGUkvKIAhiAIiXxcMlZD0I1/wmI9kADOBmMFTqcVuzumkuzlnQm4JCQAWzGCGdqWTgWlOBDAAlZGMwPAAJhKJ4Ec/+hFaW1vh9Xoxa9Ys/OQnP0EsljTe4nkeK1euRHNzM7xeL5YuXYrt27cbvVSCsBz5RLwUwOjHREiZiR2judoDh41DMBJDtwl35cvamnDHuUdNejzdzTmdcDSGwcRQQjkBDMvAbD84UvRt/eIxAow6QchLAYxeGB7A3Hffffjtb3+LX//61/joo49w//334z//8z/xq1/9SnjO/fffjwceeAC//vWvsWnTJjQ2NuLMM8/E6Oio0cslCEsRjMQDfU/WNur4V7rYLxhmoGQStRiH3Wb6VGqHLfWUP6+lCm/efkZOE7v+xBRqh41Dtdcp+bOmT/FiarkbkRiP9s5hZQsuEMRjBBhCJxKVkHTD8ADmrbfewle+8hV86UtfwsyZM3HBBRfgrLPOwnvvvQcgnn1ZvXo17rrrLpx//vloa2vD448/jomJCTz55JNGL5cgLIXkNmrSwGiO4MKrMAMDADPYSIE+c0YKvNPRDwA4IaF1CUXyuwKz8tHUcjdsMhyEOY7DghLwg4lEkxm1aSkBTLyEdIgyMLpheACzZMkS/OMf/8Ann3wCAHj//ffx5ptv4txzzwUAdHR0oLu7G2eddZbwGrfbjdNOOw0bN27M+r7BYBAjIyMp/wii2Minw6ASkn5MsDlICjMwQLKV2gwhbyzG452Ez8t585oBSGuvluvCK2Z+CTjydo8EEOMBp51LMfpjg1ephKQfhgcwt99+Oy666CIcddRRcDqdmD9/Pm666SZcdNFFAIDu7m4AQENDQ8rrGhoahN9lYtWqVaiqqhL+tbS06PdHEIRJ+PO0UbPMDJWQtCegcBK1mFYTp1J/0juKoYkwfC47vnh0/PzaNxZCOJp78GcyA5N7CnUmmKHdlr1Dsl9bKBxMDHFsqvKmZKjIzE5/DA9g/vjHP2Lt2rV48sknsWXLFjz++OP4+c9/jscffzzleeltijzP52xdvOOOOzA8PCz8279/vy7rJwgzkWxkRyUkzVE6B0mMmSWkdz6LZ18WzpiCunI3nPb4+TRfiUNJBxLjuOlVsNs4dI8ETJsBpTedQ/F9KS4fAckSUi+NE9ANw6dR33rrrfi3f/s3fPOb3wQAHHvssdi7dy9WrVqFb33rW2hsbAQQz8Q0NSWFZb29vZOyMmLcbjfcbvlfMIIoJAIShzn6w9G8QT8hjwkNMjBCK/XAOGIxXpamRC1M//L5WbWw2eLljoPDAfSMBFLEp+moCWB8LgeOaqzA9oMj2LpvCE3HZv+cQoVlYNK3IZWQ9MfwDMzExARsaUp4u90utFG3traisbER69evF34fCoWwYcMGnHTSSYaulSCshiAkzWNkF+OBUJ7SACEPLTIw06d44bBxCIRjhl7YeJ4XMjBMwMt8SnryZAgEF16JgxzTSZaRilMHc2Bwcgs1kCwhDfvDVNLVCcMDmOXLl+OnP/0p/vrXv2LPnj149tln8cADD+Bf/uVfAMRLRzfddBPuvfdePPvss2hvb8cVV1wBn8+Hiy++2OjlEoSlYNOo84l4ASAQogBGS/Jlv6TgsNswPXGh6zBwJtKu3jH0j4fgcdpw3PRqAEBDIkNwKI9GI5mBke7CK2aBIOQdUvR6q3MwbYwAo8rrhMsRv8RSJ5I+GF5C+tWvfoV///d/x3XXXYfe3l40Nzfj6quvxo9//GPhObfddhv8fj+uu+46DA4O4oQTTsBLL72EiooKo5dLEJYiXxbAabfBaecQjvLwh6OognTfDiI3E6H4TBs1GRgg3om0p38Ce/vHceLsWi2Wlpe3E91HCw6bIlxUG6RmYFSUkIBkJ9KHncMIRWLC5xcLyQDGl/I4x8XLdJ1DfvSOBtFS48v0ckIFhgcwFRUVWL16NVavXp31ORzHYeXKlVi5cqVh6yKIQkCKmZrHaUc4GhEuuIQ2+BMZLTUZGCDeibThk0OGDnV857Ok/oWRDGCkZmCUBTAza32o9jkxNBHGjq4RzEs49BYDPM+LxghMzlA1VMYDmHxZLkIZxRUKE0SRI2RgXNm/uuQFow9qnXgZyU4kY0pIPM/j7TT9C5DUaPTkKG+MByMYT+iulAYwHMeJBjsWlw5m2B8WxN2ZhNBCJxKVkHSBAhiCKCDYRdSdZZQAQF4weiGUkFRmYGYa7AXzWd84+saCcDlsOF6U/WAZmFxW930JAa/XaRemKytB0MEUmSMvE/BOLXdl1KUJnUjUSq0LFMAQRAGRb5QAIPaCIRGvlmjRhQSkTqXmef2nUrPuo/kt1SkXWXZxzVVCEpeP1LTkMx3MliLLwCT1L5nbw8nMTl8ogCGIAoJ1IeXTwABUQtIav4RtL4XpU7yw2zj4w1FDSgti/xcxDYnyxuBEGMFI5mNFrf6FcXxLFTgunrEopot5Uv+SLYChEpKeUABDEAVCNMYL3i7Z2qgB0sDohT9RQvKpLCE5Ra3Ueutg4vqXxADHWTUpv6v2OeGy527z7VPpAcOo8DhxRH28i3RbEZWRDuYJYOqohKQrFMAQRIEg1rTkygIIGhgaJ6ApwhwqlQEMIB7qqG8n0t7+CfSMBOGy2wQdCoPjOFEZKfMFVqsMDCAytCuiAKZTcgmJAhg9oACGIAoEcUbFncNLgzIw+iC0UassIQHx1mIA6NBZyMvKR/PS9C+MfEJe5sI7VWUGBkgGMMXUidSZZYwAg5WQ+seDiJAztuYY7gNDWJNojMe7HQPoHQ2gvsKDxa01sBs4p4XIT3KMgC3nDB3SwOiDViUkQCzk1TmAYe3TaeUjRkMeIa+WGRiWAfrgwDAi0Rgc9sK/f+5MdCFNn5I5gKktc8Fu4xCN8egfDwkBI6ENFMAQWNfehbtf2IGu4eRJrKnKgxXL52JZW1OOVxJGwoSWufQvQNIjhiZSawfP85p1IQHAzKn6T6VO0b+0Znb8ZRmCbF4wWgYws+vKUeF2YDQYwc6eURzTXKX6Pc0kEI4KGqFsGRibjcPUchd6RoLoHQlSAKMxhR8CE6pY196Fa9duSQleAKB7OIBr127BuvYuk1ZGpCO1hOFzxe9LyAdGO0LRGGKJjmctNTB7+sd1a6U+MOjHweEAHDYOC2ZUZ3xOvlZqLQMYm43DvCLSwbBzptdpxxRf9pEdyU6k4um+sgoUwJQw0RiPu1/YgUynT/bY3S/sQDSmv1cFkR+pGQAqIWmPOJulRQamZYoPNg6YCEUFnYnWsOzL8S3VQlCbDmulztQlw/N8chK1BgEMkPSDKQYdzEHRCIFcHjkk5NUPCmBKmHc7BiZlXsTwiN9lvJsYBEeYCwtI3PlKSInfT1AJSTPYtnfaOTg10G64HDZMS+gm9OpEeqdj8viAdAQRb4bswLA/jHA0fvMytdylyZqYkLcYWqmFDqQpuYc0khuvflAAU8JITWlS6tMaJGfx5P7ast9TBkY7kgJq9dkXBhPydujkBZP0f8k+8bohRxs1Kx9VeZ05R1fIgc1E+qxvHIPjIU3e0yyYgHdahiGOYuqohKQbFMCUMKw2q9XzCH0RApg8GgzygdEels3SogOJoWcnUueQHwcG/bDbOCycMSXr8+oTGZhhf3iSZkpL/Quj2ufCrLr4371t/5Bm72sG+cYIMKiEpB8UwJQwi1tr0FTlQbbqLYd4N9LiHClowjiEOUikgTEcrSZRixGmUutQQnonkX05dloVyt3Zm00rPQ54Ehm79BLHIY1ceNOZ31IcOph8YwQYQgCTY+YUoQwKYEoYu43DiuVzM/6OBTUrls8lPxiLEJCpgaEARjsEF14dSkh6jBPI5//C4DhO0MH0pJU49MjAAMXjyCs5A1NJ85D0ggKYEmdZWxPWXLoA1WltgI1VHqy5dAH5wFgIqcMEWQmJfGC0Q5cS0lT9plK/zQY4ZvF/EcMyBOmt1HoFMMzQbtv+oYLtcAxHYjiQ0MD0jARy/h1s+x4aDSJWoH+vVaEAhsCytib8+5eOFn6+91/a8ObtZ1DwYjGktlGz35MPjHZI1R/JoaXGC44DxoIR9GsoaO0eDmBv/wRsHLBoZnb9C4NlCNKFvFq3UDOOaCiHz2XHWDCC3YfGNH1vI1jX3oWT73sFkUQw8oOnt2HJfa9k9cxiYxgiMR6DE4UtXLYaFMAQACC0SwLA4fUVVDayIAGhjJH7a0saGO2Rqj+Sg9thR3OV9lOp2fyjtmlVqPBkN1hjNGTpkhEyMBprYBx2G46bHnfh3bK3sHQwzPgzvRyUy/jT5bChpizehk5lJG2hAIYAEHcaZYwHIyauhMiGVCEplZC0h5WQvFkM4ZTSOpU58qoX8kZjPN7a3Y+nN+0HAHxOQvYFSLZSTxLxJi62UzXOwADJMtLWAtLBqDH+pE4kfaAAhgAABMOiACZEAYwVEbxI8rVRCyUkmn6rFX6JHjxyYZ1Ialup17V3Ycl9r+CiR97GW7vjGZg/b+mUNApEEPGmaWD6dOpCAkSOvPsLJwOjxvizjjqRdIECGAIAZWAKAakaGCY0DUVjiEQpiNECPdqoAW3M7LLNMxuaCEuaZ5ZpHlIkGhN0OVprYIBkJ9KnvWMYCYQ1f389UGP8mZyHRBkYLaEAhgAABEV6ibEglR6sCMuo5GvlFf8+EKEARgv0KiGJO5GUoMU8M2GcgKiENDAeAs8DNg6CfkNLppa7cViNDzwPvF8ghnZqjD9ZkHiIAhhNoQCGAAAERXfqE5SBsSRSswBuhw1sttwElQM1QWr2Sy4zBTM7ZVOptZhnxvQZo8GIkH1lmYLacrdugn6WhSkUHYwa48+kBoZKSFpCAQwBIFUDM0YXPUsi1UyN47ikDiZEGRgtCAgZGG1PmS01PnAcMBqIYEBBK7UW88zK3Q6h7MgCF71ceMWwuUhbCsSRV43xZ32Oqd+EciiAIQCkamAmqIRkSZIDBfN/bcmNV1v0KiF5nKJWagVlJC3mmYndeJnIVC8TOzELZiQ7kbQ28tMLZvzpdqR+B/MZfwoTqamEpCkUwBAA0rqQqIRkSQIR6WUM8oLRFr1KSIC6TiSt5pkJbrwsA2NAAHNUYyXcDhuG/WHdJnLrwbK2JrQ1VwIAvnPyTDx11efzGn+KS0iFEqwVAhTAEABSMzBjFMBYkmQZI/9FlLxgtEXfAEb5TCSt5pmZkYFxOWw4dlrC0K5AdDCM8cT3aumR9Thxdm3e7csyYIFwDKN0ftUMCmAIAKldSBN00bMkci6iNE5AW/w6zEJitE5VN5WalTVq07qF5Mwza0hrpTZCAwOIy0iFoYNhsJu8co+0kqLXZUdFYio46WC0Q9uCLlGwUAbG+khtowZIA6M1ekyjZrAMjBozu2VtTYhEedzw1FbMrPVh1fnHYXFrjeQOooa0eUhGZGCApJC3UDqRGKzMXu6Wfgmtq3Rj9FAEvaMBHF5frtfSSgrKwBAASANjdXiel3UR9VAJSVP8Msp3chGb2anRR7B26mOnV0sqa4ipS5tI3WdUAJNw5P24e6SgzjtjCgIY8VRqQhsogCEApHUh0UXPcgRFhnSSNDCJTiXKwGgD2456lJCYiHckEMHQhHJX2s4hPwBg+hSv7NeyDMyh9DZqnQOYxioPmqo8iPHABweGdf0srQhGosLw2zJZAQy1UmsNBTAEgPiXkkElJOshzqR4HNLbqEkDow16TKNmeJx2NFXFL257VJSRDgzGA5hp1coDmJ6RAALhKEYD8XPAVJ01MIBosGOBzEUaCyTPj0oyMGRmpx0UwBAAUktI5N5qPVgLtdPOwWGXEMAk/EqohKQeueU7JcwQOfIqhWVgpinIwLCL63goKqzB5bChUqJIVQ3MkXfL3iHdP0sLxoPJYFZOmY68YLSHAhgCQGoJKRzlUzIyhPkkTeykXUBJxKsd4qneepSQAKB1KmulVtaJBACdg/HXTleQgSlzO4QumfbOEQDxDiSO02eMgBgWwGzbP1gQHimjwXiZT2oHEoNKSNpDAQwBIDUDAyTvMghrINeHhFneUwCjHvE21C8Do64TaSQQxkiitKEkAwMkMwTtnXEtit76F8YxzVVw2jn0jYWEMpiVYedGOeUjgEpIekABDAEgNQMDUCeS1QjILGEIGRgqIamGBTAuh023wYZsqGOHQi+YzsSFf4rPCZ/CcQdMB7P9oLEBjMdpx9xmZmhnfR3MGMvAyA1gqISkORTAEABSjewAYJx0MJaClTGkZmBolIB2+BPfBb3KRwAwc6q6DAwLYJRmX4BkhmDHwUQJyaAABigsP5ixRAamzC3veKhLlJBGAxES12sEBTAEgGQGxpUQiFIJyVoIGhiJF1EaJaAd/pC84FEJh9XEMzBDE2EMTcifSi0IeBXoXxgsA8Ns8vV24RVTSI68rAup3O2U9bpKj0MYAkk6GG2gAIZALMYLvgZTyuJfSiohWYukBkbaV5ZEvNqh5xwkhs/lEOz8lYwUSAYwPsVrqK9MnVhtRgZm+8ERy2cnki688o6HlKnfpIPRBApgiBT9S01Zop2ySAOYaIzHW7v78fy2Try1ux/RmPW7HgDlGhirXwwKAWYroIcLr5iZKoS8WpSQWADFMDKAmT7Fi6nlbkRivCAitiqjMucgiUkKeSkDowU0C4lI6UCqYRmYIiw9rGvvwt0v7BAs1wGgqcqDFcvnShp4ZyYBmVkAYZQABTCqkbvtlTKztgzvdAwoaqU+oGEJiWFkAMNxHBYcVo2XdvRg674hLJpZY9hny4Xd3Mlx4WUIQt4RysBoAWVgCASj8RM0xwFV3uIsIa1r78K1a7ekBC8A0D0cwLVrt2Bde5dJK5OG7DZq6kLSDGHb65yBmTFVuZkdy8AoGSPAaKhIC2AM1MAAyblIVnfkZRqYCiUBDPOCoQyMJlAAQwgZGLfDhrJEC6bR4wT0LO1EYzzufmEHMr0je+zuF3ZYupzEhKRu2SWkWJ5nEvmY0HGMgJgZU+IBzJZ9g7K+A4FwFH2J2UVqMjD1aSWkz/rGDP1OMEM7q5d4x0LKMzB1VELSFCohESkdSOxLaeQ4Ab1LO+92DEzKvIjhEZ/k+27HAE6cXav68/SAjRKQbmRHJSSt0HMSNWNdexdWvLAdALC3fwIXPfK25O/AwUT5yOeyo9onrzNGzGs7e8EhGdR/6w+bDC2xsknYgxNh3Pj0NgDWLPEmu5BIA2M2lIEhkhkYp13wNjCqjdqI0o5Uxb+VOwOSF1GZXUgWKyEVoohabw0M+w70jaW2T0v9DohbqJVa/7M1pO8No0qs69q7cFMiaFHz+UYcX8kuJCUaGDZOwLrnmkKCMjBExgyMERqYfKUdDvHSzplzG1U5oNan1fbVPs8M5F5ExRkYnucNmWmTj0IVUU/omIHR4jugtgPJqO+h3p9v1PE1pkEX0iHKwGgCZWAIwYXX7UxqYIxw4pVT2lHD4tYaNFV5kO3UxyF+olvcat3OB6Vt1AAQjJivgylkEbWePjBafAfUmtgZ9T1U+/lX/OFdPPjyp3h+Wye27R9KMfwz8vgaU5OBSQQw/eMhhKPmfy8LHcrAEBkzMGMGlJCMKu3YbRxWLJ+La9dumfQ7FtSsWD5Xtzk3WuCXGcCInzcRiuo2hFAKZt/hq0XPEpIW3wG1GRizS6xS3/eNXX14Y1dfymPVPicOq/Hhk55Rw44vNQHMFJ8LDhuHSIxH31gQTVXKRdeESRmYzs5OXHrppaitrYXP58O8efOwefNm4fc8z2PlypVobm6G1+vF0qVLsX37djOWWhKINTDMXXLCgBKSkaWdZW1NWHPpAnjSnGwbqzxYc+kCS5cwAMAvcxaS3cbB5bDGRGqz7/DVomcJSYvvgFoPGLNLrFLf98LPteAbi6ZjcWuNYLo3NBHGBweGc3bbaXl88Tyf1MAoKCHZbFyyE4nGCajG8AzM4OAgTj75ZJx++un429/+hvr6euzevRvV1dXCc+6//3488MADeOyxx3DEEUfgnnvuwZlnnomdO3eioqLC6CUXPSwD47bbhEm2RrRRs9JO93Ag490Th3iAoVVpZ1lbE+a+/hm2JAbGnX1MA35zyUJL3vWnEwjJy8AA8WAnFImZLuQ1+w5fLXp2IWnxHVDrAWP091Dp59/7L8emfFcnQhHsG5jAnzYdwO//2ZH3c7Q4voKRmDB2RUkbNRAvI3UNB6gTSQMMz8Dcd999aGlpwaOPPorFixdj5syZ+MIXvoDZs2cDiEe4q1evxl133YXzzz8fbW1tePzxxzExMYEnn3wy6/sGg0GMjIyk/COkEYyINDBCG7X+Fz1W2smEXqWdHtFdTzTGF0TwAojaqCV2IQHWGSdg9h2+WvTUwIi/A9mOxFzfgUg0hu5ER4vSOUi51mBEiVXp5/tcDhzVWIkvzm2Q9DlaHF/i5gamF5RLnWBmZ82AvZAwPID5y1/+gkWLFuHrX/866uvrMX/+fDzyyCPC7zs6OtDd3Y2zzjpLeMztduO0007Dxo0bs77vqlWrUFVVJfxraWnR9e8oJkIRsQaGtVEb4wPDSjvp/hV6lHZiMT7lpLF/wK/Ze+uNX0kGxiJeMIUuombb3qeTDwz7DjRWTb7APvCN43N+B7pHAojGeDjtnCAQ1XINRpVY1Xy+kccXy0z7XHbFAR0zDOyhEpJqDC8hffbZZ1izZg1uvvlm3HnnnXj33Xfxgx/8AG63G5dffjm6u7sBAA0NqVF1Q0MD9u7dm/V977jjDtx8883CzyMjIxTESIR1qYi7kIx04l3W1oTe0SB+/Hxc5/SNRdOx6vzjNL/jG5wICelfANg/OGGZFuN8KMkCeCziBSMWUYuN0oDCEFHLFVArYVlbE86c24h3OwbQOxLAz9Z9jK7hAGx5tgkrHzVVefM+V9YaRgOor4hf9I3aL0o/P9fxxdDq+FIj4GUkW6kpA6MWwwOYWCyGRYsW4d577wUAzJ8/H9u3b8eaNWtw+eWXC89Lv6jku9C43W643cbO7igWxBkY9sUMRmKIRGNw2I1J0o34w8L/V/tcupw0Waq90uPASCCCiVAUA+Mh1Bo880UJTKQoTwNjDREvkLzD/vHz21Nq/40F4AOjZwlJjN3GCU7Qn/aO4dev7sKLH3bhK/OmZX2N2hbqXGswA6Wfz46vdB8Yj8OG1d+cp9nxpcaFlyHMQ6IMjGoMLyE1NTVh7txU3cPRRx+Nffv2AQAaGxsBQMjEMHp7eydlZQhtEDIwDjt87uRJ2siJ1EMTyQBGHMxoCTthtNT4hC6G/YOFUUZS0srLSkhma2AYy9qa8IcrPif8PLepEm/efoalgxdAXEIy7n7v3GPj2+S1nYdylnPVtlAXE8vamvDm7Wfgqas+j9uXHQkACEdj+JyGk62ZP5aSDiQGjRPQDsMDmJNPPhk7d+5MeeyTTz7BjBkzAACtra1obGzE+vXrhd+HQiFs2LABJ510kqFrLRWYkZ3LYYPbYYfTHs9+GDkPaUgUtAzrFMCwDExjpQcticF5+wcmdPksLeF5XtFEZCuOE2BDBwHA6bBZtmwkJrntjTtdHt1UgZm1PgQjMby6szfr81gGRs0U6mKCZXCuXXo4jp1WhSgP/OX9g5q9/2giA6NUwAskNTAk4lWP4QHMv/7rv+Ltt9/Gvffei127duHJJ5/E7373O1x//fUA4qWjm266Cffeey+effZZtLe344orroDP58PFF19s9HJLgmA0OY0agKHjBBhiV82RgD4BDBsWV1/pQUtNIoAZtH4AE47ywkwXj0NOBia+H61QQmL0iGbA+A0MkNWgRECtFo7jhCzMix9md5HVuoRUTHx90XQAwP9uPqDZe7IZceoyMPESUt9YqCBmgVkZwwOYz33uc3j22Wfx1FNPoa2tDf/xH/+B1atX45JLLhGec9ttt+Gmm27Cddddh0WLFqGzsxMvvfQSecDoBDOyY8ZnSSGvWSUkfS5sPSkZmPgJvxA6kVgLNQB4ZLVRW0cDwxB3XhjRqq+WaIwXSqxGlpCAZBnp1Y8PZc2GUgkpO8uPa4bLbsP2gyPYcVAbW42xYPw8pUYDM7XcBY6LH1sD46H8LyCyYooT75e//GV8+OGHCAQC+Oijj3DVVVel/J7jOKxcuRJdXV0IBALYsGED2trazFhqSSAY2SXu7ssMdONlDBqSgYlfPBsq3ZieyMAcKIAMDDOxs3FxobVUBB8YCwUKqRkY66wrG2L9kN4i3nSOaa5ES40X/nAUr+08NOn3PM8nS0gKPWCKmSllLnxxbj0A7bIw7KZOTQDjsNtQW+YCQGUktdAwR2JSBsZIN16GWPeil4i3O9Gd0FCV1MAcKAARr7gLRk7Ltyehl7FSpqPQMjDi7BUrsRpFvjJS31gIwUgMHIeMHjIEcMHCeBnpuW2dQrelGlgXklIXXkbSzI6EvGqgAIYQZWDihwO7uzBiIjUQv5NMKSEFIuB57WvD7G6nocKDlpp4yr1z0I+YxevQSn1IBBGvhUpI4jtOfzhq/W0fSgaPan1WlHBuokPrlY97J3WTsexLQ4VHuPkgUjl1Th3qKtwYGA/htRxiaKkwXWCFCg0MIPKCoVZqVdBRTwhdSG4nE/EyN15jLnxjwQgiogtZNMZr3sIdisTQNxYvUzVWedBY6YHDxiEUjaHH4mlcJR4wgDUDmO60oY5ifY8VUdL9pSXHTa/CtGovJkKTy0ikf8mPw27D+fPjPjpalJFYVrpM5fGQbKW29rnH6lAAQwgZGKavYCJeo7qQWPbF7bAJLdxal5EOJdp3XXYbpviccNhtaKqOp3GtLuRVOkzQaj4wkWgspY0asH4ZSZyBMYN4GSnujfW39tQyUudQXL9FHUi5+VqijPTKx73oH1OX8RCceD3OPM/MTbKVmjIwaqAAhhA0MG4nE/GyEpIxFxcWwEzxuVCZODFoLeRld/71lW5BR1IoXjABoYQk7+tqlVECjP7xEGJ8XIzMypVWWVs2JnScRC2VcxI6mH98lFpGogyMNI5oqMDx06sQifF4bps6T5jkKAG1GRhy49UCCmCISRkYn8EDHVkHUrXPiUpvIoDRuJWadb80VCbFjkIAY/FOJCUuvOLnW6WExPZBXYVb0FlZPQOjdNtryfyWajRXeTAWjOCNT/uEx8kDRjpMzKu2jJQcJaAyA0MlJE2gAIZAMJKqgSk3uoSUKBelBjDaZmDEHjAMJuS1fAlJtYhXffeFFiTb2D1CRsNIt2clGDUHKRccxwnjFv4m6kY6QBkYySw/Pu4J81HXCLYfHFb8PoIGRm0GhkpImkABDCG0F7rtaU68hpWQ4hmYeAkp/tlajxPozpSBKRA3XqUXUUEDY5EshzgL5nNZq7yVDSuUkAAIOpj1O3qEG46kBwwFMPmo9rlw5jHxWXpqsjBjmnUhJduotey4jMZ4vLW7H89v68Rbu/uL3unX8GnUhPUQhjlO6kIyVsRb7XMKrapaa2B6RSZ2jOnMC8biGhilVvYei5aQGirdwp2n1UtIVsjAAMCCw6agodKNnpEg/rmrD4tm1ghzeSgDI40LFk7HXz/owvPbDuKOc46W3XrO87xwTlTvAxM/D4UiMYz4I6jyqStJAcC69q5J07ibCmDauxooA0MIGRiXPU3Ea7gGRiTi1VgDw0S8YsMvVkLqGgloYnKlFyzAlHsRFbIcVgtgKjzwJf6WCYusLRsBi2RgbDYO5yQuQn/9oFsQ8E7xOQ0fcVConHL4VNQnPGFe+Vi+J0wwEhPsHtQ48QLxmwuWbdZCB7OuvQvXrt2SErwA8fPetWu3YF179nlahQwFMESGDIyxRnbDLAPjdaLSG/9srTMwzOuFpW4BoK7cDY/TBp4Huoatq4NR3EZtsVECYg1MsoRkbQ2MVUpIAHBOGysjdWNP3zgAyr7IwWG34V8WKPeEETuTq5lGzaiv1MaNNxrjcfcLO5CpWMQeu/uFHUVZTqIAhhBlYNJ9YIy58A2maGB0EvFmyMBwHCeUkaws5PWnGQ1KxWvRDEx9pduSYw4yYZUSEgAsmlmDugo3RgIRPPPefgDUgSSXrwueMD1Y+/ZeWToRYYyASxtXZq06kd7tGJiUeRHDA+gaDuDdjgFVn2NFKIAhJnUhGa6BSQQrVT4nqrza+8CMBSOCIFmsgQGQnEptYSGv0lZepoGJxHhLlMjYnWZjlaiEZPEAxgpt1Ay7jcOyY+JZmFcTrrw2jivKO2u92NU7BqedQ4wHfvRcOy565G0sue8VSSWWMY30LwwhgFHpBSM1ACrGlm0KYEqcWIxHOBo/AZrtxDvF5xLaqLXsQmL6lwqPY5JeQOhEsrCQV3EXkuj5ZmdhgpEoBsbjmbaGikLqQop/B6xQQgKAqeWpAfjf2rslX4BLHaYTYec7hlSdSNKFV6MARqMSkrgsrsXzCgkKYEocZmIHZHbiNWLY3pDYyC5xctBSxNuboYWaMV3IwFi3hBRQOI/HaedgT6S6zR4nwO4yXXYbqn1OeF2FYWTHPHSskIFZ196F1S9/MunxYhdqaoEWOpFxwYVX4wyMygBmcWsNmqo8yFbU4hDvRlrcWqPqc6wIBTAlTlBUWmAZGPEXVO8791iMF7ItKUZ2GpaQujOY2DEKYZyA0EbtkHcR5TguaWZncqDA0tdslEOyQ8raIl6lAmqtKWWhphZooRMZ0ziAqRNKSOpKO3YbhxXL5wJA1iBmxfK5ws1MMUEBTInD9C8cB2GQosdpAzvW9S4jjQYiYOfcaq8+Il7W/VKfpn8BkiWkA5bWwCSmUSu4iFrFC0bcgQQkW7ytn4GJH/8+kwOYUhZqaoEWOhHtNTDx78IhDdx4l7U1Yc2lC1KaFIC4nnHNpQvIB4YoTsQdSGzIIcdxSR2MzhcY1oFU5rLD5bAJbdSjwYhm5atMYwQYLAPTNxayrK29mk4Yr8uW8h5mkb4PvIUSwCg0EdSaUhZqaoEWOhHWhVShVQCj8TiBZW1NePWWpSmPHTbFV7TBC0ABTMkjeMCkuVIaNdAxOQfJBQBCBobngTGNAgom4s2kganyOQVb8AMW1cGo6YSxihdMehasUES8VtHAlLJQUwu00Ilo5cLLYBqYsWBEs5snsaYRAD7qHkXfWPHOW6IApsQJJk7QrjR9BfuSjukcwIgnUQPxO10WTDGDO7UwE7tMAQxgfR1MQBjmKP/rapWJ1OnTwL1OJuK1ZtaLwYz2zC4hlbJQUwty6UTYz/l0IqMadyGVux3C91NtKzWD3RDYbRyOaqwAAPxzV1+ulxQ0FMCUOCxiT8/AMKGa3heYYdEcJIbWQt5MJnZi2EgBq2ZglE6jFr/GOgFMagbG8iUkFdteS7S4AJc62XQijVUeSToRrbuQOI4Tvg9alZFYAONz2nHaEXUAgDc+pQCGKFKCzOU1vYSUuMCM6ezGOySag8TQspU6FuOFk0O6iR3D6hkYNZ0wVinViOcgAdab05QNq3QhAeovwER8G755+xn40blHAwCaqz148/YzJG07rbuQAPFUam20S+yGwOOyY8mcqQCANz49pOnEaytBU8BKHJaBSZ/MKmRgdC8hJecgMbTMwPSPhxCJ8eC4+OyjTAhmdhbtRBK6kBSJeBMaGIv4wNQXmog3sd3MLiExlrU14cy5jXi3YwC9owHUV8TLRpR5kY7dxuFziVIbz0PytmM3c1ppYACgrlIbN16GWPD/uZk1cDts6BkJYlfvGOY0VGjyGVaCApgSh2lgJmdgjNHAMA+YKaIMjDBOQINWanbnP7XcDYc9c8KRlZCsOA8pGuOFIFOJkNQKJaTxYETQD7DsATu+zM4M5SIcjQmurWaLeMXYbRxOnF1r9jIKGsGsU8b5bSxxQ6VtBkanEpLLDo/TjsWtNXjj0z68/mlfUQYwVEIqcZIamMwiXr0HOqaLeIFkJ5IW4wRytVAzhIGOFszAiDMnarqQ/CHzZiGxk3OZyy6c/JMamIhl09vibW+2BobQlnKR27jU44+dC61cQkrXbJ0iKiMVIxTAlDjpgxwZ5e7kBUZPhiZS26gBCF4wIwH1n500UMtcPgKS4wRGAxHNOp+0Qpw5Sc+SSYEFMBMmOt5mamNnJaQYn+oGbSXY3ayNU7btCevCBtZGY7zk40/rWUhAMgOjhZkdkDxfsxuEU+bEhbzvfDYgnOuLCfpWljhiIzsxRpWQBBGvd3IGRosSUneOOUgMn8uBqeXxAMpqWRh2EXU7bLAp0DkIGhgTSzXiMQIMn3jQpEXLSGI9ATN5JIqDMtFQV6nnuKSIV7tsXL3GGph0z6ijGiswtdwNfziKzXsHNfkMK0EBTIkjGNlNysAYM5F6yK9vG3WuQY5iplu0E4ndNSntgrGCBibdAwYAHHabEDRPWLQTya9wiCZhfWy25DwuKec4nudFAYwzz7Olo3kJKa1rjuM4oYz0ZhG2U1MAU+Jky8CIJ1LryeB4pjZqloFRHzzlGuQoxqqdSEy7olREmjSyM69Mw8p46fvAa5EW72xMWKiFmtAeOWadwUhMGJRZpmUGJlFCGpwIC+diNUxkcO1ecjjTwVAAQxQZyVEC6SJe/UcJRGO8oHMRZ2CqNMzA5BrkKKZlijU7kdQaqVkhSGAZmPq0AMYqHjXZYGU3K3UgEdpRJmRg8h9/oyI9nrj8pJZqn1O4eTykgeV/QNSFxGAZmPaDwxhI3DAWCxTAlDgsgEn3gTFimKO4yyjVB4YZ2WnYhZTFhZdh1QxMQG0Aw2YhWaKElBpEel3GCMWVomaIJmF95LRSC3OQXHZFWrRscByHOtZKPaK+jCQ2smPUV3pwVGMFeL74xgpQAFPiCF1IJgxzZALeCrcjxaNFKxFvMBIV7jga8gy5Y268VhsnkLyIKvuqWkMDwzrBMmdgrKqBoRJScSOnhKRHBxKDBTA9Ggh5swXdyTJScbVTUwBT4oSyZGCMEPEKLrxlqaK4pIhX3WczZb/LYUspUWUiOQ9pwlK+JAGVQlKzS0g8z08aI8DwOa1tZkcZmOJGzrw3PcYIMJKt1OozMP4MJSQAOCUxF+nNT/ssdX5TCwUwJU52DYz+Acywn7VQu1IeZ7OQxoIRRKLKhW3i0kW+NtimKi9sXNy2X4tatFawE5LHoewi6jN5lMCIPyIcY+k6JKuPE1AbPBLWJpmByX/8jQV0DGA0HOiYLehePLMGLrsNB4cD2H1oXPXnWAUKYEqcbBkYsQZGr4h9cHxyCzWQzMAA6nxosnW/ZMLlsAnPs5KQV9DAKM3AmFxC6kncVVb7nJN0PEkRrzU1MEIJyUkTV4qRchll8vGQfiUkoZVayxJSmtDY67Ljc61TAABvFlEZiQKYEieZgUlvo5bvVCmXpAdMagbGabcJFzc1rdRSTOzETK9hOhjrCHlZ+7PSMobZGphs5SPA+hmYpKcGnSaLEeEmTUIAw7qQtOxAYiTnIWkn4s10vmCuvMXUTk3fzBInlBDxpmdgfKIvql5lJCbinZJBn6LFPCSpJnaMFgua2SXbqJV9Vc3WwLAxApna2H0WD2DSXU2J4kKOiHdcRxGvliUkdsxmmp7OhLxvfdavieeMFaAApsTJloGx2zjhxK3XQEdhDpI3QwAjzENSHsBINbFjWHEqdVDlRZS9TmzEZSTspJwpiBQmUlu+C4lKSMWInEYFfUW8zI1XfQAjtFFnOF/MbapEbZkLE6Eotu4rjrECFMAUENEYj7d29+P5bZ14a3e/JhekbBoYQOzGq08GJjmJ2jXpd1q0UicN1HKb2DFaLDiVWm0njPh1Zgh5s3nAAKJBkxbVwFAXUnHDrCIkiXgN6ELqHwuqPqenjxIQY7NxOLnIXHnp1qJAWNfehbtf2IGu4WSdtKnKgxXL52JZW5Pi983WhQTEdTB9Y/qVkIYzzEFiaDEPSY6IF7CmmZ0/gzGVHMSZNX84KgSlRtGTIwtm9RKSWg8ewtrIysAwDYwO35/acjdsXHwye/9YcJJjtRz8OUpIQNyV9y/vH8Qbu/pwy9lHKv4cq0DfzAJgXXsXrl27JSV4AeL6gmvXbsG69i7F7x3KUkIC9HfjHRQ0MJMzMMI4AYUi3hT/EZklpINDAVXt21oiaGAUtlHbbJygnzFDB5Mc5ZA9gLGsD4zgqUH3ecUIO79JyQCyLHSFDhoYu41Dbbk2Ohh/nvEXTMj7wYEhQYNYyFAAY3GiMR53v7ADmRKL7LG7X9ihOPWYzYkX0N/MjmlgqjKKeOOfrVTEOxqMCHf2UgOYhgoPXHYbojF+UrBoFgHWhaTCi8TMcQK5hNRe4QJi7QBGafaLsDZyRLx6diEB2nQixWJ83gnqjVUezKkvB88DG3f3K/4sq0ABjMV5t2Mg58WUB9A1HMC7HQOK3j/bLCRAXCPWN4DJlIFRW0JiF85Kj0Pyxd9m4zBtCnPktYaQV4tOGLO8YGIxXiTizd6FZNkMDGlgiprkDVr+40/PLiRAFMCo8IIR213kOmaT7dSF7wdDAYzFkRqRK43cQzk1MIk7ZB0CmHA0JgRGGbuQVIp4u4cT+pc8QxzTmc6mUltEB6O2jRpIZhCMDhT6x0OIxHhwHDC1PIOIV5iFZFERbxZbdqI4KJNhZKeniBfQphNJfIOSO4CJC3nXb+/B81u1awgxAyruWpz6PEMI5T4vnVwZGGHcvA4XPlYa4rhU511Gso1a2cVNrv6FwYS8ByziBaN2GjUgynQYnIFh+6C2zA2nPUOGz1kYIl41256wLuWiLkue53OOG2FZGt0CmEr1JSSm5XE7bDknZrOsdt94CDf+cRsAbRpCzIAyMBZncWsNmqo8yHY4cogffItbaxS9f04Rr4wasVyYgKzS44Q9w5dNdQZGYQCTzMBYo4SkRRnDLA0MOxk3VmVuYxd8YCwewFAJqThh57cYnz+4H01c9PXq4tOihCRldte69i7c9PS2SY9r0RBiBhTAWBy7jcOK5XMBYFIQw35esXxuxiBACsEsTryAaFqrLgEM079knhJdpVIDk8t/JBdWc+MN5PB1kIpZ4wRYB1KmMQJA4YwSoBJSceJ12sGSLrlu0nieF7LQenQhAUCdBiUk9j3yZQm49W4IMQMKYAqAZW1NWHPpAkytSL0YN1Z5sObSBYrTfrEYj3A0frBmysCwO2QpRk9yGRQ6kCYLeIFkWUlpF1Iu/5FcWM0LRosyhiDiDRnbGp4cI5B5H1hZxMvz+Ts6iMLGZuOEi30uIW8gnHSx1i0Dk7jROqRGA5Ona07vhhAzMD2AWbVqFTiOw0033SQ8xvM8Vq5ciebmZni9XixduhTbt283b5EWYFlbE/7r0oXCz1PLXHjz9jNU1SxDIq8Td4YLJJvWqodTaq45SIC4hKTss7tHslvY56IlUULqGQma0nacTkDlMEdANA/JpBJStiwYC2BC0ZhlfHcY4SgvXLRIA1O8lEmwimDZGY7Lnt1QCyshHRoNgueVZUAm8pjY6d0QYgamBjCbNm3C7373Oxx33HEpj99///144IEH8Otf/xqbNm1CY2MjzjzzTIyOjpq0UmsgzkaMBCNQWDUSELfduTKJLF16amCyz0ECkiJefziqaPCY3EGOjJoyl3AC6BwyVwcjzgJok4ExttunJ08QKf6bJiwQLIoRZ4WohFS8SPG6Yue/MpcjpzhWDXWJACYUjQnnRrkE8pjY6d0QYgamBTBjY2O45JJL8Mgjj2DKlCnC4zzPY/Xq1bjrrrtw/vnno62tDY8//jgmJibw5JNPmrVcS9A3lkwvhiIxxR06DKZ/4TjAaZ/8xZRyd6KUIX/2OUhAqtp/VKYOJiryH5HbRs1xnGV0MOIAU1UbtWkamNxlPLfDJgThVisjsW3lsHEZO6iI4kDKvLdxnVuogbiNBRupolQHk2/4qN4NIWZg2jfz+uuvx5e+9CV88YtfTHm8o6MD3d3dOOuss4TH3G43TjvtNGzcuDHr+wWDQYyMjKT8KzYGxlOtn9XUSwHRIEe7LWMLoSDi1eHiwjQwmeYgAYDDbhM+X26g1j8eH4pm44DasswBUi6EqdQmdyKJL+qqMjAuczQwyTECmUtIHMcJWT6rCXmpA6k0KJMw0FFw4XXreyyodePNN7tLy4YQPQYLK8EUH5inn34aW7ZswaZNmyb9rru7GwDQ0NCQ8nhDQwP27t2b9T1XrVqFu+++W9uFWoz+DAHM4fXlit8vmKOFGtDXiXc4TwkJiHcijQUjslupexImdnUVbjgU3D1Pn2INL5hAIkPmtKvLApjhxBuOxtA/nl+H5HXZMRaMWG4iNVsPCXiLGyklpKQLb/ZzlRbUV3jwSc+Y4lbqgKCByX5ZZw0h6YOBG2X4wOg1WFgJhmdg9u/fjxtvvBFr166Fx5P9xJaeEchnNHTHHXdgeHhY+Ld//37N1mwV+sfSApgxjTIwWQYF6puBSYh4c2RIKhTOQ1JqYscQzOwskoFRKyI1wwcmLkaMl2BqspQJAet2Iknx1CAKHzki3nLDMjDqSkj5zhfL2prw5u1nCA0Ld557tOSGED0HCyvB8ABm8+bN6O3txcKFC+FwOOBwOLBhwwY89NBDcDgcQuaFZWIYvb29k7IyYtxuNyorK1P+FRvsjpahtoSULwOjr5Fdoo06RwZG6TwkpSZ2jBaLjBPQygnWjFECLIisr3DnFD56LerGy8ptVEIqbqSc4/QeI8CoU+nGK6fsabdxgr3BYTVeyWUjq/nIGB7AfOELX8CHH36Ibdu2Cf8WLVqESy65BNu2bcOsWbPQ2NiI9evXC68JhULYsGEDTjrpJKOXaymYBuawRIZAKw1M1gCGtblGYghr3OaabKPOfneutJW6V6GJHWO6RUS8WrRQi19vZAlJ6EDKI6L2WdTMjkpIpYEwLkVKF5LOAYzaeUhyjRfL3fJ8vqzoI2O4BqaiogJtbW0pj5WVlaG2tlZ4/KabbsK9996LOXPmYM6cObj33nvh8/lw8cUXG71cS8FKSEc1VmDfwIQGGZjsLrxAai11IhhFlU+7eHfIn1vEC4jnISnLwMg1sWMwEe/gRBhjwYjud17Z0GISNWDOLCTBAyZPS6YwTsBiAx1JxFsalEm4iLPgpkL3ACbhBaNQA+OX6drNJmuPSTy/WtFHxpLDHG+77Tb4/X5cd911GBwcxAknnICXXnoJFRUVZi/NVFgJ6ajGCry0oyelrVoJ+TIwLocNLrsNoWgMY6EIqnIEG3IIRqLCHXe2NmpANE5AtgaGdb8oC2AqPE5U+5wYmghj/8AEjm4ypxyZz1lTKmZoYKSOcrDqOAGtgkfC2iR1ftkD6GQXkjEBjNIAYELmMVsu0+fLij4ylghgXnvttZSfOY7DypUrsXLlSlPWY0UmQhGhpHBU4oKqnQYm+wFf5rYjNBHT1AuGdSDZuNx3NayEpFTEqzQDA8RnIg1NDJsbwDANTJYAUyqCD4yBQUL3sLQg0qoi3gkNZlAR1keKiDfZhaRzAFOpTQlJdgZGYgmJ+ch0Dwcy6mA4xLuZjPSRIYemAoGVj9wOW1IDo1kXUvbDQA8zu6QHjCunwDMp4pX32WpFvIA1vGC06oQxY5RAcoxAYWpgqIRUGlhJxMsyMBOhqKLGCVaGla+BkXaDKPaRSUeLwcJKoACmQGAeMLVlLuFA7x8LqlJ8Mw1MthISELfPBnIPO5MLE/Dm8oABgMrEHYKcElIgHBU6nNRmYABzhbxalTEsXUJyWtPITosp4IT1Ya3Ruc5vRgUwZW6HICpmjQhykGu7IAQwMm4QmY9MZVo2Su1gYaVYooRE5Kc/kW2pLXejpswFjgNifLwzqa5CWbeNtAxM4guuodFYPhdehpI2amYC5XHaBBGwEqYLXjDmBTBatVGb0arMdEj5gshkCclaIl4qIZUGyRs087uQgHjG8rO+cfSOBjGrTp5JqV9m16LcEhJjWVsTPu4exeqXP8WJs2vwgzOOwOLWGkMzLwzKwBQILANTU+aCw24TLPLV6GDy+cAA+pSQhvPMQWIk26ilBzA9otJFLuPDfAheMANmlpDi+0e9D0x8//rDUcWTbuUQCEcF3VI+DYxVRbxUQioNJJWQAsZ0IQHJoY5KdDDsJkCvEpKYwcT1aOFhNThxdq0pwQtAAUzBwDQwteXxi/7U8kTLnQodTFBKBkbCHYpcpGZgqhRoYLqH1etfALEb74QhF/1MaHURZa/n+dQBkXqRkgXLI3wUNDBWm0ZNAUxJIGeUgBEZGEHIq6SEJDNjW67CqLRvPPV6ZBYUwBQIA4kWapZ5YZF6nyYZmFxdSGxaq5YaGDYHKU8Gxit/lIDaMQKMadXxDMx4KCoEXEaT7CrQpgsJMEYHIxZR58uCWbULSa4pGFGY+IQSeRSxLHrCUYO6kACRF4yC8/qEXCM7j3J9I7vu1JYrky9oBQUwBUIyAxM/YFgAoy4Dk9vIDhBpYDTMwCRdeKVpYEKRmOQLb7KFWt0Xy+O0CwJUs4S8AaGNWt1F1Gm3wWmPBxJGdCIJQaQEPwivMI3aWhoYrfRHhLURC3MzZQF5nk+2URuRgVFRQpLbtcj+nlGZXZ5AUtIwlTIwhBTEGhgAqCtXHqkz8hnZAWINjA4ZmDwBTLnLAXYDL1XIK1jYq8zAAKKRAiYJebUcKGikF4wQwOQZIwAAPmHMgf6lLTnI9dQgChOv0w4m35jIcJPmD0fBEjOGBDAK5yGFozGEo/GF+pzS1qlGA8OaSqZSBoaQAnPhZRFvnYpUI0OKBkZKjVgubBJ1PhGvzcYJwjmp85C08IBhmC3k1TILYOQ8JHb32CChO86qXUhsO1EJqbjhOE7Q+WXSgrDHOM6YY0GYhyRznID4e+2RWHJm5/ZAOIaIjFl3kWhMKKszSYNZUABTIAyMsQxMWglJkwxM9i8m+9KOaXiBGZYwB4kht5W6V8sApsbcDIxfoy4kILkfjdDAyNEhWbYLSaanBlG45Moysw6keDZY/04bpSUk5ltkt3Fw2aVd1sWiZDkZ9oHEDaiNy38TqjcUwBQAPM+nGNkBohKS3l1IbFaIphqYeDCSaxI1g3UiSRHy8jyvepCjGLPN7AQzNQ0uoskSkv6lGhbA1EvQIQnDHC0WwExouO0Ja8N0fpkyMOzCbkQHEpDMwAz7w7JuNsTHq9RAy+WwCef+URllpP6xpJzBrPZpBgUwBcB4KCoEG7UalpBCJjnxshJSVR4nXkCeF8xIIDkvSsrFMx/TE+MEDpg0TiAQ0aYLKf4expWQ5OiQrDpKICCUkMjrs9jJVSZnF3YjOpCAeOclCyrknNuVlpsrFGgchYaSMnP1LwAFMAUBKx95nDbhhMoCmGF/WOgmkos0I7vsdydKCISTwdgUCfVT1kotxQuG3flX+5yapP5ZBqZz0J+1xVJPtCxjGKWB4XleUQnJH87exmoG5ANTOiStIszPwHAcp2gqtdwWakbSjVdGBoZZepjcgQRQAFMQ9AkeMMmIt8rrFFpjWUQsFymjBKSMm5dDX6LkZeOADw8M5Z3lJCcDI6d9VwpNVR7YbRxC0Zjg8Gskeoh4AzpnOsaCEeFkmm8OEpB6wg0oDMS1huf55LbXIPtFWBtfThFv/LxjhAsvQwhgZAh5lc5NYxl2Oa3UfWmWHmZC384CYGBssushx3FJN16FZSQpRnbJL7f6i8u69i589eF/AojPcbrokXew5L5XsK69K+tr5Ih4BRdeCe27UnDYbWiujr+XGZ1IAZmzTXLhMaiExMpHFR6HpPKL2OPGKmWkYCQGZr5MJaTipzyH19WYkIExLhMndCLJOK8rnd2VzMBID2CEuXwmdyABFMAUBP3jmQ8YtToYKT4wWmVg1rV34dq1W4TondE9HMC1a7dkDWKEcQIS2qi1MrETw8pIZgx11Goatfg99A4S5HaB2WxcsrxlkQBGvA5Pju8GURxI6kJy59fraYUSLxilJc8KBTYZfWOplh5mQt/OAiBpYpd6YVbbiRSUIOJlVtsTOay28xGN8bj7hR3I9Gr22N0v7MhYTmKzdKSVkLQzsWMkO5GMzcCklDEKSAOT9OGRHkRaTcjLHFlddhscEltSicIll4g36cJrZAZGfglJ7iBHRpkCN950V3gzoW9nAcAOmPSIV6sMjBQNDJBZ5CaFdzsG0DWc/W6CB9A1HMC7HQOTfierhKShBwxjOjOzMzgDE47yQkCnSQbGIB8YIYiUoUNKesFYw8yOXHhLi1wi3jED5yAxlJSQBMG/ASWkPmGMAAUwhAQG0sYIMNQGMFI0MG6HTej1V3qHLDUVmul5ckS8WprYMQQzO4O9YMSCVi2EpEaNEpAzRoBhtYGOWpbuCOtT5s6u8xszcBI1o65SvpmdX6FeTkkJSdDAUAmJkIJgYpcW8QoTqRWWkKRkYDiOS7rxKmylrpd4N57peckMTP7P1tLEjtFikhcM6xaycZDsrJkLo0pILAiVMkaAkRzoaI0ARqkgkihMcop4E+cdM7qQDsnRwKgsIckT8SYyMOQDQ0ghm+pbuy6k3IeBIORV2Im0uLUGTVUeZPNs5BBvWV7cWjPpd4IPTJ4MTDTGC9tBjv4iH0wD0zXsR1jGvBC1iPUvWliYe522lPfVCyU6JDbQMdM0YDMgD5jSItdFnJWVzCgh9Y+HJM8oUnrMyp1IPRGKCJ9FGRhCEgPjk9uoAVEJSccMDKAsShdjt3FYsXxuxt+xS/OK5XMz2lJXiTQwPJ9dRNw3FkSMj3+WluKyugo33A4bYjxwcMi4LIyWLdRAsh1Ybx+Y5BgBJSUk0sAQxlOWy4k3cWEvM7CdvjZh0c/zmNS1mQ21bdRSS0j9Kaaq5n8/KICxODzPp8yeEFOnIgMTi/EIRaVlYMpc2VOsUlnW1oQ1ly5AeozSWOXBmksXYFlbU8bXMQ1MOMrnzB6wC2dduVvT+RwcxyWFvAZ2ImnZgQQY4wPD87zQOSEnC2a1gY7+cPw4pwxMaZAcl5KjC8nADIzNxgkNG1L1g2ozMFJvTvvGkqaqRgy3zAe5NFmcsWBECDTSZ0+wDMxEKIrxYESW0CwkSk1KzcAo7UJifOHoBrBO6Xu+2obZdeVY3FqTM+Dwueyw2zhEYzxG/JGsxmJam9iJaanxYfehcUM7kZJjBLS5xzBCAzM4ERaOK6m6J8B6bdRs4CVlYEqD5LiU7CLecgM1MED8+9MzEpTcSu1XOkpAZgkpW0esWVAGxuKwA8bnsk86oZa5HcIBKzcLw/QvQO4upPhnazPQka3Raedw8eLDcOLs2rzZEo7jkl4wOVqpe5j+RYZ4VCpmTKUWOmE0uogaYRbHsmC1Za68QbEYq02kJg1MaZHLrNO8AEZeJ5LSjK1QQpJ4c5qcg2S+gBegAMby9GdpoWYo1cGERAEMm6mUjXLBzE5dBkbQR1R4YJNR5hE6kXIIeXsSGZhGXTIwzAvGSA2MthdRNtFaTx8YJfoXwIIlpBCVkEqJMneyC05s1snzvMjIzuAARqYbb3KYo7x1CiUkiRkYYQ6SBcYIABTAWJ5kz33miJd1IvXJzsAkXXjz1TLVingZPQpcWgGRF0yuDIwOHjAMM8YJaK6BMaCEpET/AiS7kJj2xGz8Gme/CGuTzazTH44KJW8jNTAAUCfTzC6ZsZV3SVesgaEMDCEFoQMpWwZG4TgBqR1IQG6VvhyUWv2zTqThHBkYPVx4GdNNGCegdQBjxCwkpdPArZeBIQ1MKSE26xSXyVlWwsYZn42TO04gqZlTVkIKR3nhpjYXpIEhZNGfL4BR6MYrxYWXIaj0VV5glAYZSS+Y7AFUjw4mdgxWQuobCxqm09C6jdqIUQJK5iABybS3ZQIY6kIqKTiOEzotxZkIsQuv0R03cs3slJaQxO3hUspITANjhTECAAUwlkdooc4S8aoPYKRkYNS3UQPKyzxSxgn0KCxfSKHK6xScOI0qI2mugUm8TzjK62bIJ+wDmToklva2jIhXYUcHUbhkGuholoAXSOrIZJeQZJ4v7DZ5TuvJQY6UgSEkMMAi3iy2zUoDmJCsAEarEpJCDUyegY6BcFQoL+nRRs1xHKazmUgGBTBat1GLU8t6ZWGSYwRkBjDO7F0gZqB1+Y6wPj6rBTCi87pYWJyNCRVBtxwdTFLESxkYQgJ5u5AUamBYvVOeBkbdhY/docst8wht1FlKSCww8jrtus0saTHYzE64iGqUBYiLtVPfW2uUZtis5gMjuJpSAFMyZGpUYCUVIwc5MliJJhLjMTiR242X53lVQbfUTqRYjE/eUFMGhpBCvpTdVCMyMMyJV20b9bCyNtuqPBmYblELtV61aqOnUmtdQuI4TnivQEj7EpKaWVQ+A1yC5cC2PZWQSoekVUTyGGTnuwqDO5CA+I0lu2nNV0ZiejlAYQbGIy0DM+QPC11ZU6iNmpCCYByUp4TUNxbMOSsonaDBXUjjwQhGE6+X69VSmacLiZnY1etgYscQMjBGlZB0MFPTM1BQM4vKcl1IGme/COvDxKwZMzAGzkESI9XMTvx9VpWByXN+Z5Ye1T4nnHZrhA7WWAWREZ7nhTbqbCJelsoLR/mcbcbphGR0IZVrUEJi5YUyl112TTmfD4yeJnaMZAbGmBJSQAcdhp5eMGpmUVnNiZdKSKVHZhFv/Dgw2gOGUSe0UufuRGLaMZeoHVwOUn2+rGZiB1AAY2lGAhGEo/GsSraDxu2wCyUWOWUkORoYnwbDHJV2qAD526j1NLFjtJgk4tXyIqrnOAE1XWBJDUxEVhZRLwLUhVRyZMoyjwXjN0xmiHiB5Dyx/CUkdcdrhUQNjNXGCAClMMxxfBywF+aJaLBvHN5QAGUuOzyhAJBFy9XiiiE0HEB/zwDmlEuLSWOjY/CGAqiIBOPbKAflkSC8oQBiYYAfG1OkM+nrGYA3FMBhLl/ez0unKhqCNxRAaCSa8bUDvYPwhgKY5ozJfm+pTHfF4A0FEAkBw4eGUOVz6vI5DH4sse+j+fePVKr5xHYcHgHGtQ322P6d7q6UvV5vOAxvKB6EBodHTe/+4cfH4Q2F4QsFdDueCGvBvhvB4VFhn4eGR+ENBTCFD5lyHExzReENBTB0aDDn5weGEuv08orWOQXsbx/J+fqhxHm22ZH5PKwpEt+f461wy6MDIyMjqKqqwjCASrMXQxAEQRCEJEYAVAEYHh5GZWX2KziVkAiCIAiCKDiKv4R08CCQI4KzMs+8tx8rnt+O04+qw28uWZj1eate/Aj/31t7ceWSmbjl7KMkvfdD//gUa17bjYsWt+DHy4/J+/xF/7Ee46Eo/n7TKTistkzy38D416e3Yd32btx57lG47MSZsl7L8zzm3b0eoWgM//jhaWiu9qb8bv5P1iMYiWH9v54qGM7pwc1/3Ia/tXfjtrOPxLeXtOr2OQCwbPUG7O3343++uxgLZtRo8p4/eGor1u/owY+Xz8VFiw/T5D0ZV/1/7+HNT/twz1fb8LWF02W//vP3/gPD/jD+7/snY3Z9haZrk8NEKIKF//EyAGDzv39RtjU7UZj8v80H8KPn2nHKEVPxu8sWAQCufGwTNu7ux30XHIvzjp9m+Jo27x3Apf/9LlpqvHjpX0/L+ryX2rtx4x+3YeGMaqz97udlf86T7+zFf/zfRzj7mAas/ub8rM+74ckt+MdHvbqcPyYxMgI0N+d9WvF/O8vK4v8KkN6oHX6XB+U1VTn/huq6KfC7enAwYpf8t044PfC7PODKyyW9hqsoh380iFGHW9H23B/i4Hd5UNtQI/v1HABnVTmGx0IYtrnQLHr98EQIQzYX4ALqmmoACV1VSmloroX/kyF0BKD7MTXIueB38XBVVWr2WfaKcvhdwxizK9uHudgfjO/fqY3y9y8AcOVl8EcDGHd6TP2+TvBB+F1xfZCnqjI+yY8oejzVlfC7PBiEUzj++uGE3+WBtzr3+VcvpjYAfpcH+4IceJ8vq/ZwzOmWdS5Ph/3t/aK/PRNdkfj1aEpdtf7bIyqt0YBKSBZGGOSYR/WtZJwA60KSYmQHJJX4Sr061E6LzjYPib1vTZlLUku4GlqmGGdmp3S6bC70bKPuVWhix7CKF4x4hIONgpeSITnvTTSNWhjmaI6onHUhBcIxwUMrE2rGCADS26iZD4yVupAogLEw/RL77pUEMCEZRnYAZA38SofneWEsvNILXIXgxpv6+ax9V08TOwabSr1/UH8vGGEatYatvF6dAphgJCr4Fcmdg8QQTPZMDmC0dkAmCgPBB0bkNs5aqivc+nYcZsPrSo5GYefPTKg9ZiskGpVKvR4ZCQUwFmYgzxwkBpuH1CdjHlJQhpEdoM6Nd3AijFBiAnK9wgtcch5SagbGCBM7BsvAHBic0NWvJBrjhe2lqQ+MTlOfWeDssttQrbC93OdUl+HTCjKxK00y+sAEzM3AAEB9JXPjzW5mJ3hGKbzZEUYJ5PCBCYSjQhaIMjCEJPokpuxYBqZ/PIRIVNqcG7kZGKGEpMCNlxnN1Za5JH9eOlVZxgkIJnYKAyM5NFd7wXHx7Ijc4ZlyEE+L1sPITutp1Gwf1Fe6Fc+i8orM7MxEGOFAJnYlRbqdPs/zGEsci2Y58QLJG75c2fUJIQOjbJ0seMtVpmI30047J9xMWgEKYCwMO2jypexqylywcQDPJ1+TD7kaGDUlpG7hAqc8yKjMMtBR0NYYkIFxOWxoSvwNeo4UEJd4pO4fKXiZZb/mAYyyKeNi2PGldXAlFwpgShN2EQ+EY4hEY5gIRcGSrGY58QKiDEyOElIyA6PsXCEuIWXLLCfLR8pvUvSAAhiLIp6DlG0SNcNu41BTJm3wF0NxBkbBHTKb5dGoUP8CiEW8mTUwSrU1cmFt2gd0HCnALuJuh7ZCUr1GCWgxysFqIl4qIZUW4jLReCgqlJJsnLnHQnKgY/4SktKWf5ZhivHZb26S1QDr6F8ACmAsy4g/gkhidnk+DQyQOpVaCkkNjLRDIKlUl3+B6R5mQYaaDExCAxPIXEJSc/cvh6QORr8MTECnLICggdEpA1OvIoj0WS2AIf+XksJlt8GRuFkYD0aEckqZ22FqxkHKPCS/ysGvXqddcAvIpoORKmcwGgpgLEpfYnBWhdshSWjLplK/tL0bb+3uRzSWW2QakhvAqBjo2DOq/g49Wxu1EYMcxUyfkuhE0rGV2h+K7xuPxm3hemlgejXYBz6dyltyEUpITjo1lhIcx6UIeZMdSOYGslJKSGrbqMV/ezaJALP0mGqhDiTAhABm1apV+NznPoeKigrU19fjq1/9Knbu3JnyHJ7nsXLlSjQ3N8Pr9WLp0qXYvn270Us1FaEDSULKbl17F97bMwgAePLd/bjokbex5L5XsK69K+trFHchKSghsU4hNRe4qgwamEg0JtwZGBXAGDGVWi8dhl4+MMkAVfndGQuupJQoozEeb+3ux/PbOiUF63KgElLpkmyljoo6kMwNYFhmvSdHCUmL1v+KfAFM4jw71QC7CjkYHsBs2LAB119/Pd5++22sX78ekUgEZ511FsZF0yfvv/9+PPDAA/j1r3+NTZs2obGxEWeeeSZGR0eNXq5pSO25X9fehWvXbpl0UeoeDuDatVuyBjGyfWBUtFGzL19jlQoNjNCFlPz8vrEQYjzgsHGGeRO0CBkY/UtIWk9l1ksD0z2svhNMaglpXXsXltz3Ci565G3c+PQ2ScG6HJLBI5WQSo2kmV1EuJCb2YEEiLqQcmZg4mtVc74QMjBZSkhW9IABTAhg1q1bhyuuuALHHHMMjj/+eDz66KPYt28fNm/eDCCefVm9ejXuuusunH/++Whra8Pjjz+OiYkJPPnkk0Yv1zT6EyUkJs7NRDTG4+4XdiDT/Sd77O4XdmS8Q5XvxKtco8A0MEo9YIDMPjBCd1OF2zDXVJaBOTjk1/TOX4xeZQyvTmZxgkmhik4wKUZ2LFjvGk69G80XrMvBT0Z2JYu4jCIEMBYpIY0GI1m/G/6E6aXSEhIg8oLJcoPaJ9EV3mhML/QODw8DAGpq4gPrOjo60N3djbPOOkt4jtvtxmmnnYaNGzdmfZ9gMIiRkZGUf4XMQCLinZqjhPRux8Ckk7kYHkDXcADvdgxM+l1QZgamzCXNbjqdcDQmBGNqzOYytVH3aNCeLZeGSg+cdg6RGI+uYX2yMLpnYDQsIYkFj+q6kHIb2akJ1uWgtiWVKFzKM2hgzA5gKtwOeBI3Mtk6kbQQ/af74KTTT11Ik+F5HjfffDOWLFmCtrY2AEB3dzcAoKGhIeW5DQ0Nwu8ysWrVKlRVVQn/Wlpa9Fu4AfRLcOHN1VqX73khg5x4+8aC4Pm4AVKNT/nBz0S8Y8EIYomLlNEdSEC8ZX1atb5lJL3s7PXQwLDuiDKXXdXJPl8GRk2wLge1LalE4cJu0tK7kMyE47i8nUishKTmfJE/gGEiXsrACNxwww344IMP8NRTT036XXrrGs/zOdvZ7rjjDgwPDwv/9u/fr/l6jURKACO1JJPpebIzMEIAI+/ix/QR9RUeVWUe1kbN80nHyG4DxwiI0VvIKwwU1LyNmnUhxYQgUC1adYEJPjDhzCdQNcG6HNS2pBKFi9gqgmlBzM7AACIvmCw6GLWjBIDcAQzP80IWnTIwCb7//e/jL3/5C1599VVMnz5deLyxsREAJmVbent7J2VlxLjdblRWVqb8K2QE1XeOmuPi1ho0VXmQLSzgADRVebC4tWbS7xS3UcvsQtLCIwSIZ4pYKpXpYLR6b7lMZ14wOrVSs5q2Xm3UQDKAVYt4jIAafM7cGis1wbocaBZS6VIuEvFapYQE5J+HxIJuTTQwGUS8I4EIwlHpnmRGYngAw/M8brjhBvz5z3/GK6+8gtbW1pTft7a2orGxEevXrxceC4VC2LBhA0466SSjl5uCnu2b6UgZ5Gi3cVixfC4ATApi2M8rls+FPS3zERMNC5SfgZEbwGhX5mFlpGEhgDG+hAToP5U62Qmj7ddTnFXQqoyk1T4QfGCyBDAsWM9GrmBdDgENLgZEYeITZSFGLdKFBOQ2swtHY0JwoVcJid1Mx/U41vpeGL53rr/+ejz55JN4/vnnUVFRIWRaqqqq4PV6wXEcbrrpJtx7772YM2cO5syZg3vvvRc+nw8XX3yx0csVWNfehbtf2JFSh2+q8mDF8rlY1tak+ef1jUkbI7CsrQlrLl0waW2NOdYWEg18lOvEG47yCEVikgMfLY3mKr1O9I4GBSGv0SZ2DObGq5eZXVAnDYzdxsHtsCEYiWkYwGjjw5NvlAAL1q9Zu2XS73IF63KIxnjhLndv/wSiMV7V+xGFRSYRr9kaGCDpBZOphCT+HutVQpJ6LTIDwzMwa9aswfDwMJYuXYqmpibh3x//+EfhObfddhtuuukmXHfddVi0aBE6Ozvx0ksvoaKiwujlAjCmfVNMLMZjcCI5PCsfy9qa8ObtZ+A7J88EAMxvqcabt5+RNbASlw+kdyGJZoXIyMJ0a1RiAMSt1JGU9zY8gKnRd5yAnq28WrdSa9UJJqWNekZtWcbHG6s8WHPpAlU3Esxf5pOeMQDAL1/+RFN/GcL6lImCaHYhN9uJF8g9DymQ+L7YuPg4BKXkKiH1W3SMAGBSCSnTvyuuuEJ4DsdxWLlyJbq6uhAIBLBhwwahS8lojGrfFDMSCAvvJ7XmaLdxOOuYuH6obzyY884xJA5gJB70DrtNyNbI0cH0ajCpmCFupZ4IRTAaYO27Rmtg4iWkntGA4KejJewi7tYjgNF4nECvRsM0WQATisanAWfiuW2dAICz5tbj9CPrAADnz2/OGaxLwegbFMKapPrARFMeMxN2c3AoQwlJrNlSM7MpZwZm3JomdoAFfGCsjlHtm2JYyq7C45CcIQGAOfXlAOKZgVyW7GITOzkHfbmCTiQtsyTieUisdFHmsqMi8bhR1Ja54HXawfNApw5ZGF0zMBq3Umsx5wpITX9PZFhbLMbjL9sOAgDOXzAdJx8+FQAQiMRUl42MvkEhrIm4hDSWKFNbQsQrZGCyl5DUOkeX59A4WnWMAEABTF6Mat8UwwS8uTqQMlFb7kZNmQs8D3x2aDzr8+SOEWD4Eip9OWZ2WupUhHlI/rBp+hcgniHUU8gbSHQhaT0LCRB5wWhQQuJ5PtnKrnI/uOw2IRDJtLZ3EjcSFR4Hlh5Zj8MTwfqu3jFVn2vGDQphTcQZGHaTZqUAZmA8lJI9B7QT/LO/czRjAGPNQY4ABTB5Map9UwyLeJW0rLET+6e92edGyR3kyCgT3FKlBTBal3mYF8xIIGJqAAPoK+RNOvFq//UUNDAaZGBG/BHhWKpTeXfGcVzOVurntsbLR+e2NcHjtAvHeUffeNaSkxTMuEEhrIl4YK1VZiEBwBSfC45EcM+G1zIE40WnunXmmoWU9IChDEzBocZrRSn9KmqOrIz0aU/2O1O5HjAMua3UWpd5UktI6icgq0FPMzsjSkhaaGBY+aja59SkvTLZiZR6fAXCUbyY0KF8df40AEBzlRdepx3hKI+9KoJIM25QCGtSLrqIM50fG/BoJjYbl+xEGs0cwKg1vazwZD+3UxdSAaPUa0UN/SoOGCGAyZFaD6oOYKRd/IQpxRplScQiXjYgUs0AQTUwIe8BHcYJ6DULSfyeSoZypiMEkRpd3LN1Ir36cS9GAxE0VXlwQuJGwWbjMLs+3pWkpoxkxg0KYU3Y8Tc4EQafkDxVuI3V12Uj6cabmglkejGfynOFoIEJRSfpvYQuJIuNEQAogJEE81pJt6zXon0zEwPjyktIcxrirea5TupKNTCCU6XEElKvRgJPRjIDE0mKR026My7YDIyGbdRaOyFnG+jIuo/Om9ecMo5iTn3+Yz0f4huUdPS6QSGsSbrexcbpU8ZVQl0WM7uABmMEgNRuq/Tze/94/sHCZmGNvVMAMK+V31yyAED85PbyzafpY2I3Lt0DJh2WgdnbP561TCDuQpKDT+ZEaq3LPFWiDEyPSXOQGLpqYEL6ZWC8iROyFhoYrXVIvgxmdsMTYbz68SEAwFfnTUt5PtPB7FYp5F3W1oTfXLIA6Q15et2gENYkvWW63O1Q1ZqsJclxAqkBjBaDHIH4tcBpj/+t4jJSOBrD0ES8I8uKGhjzFUoFhN3G4Zy2RlR5nRj2h7GnfxzHNFdp/jkDKkpIdRVuVHocGAlE8NmhccxtnjwTSnkGJnGHLLmEpG2Zh4l4h/1h4S7BPA1MvIQ0OBHGWDCiabdCsrPA4hoYjUc5CCUk0UDHF9u7EIrGcFRjBY5uSj2WZ9flL5dK5biWavB8/K77/guOw7RqHxa31lDmpYRwOWxw2W2CU7nR9gy5YCWkQ2licr9GHYscx6Hc7YifzwIRIHFZYx2xNg6o9lpnezAoAyMTjuNwZKJMk0soqwZB9a0gA8NxHI5g68vSiaS0C4ldYCRnYDQu84hnIWllYa+UCo8T1b74erTOwght1HpoYDQtIWmbYcukz3k20X30lbTsCyDKwBwaUz1d+8MDwwCAoxorccHCFpw4u5aClxJELNq1goCXIcxDGkkX8WqTgQGSGShxK3Wf0BHrTinfWgUKYBQwpyF+4tzZk71VWQ1SBjnmgq0vmzZAaQZGdheSxmUeJuKdCEWFv8HM7hBByKuhFwzP80IGxq1D/Z21W2pTQmIaGH1EvJ1DfsF/5Svzmic9f0atDw4bh4lQFF1p4ka5tHfGA5hjp2mfUSUKB3EZyQoeMIxsZnZaTKJmZDKzEzxgLKh/ASiAUcSRjfEMxyfd2gcwsRgvMrJTdtAcXp87Q6RUAyOUkCTevSddWrW5Q69I82SoLXPJDsK0RA8djHhOlT4iXu00ML06a2CeT4h3T2itQXO1d9LznXYbWqeq70QCgA8SAUzbdApgShlx0GKFMQKMpAYmvYSknV6uIsM8pKQHDAUwRQMr0XySwyxOKUP+MFg2fIrSDEweM7ugUideGSUknueTd+gaZUmcdlvKnYZZ5SOGHp1IYm2KPiJebTQwsRgv3A1qFaB6nckAmed5wbzuX+ZPLh8xtHDk5XmeMjAEgNSgJf2GyUzYObRvLJTS5syCfS0yMJlKSMkMjPUEvAAFMIpgAcz+Ab+sycxSYC3UVV4nnAqni7IS0p7+iUnW04ByH5hkBib/3zw0EU6WeTQU2laJhGRmCXgZLYkS0n4NvWDYHZXTzine/7nQapRA/3gIkRgPjgPqNDq5JUtIEXzUNYpPesbgsttwzrHZu4CSAYzym4mDwwEMjIfgsHE4qtGcifeENRAHMGUq5wtpydRyFzguPruLZeiB5I2IFoL/TCUkwcTOgh4wAAUwiqgpcwkRqRYdEGKSB4zylF1jpQflbgeiMR57+ifPRFKagUnOCsl/8WNDHGvKXLLFwrmoFHUGmNVCzZieyMAc0DADIzhrarjNxGg1SoAJeKeWu+HQKNDyikpIrHx0xlH1KUFrOlpkYJiA94iGCl2yXkThUCYKBKwwRoDhsNuEa4K4jCSeRq2WjCWkMSohFSVHNsZPnJ9oLORl0bWaA4bjuORMpAw6mJDSWUjMyE5C1kmvWUWslRow395drIHheW2mFQs1bR1aqAHxNGrl84MAsUmhdndmLAMzHorg+cTk6a/OnyzeFcNaqdUEMFQ+IhhWFfECmc3s/BoZ2QHJjJNYImBlEzuAAhjFCDoYjYW8agY5ismlg2EiXqUZGCklJL1mFVkqA5MoIY2HohhMmD2pRc8WavH7BlSWkIQ2dg2DSBbAbNzdj+6R5OTpXMyuKwfHxf14+tMG3UmFBLwEo9zCAYzgBSNqpdbStZtlnFICGAuPEQAogFEMC2C0bqXuF1qo1R0wTAeTqcSleJijDCdedoHTyuSMUWkhDYzHaRdOKlI7kaIxHm/t7sfz2zrx1u7+SXNH9JxEDSQzOxNhddotFqBq1UINJDOCzPnznLbGvCUdr8suBJJKsjAk4CXEpPrAWDOAEZeQtMzACMMsM2lgLJqBsdYeKiCO0MnMTqu+e2FOTIb1qdXABMIxRGN8TqOvbh0ucEByHhMQv4jmW4fetNT40DsaxP7BCRzfUp3zuevau3D3CzvQNZw8ATVVebBi+VzBrt6vYU07E0IJKaSuhJQ0EtQmiIxvm+0pj738US/WtXfltfI/vK4c+wf8+LR3DCfMqpX1uSTgJcRYtQsJyDxOQNMMjDtVA8PzvGBkR11IRcYRiQxH90gAwxqVDwD1JnYMpoH5rG8MkWjqxUqtBgbIP9CxV2ObeSB+kWPOrABwx5/bseS+V7CuvUuzz5CL1E6kde1duHbtlpTgBYhP7L527RbhbwhE9JuDBGjXRq3lGAG2bdLLcIPjoZRtkw01Ql4S8BJiyi3ahQRkduP1C23U6teaXkIaD0WFm12rZmAogFFIhceJ5oQGQ0s/mD5B9a0u4p1W7YXXaUc4ymNvWnlDqQbGZbfBYZs88CsT3RprYNhFLr0DKj0AMJrpU/J3IkVjPO5+YQcyyXzZY3e/sAPRGK9pSjgT4i4kNcJjrUTacrZNNli2cfchBQFM5xAAKh8RccRBi5W6kIAsJSQ9MjCJczvTv3iddk0CJD2gAEYFRyRSzjs1FPIKXUgqMzA2W/ZOJKUaGI7jROMEct/BazmrSIuLnF6woY77c4wTeLdjYFLmRQwPoGs4gHc7BpIaGJ3bqKMxHuGomgCGjRFQF6DK2TbZmK0mA9M5AoAEvEQccSDw2aExU84p2UgvIYnHjuihgbG6/gWgAEYVyaGOOgQwGhw0c7KYfCk1sgOSPgm5MjCRaEzIJGkRwGhxkdML1kp9IIeIN93+O9fz9JxEDaSeoJV6wYSjMcFiXO3+lbNtssEC9a7hAEYD0su5YgHvcZSBKXnWtXfhR8+3Cz/f+az5JWox9aI2ap7nEYzEwJKomgQwnlQju36L618ACmBUMUfjTqRojMfAhDYaGAA4PEsnktIMDCAa6JhDA3NoLAieBxw2TnUmCdDmIqcXLYKZnT/rRGSpfjX1FR6hjVovPYaN48A0z//c1afoDrMvsX+ddg41PnX7V862yUaV14m6RHp996HJxo3ZEAt4jyQBb0nDStRil1vA/BK1GHaMhyIxjPgjKTPptCwhjSZEvFb3gAEogFEFy8B8olEn0tBESIio1V4YgKQ2IL2EFFQo4gUgqYSUnIGkzQh2LS5yetFU5YHdxiEUjU2aFMtY3FqDphyeNVzifRa31mha005nXXsXltz3ijBr67r/2SL7DjMa4/HyR70AgCqPM2NZTw5s22Q7SsTbJheHKzC0+/DAEAAS8JY6Vi5Ri/E47ahMZEnE2VqXw6ZJJyYLYIKRWDzLanEPGIACGFUcXh830RoYDwklEzWwiLfa59TEnp2VkHan1XJDCtuoAcCXmGb8+ieHMvqYAPG7FkC7FmqtLnJ64LDbhOAk21BHu43DiuVzM/6O/U0rls+F3cYlRwlo7AMjtQsq33ssue8V/Ptz8TR733hIdYpdvG3S92/6tskF8z2SFcCQ/wsBa5eo02El297RIPyJLLhWNzviFvLxYIQ0MMWO12XHjEQJQQtH3n4N5iCJaanxweWwIRiJpXTJsC4kuSWkde1d2Lx3CADwxNt7cdEjb2e8gLFSjlYt1Fpd5PRCPFIgG3ObqpBpeQ2VHqy5dIHgdRLQIQOjxR2mFgFQNpa1NWHNpQsmOSs3VqVum1woaaUmAS8BWLtEnU5SyBsQvJy0mEQNAE67TbhxGg1EhBtqtR2xemLN3qgCYk5DBfb0T2BnzyhOOnyqqvdiwkitUnZ2G4fZdeX4qGsEn/aMYUZtGQBlGRh2AUu/xLELmPhCwzIwWjrlsotcuhFcY5oRnBm01Hjx1me5vWD+6/XdiPHAKXOm4rqls3H12s0Y8Uew+sJ5+PzspPmaltNlGVLvMG/50zYc01yFSo8TlV4nqrxOVHodKHc7sOIv27MGQBziAdCZcxsVB5HL2ppw5txGvNsxgN7RAOor4hk1qe+XLCFJu5EgAS/BsHKJOtsaekeCmFYdv3HS8man3O1AIBzCWDAiEvFaNwNDAYxKjmyowPodPZroYLQysRMzpz4RwPSO4YtzGwDI18Dku4NPv4AJLdQazypSe5HTCyEDk6WE1DsSwJ/eOwAAuOH0w3HCrFqcNGsq1m3vxvsHhlICGFbXdmt4UpJ65/js1oN4dutB2e8vTrGfKPpb5GK3cYpfzzIw+wYmEAhH82paOof8JOAlACRL1N3DgYznOA7xGyUzStTpMC+YnpGgMJNOy5udcrcDfWMhjAcjooqAdTMwVEJSCfOC0WIqtR41x0xDHeVmYOTWiAWTMx3uWNhF7ivzpuHE2bWmBy9AshMpWwnpv9/sQCgaw6IZU4ST4PzDqgEAW/cNpTzXr8MwR6l3jmfNbcBX5jXj9CPrsOCwahxeX466CrfkbWxmir2uwo0KjwMxHtjTn78TiWVfSMBLWL1ELaZOZGanR7mZtVKPBiMiU1XKwBQtbKTAJ92j4HkeHKf8IB8QSkgaBjAZxI1yfWDk1ogFm3mTp0UbBTOzO5DBzG5oIoS1b+8FAFx/+uHC8TEvMTdp2/6hlOfrcVKSeoe55tKFGU/Sb+3ux0WPvJ33c8xMsXMch8PryrB1/zCefncfzj6mKWt2Lhrjsa69GwDQUOE2fZ4WYT5WLlGLqReJeCd0cO1mTsQj/rBg6UEBTBEza2o5HDYOo8EIukcCaKryKn6vAR1EU4ezoY69Y4jFeHAcEIrKy8DIrRFrPUbA6rBxAl3DfoSjMThFHWSP/nMPJkJRHN1UiaVH1gmPHzu9CnYbh+6RALqG/cJxk9TAaJccZXeY167dAg5ICWKk3GEWQop9XXsXPu6OB+mPbdyLxzbunTQokz1PfJF69ZNDWHLfK5a6SBHmYNUStRhWQjo0GtTFcoENsDww6NfU0kMvqISkEpfDhtapcXGs2pECrISkpQZmRq0PTjuHiVAUB4f9QvYFkJ6BkdPGPBGKCEZIWrjwFgJ15W64HDbEeKBrKHn3NhaM4LGNewAA158+OyU753M5hOnH4jKS0Eat8SgBNZ0+Vk+xM4F5urNweoeUnp1URHFgxRK1GGEe0khAl7lpzAtmT1+8DDtFI0sPvbDuygoIrXQwWo4RYDjtyQDr094xIfsCSM/AyLmAMQGvz2VPmexazNhsHKazqdQiIe+T7+zFsD+MWVPLcE6GACGpgxkUHmMXYY8OowSWtTXhzdvPwFNXfR4PfnMenrrq83jz9jMkZR60aHXWg3wCcx7Aj5/fjj194/j357N3UgHWMCsjiFywEtJ4KCrc8GrVRg0kvWD29sfPY1YeIwBQCUkTjqivwF/RpboTSS/nwzn1FfikZwy7esbQ1pxsGXXJiKyl1ogF/UulR5UeqNBomeLDZ4fGBSFvIBzFI290AACuWTo7453c/JYpWPv2vpQMTEAHEa8YNZ0+Vkyx5xOYA3G9wNKfv5bzOVp1UhGEnpS7HfC57JgIRbFvIJ4l0VKEzkS8exPvbWX9C0ABjCYc2RgXym7ZO4jnt3UqOrEHw1EMTsQH0XX0jeHw+nLNLgxzGsqBD+OdSGL9i9wAg13A3vmsH1c/8R5Gg1H8/ILjcfKcpP8NC2DUTikuNJJTqeMBzP9uPoBDo0E0V3nw1XnTMr6GZWA+7BxGKBKDy2FLTqO2aGeMmgBID6QKzNO1P2rfjyDMor7CjT39E9jTFz/XaJmBqUhkYFgm3comdgCVkDSBiVY/6xvHjU9vy+pQm4117V045f5XhZ+vWSt/Rk0uhJlIvWMIhpW58DLsNg4nHT4VZ85tBAC8vutQyu/FGZhSIunG60ckGsNvN+wGAHzv1FlZS3WtU8tQ5XUiGInh4+64K6yes5CKEakC8x996WhN348gzIIdo/sS2V4tzxVlaWX/qRrqMfWAAhiVrGvvwt1/2THpcanCQCYsTB8EqKWwUGil7hmT3UKdjdMSHTUbdqYHMAkTu1ILYGqSZnYvfHAQBwb9qC1z4cLPHZb1NRzHCVmYbfuHEI7GBA0GBTDSkCowv+zEmZadp0UQcqhLZLfHgszITrtCSrpukTIwRYzaGTNGTUGdWVsGe6LVm0XtSiZRizl1Th04Dvi4exRdw0n/k26hhFRaAUxzog16V88ofv73nQCA7yxpzdshML9lCoB4J5K4i8ajYRt1MSNVYO5y2CzdSUUQUmGdSAw92qgZVtfA0FlSBWqnmBo1BdXlsGFmbTxDsD3hQKpkErWYKWUuHD+9GkB8MjWjtwRLSOvau3D12vcAAKPBKDqHAsIdfT7EnUiBRFskx8kTWJc6UjukrNpJRRBySC9zaukZlV5CsvIYAYBEvKpQO8XUyCmoc+orsPvQOLYfjGst1JaQAGDpkXXYtn8Ir+08JJRKSs3ELtuQSx7AD595Hz6XPeeF8fiEI++e/gl0DsUzWV6nvaQ6uLRAaoeUFTupCEIOkzMw+pWQrDzIEaAARhVqp5gaOQV1TkM51m0H2g9qk4EBgKVH1mP1y5/izU/7EI7G4BAPciyBDEyuEiAj35TmKq8Ts+vKsPvQON7+LJ5pI/2LMqR2SFmtk4og5JDe4amlkd3kEpK1b0QpT62CfAJCILcwcHFrDaq8zqyv1VJYyKb1sgBDiwzMcdOqUFPmwmgwgi17BzE0ERYGRZZCG7VWJcD5h8V1MBt39wGwbgs1QRDmk35Dq4eRHYM0MEVMLgEho6bMhWAkmvF3e/vHJ9mfM7QWFrJWaoYWGRibjcOpCQ+Y1z45hJ5EqaumzKVaJFwIaFUCZDqY9/bEHXk9TvpaEgSRGT1FvOISkstuE3xhrAqdKVWSTRhY43PCaeew/eAILnrkHfSPBRGN8Xhrdz+e39aJDZ/04vr/2YJQJIYjGsoniV61FhbOqiuDOA7SKsBYemQ9AOC1nYfQnchGpH/BihWtSoCsE0nwgNFhjABBEMVBtc+ZIvLXYxo1ENe/WF2LZ+3wqkDIJgx8/8AQvvPYJry/fwjnPPgGeB44NJbq91LuduCJK0/A1HK3rsJCj9OOw2p82JOYcaFVl8upR8TbqT/qGsGHB+L6mvRgrljRakrzEQ3lgj04QBoYgiCyw3Ec6ircKaJ/reABeBw2BCIxuJ12RGO8pQXulIHRiExTTBccNgX/e81JqPG50DsanBS8AHEzoq37Bg2Zgnq4qIzk1qhMUVPmwnGJduo/bT4AAGgoETdTraY0O+w2HDc9OaOKNDAEQeSiTpTl1koDs669C0vuewWBhI6xo29cU0d4PaAARmdap5bBYc9+AeNg3BRc5sgLaCPiZSw9Iu7Ky0zywrFYyUz11cpbhLVTA8BEKFoy248gCPnUicS17x8YUn2+YHYQ6U0JWjrC6wGVkHQmXhaanHlhGDkFd/bUMuH/B8ZDmqUH04OhP2/pxFu7+1OmVBczar1F1rV34ZlN+4WfN+8dxJL7XimZ7UcQhHTWtXfhn7v7hZ+/9YdNaKryKD5f5HOEZzfZuewgzMLSGZjf/OY3aG1thcfjwcKFC/HGG2+YvSTZGGlWl4t17V1Yte5j4eeXP+rVJD24rr0L/5mwzhdj9chda5SWANmdD5tEzii17UcQRH7Y+YLp5RhqzhdGOcLrgWUDmD/+8Y+46aabcNddd2Hr1q045ZRTcM4552Dfvn1mL00WRprVZYMd9P1joZTH1V4kjZrlVKzQ9iMIQip6nS+scpOtBMsGMA888ACuvPJKfPe738XRRx+N1atXo6WlBWvWrDF7abKQOi1Xrym4el4kCzlytwK0/QiCkIpe5wsr3GQrxZIBTCgUwubNm3HWWWelPH7WWWdh48aNGV8TDAYxMjKS8s8KaNWpohQ9L5KFHLlbAdp+BEFIRa/zhdk32WqwZADT19eHaDSKhoaGlMcbGhrQ3d2d8TWrVq1CVVWV8K+lpcWIpUrCzCm4el4kCzlytwK0/QiCkIpe5wuzb7LVYOkupHQXQJ7nszoD3nHHHbj55puFn0dGRiwXxJgxBVfPi6RWRm6lCm0/giCkouf5gt1k3/3CjpSMfaOK7iYjsGQAM3XqVNjt9knZlt7e3klZGYbb7YbbbW0LezOm4Op50LPI/dq1W8ABKe9v9cjdCtD2IwhCKnqfL8y6yVaDJUtILpcLCxcuxPr161MeX79+PU466SSTVlWY6J0eNLM8VgzQ9iMIQip6ny+McITXEo7neUv2aP7xj3/EZZddht/+9rc48cQT8bvf/Q6PPPIItm/fjhkzZuR9/cjICKqqqjA8PIzKykoDVmxt1rV3TUoPqjE/Sica4wsqcrcatP0IgpBKsZ8vpF6/LRvAAHEju/vvvx9dXV1oa2vDL3/5S5x66qmSXksBzGSK/aAnCIIgCp+iCGDUQAEMQRAEQRQeUq/fltTAEARBEARB5IICGIIgCIIgCg4KYAiCIAiCKDgogCEIgiAIouCgAIYgCIIgiIKDAhiCIAiCIAoOCmAIgiAIgig4LDkLSQuYvc3IyIjJKyEIgiAIQirsup3Ppq5oA5jR0VEAsNREaoIgCIIgpDE6Ooqqqqqsvy9aJ95YLIaDBw+ioqICHJe0yx8ZGUFLSwv2799PDr06QtvZWtD+MB/aB9aB9oX55NoHPM9jdHQUzc3NsNmyK12KNgNjs9kwffr0rL+vrKykA9cAaDtbC9of5kP7wDrQvjCfbPsgV+aFQSJegiAIgiAKDgpgCIIgCIIoOEougHG73VixYgXcbrfZSylqaDtbC9of5kP7wDrQvjAfLfZB0Yp4CYIgCIIoXkouA0MQBEEQROFDAQxBEARBEAUHBTAEQRAEQRQcFMAQBEEQBFFwUABDEARBEETBQQEMQRQB1ExIEEAwGDR7CSVPb2+vYZ9VFAFMNBpFKBQyexlFz9jYGAYGBjA4OGj2UggAHR0d2LRpEwCkzPsijGHr1q14+OGHzV4GkeDjjz/GihUrsGXLFrOXUrJ8/PHHOP744/Hggw8a8nkFH8B8/PHHuPrqq3HGGWfg2muvxfr1681eUlGyfft2fP3rX8fJJ5+MCy64AI888ojZSyppent7MWfOHJx33nl4+eWXzV5OyfHBBx9g4cKF2Lt3r9lLIQB8+OGH+PznP49gMIgpU6ak/I6yk8awbds2LFq0CD09PYYFkQUdwGzfvh2nnnoqIpEITj75ZLz77rtYvXq1oSmsUqC9vR2nnHIKjjzySNx5551oaGjAM888g9HRUbOXVtK0trbi9NNPx80330yBu4G8//77OPHEE3Hrrbfi/vvvN3s5JU9/fz+uvPJKfPe738Uvf/lLtLa2Ynh4GJ2dnQAoO2kE77//Pk4++WSsXLkSr776KtauXYuXXnpJ/w/mC5Tu7m7+c5/7HH/zzTcLj3322Wd8eXk5/6c//cnElRUXnZ2d/Ny5c/nbb79deOz111/nzz77bL6jo4Pv7u42cXWlSyAQ4BctWsQ//PDD/CWXXMIfc8wx/IYNG3ie5/ldu3bx0WjU5BUWJ3v37uU5juP/7d/+jed5ng+FQvx9993HX3bZZfy1117L//73vzd5haXHp59+ys+bN48/cOAAHwqF+Msuu4xfsGABP2fOHP68887jh4aGeJ7n+VgsZvJKi5MPPviAt9ls/J133snzPM/39vbyZ5xxBn/NNdfwoVBI13NRwWZg3n//fUyfPh1XXHEFACAcDqO1tRWnnnqqoNHgKXWomgMHDuC8887D9773PeGxl156CVu3bsWSJUuwfPlyXHrppSausPSIRCJwOp2YNm0aFi9ejDvvvBPz58/HjTfeKGQGAoGA2cssSg4cOIDq6mrh7n7ZsmX485//DL/fj48//hj3338/brjhBpNXWVp0dnZibGwM06ZNw2WXXYb+/n7ccsst+NGPfoSdO3fiC1/4AgDKxOhBOBzGr371K6xcuRI//elPAQB1dXU4/fTT8dRTT2FoaAg2m02/a7FuoZHO7Nq1i3/44YcnPX7uuefyK1asMH5BRcr4+Di/Z88e4eef/exnvNfr5R9//HF+w4YN/BNPPMHPmDGD/+1vf2viKkuD9DvIH/zgB/yqVat4nuf57du387NmzeJdLhf/0EMPmbG8kiASifCvv/4639jYyHMcx3/ta1/jOzs7eZ7n+bGxMf4Xv/gFf+SRR/JvvPGGySstftj3YXh4mJ81axZ/ww038Oeccw6/bds24Tn79u3jp0+fTtcEHenr6xP+n2Vb/H4/f8wxx/Df//73KQPDiMViwn9nz56Na665JuVxALDb7SkdSWvWrMHatWuNXWiBw7Ynz/Pw+XyYPn268LvW1lY8//zzuPzyy3Hqqadi+fLl8Pl86OrqMmu5RY/4+Abidz0AUFFRgU8++QQA8POf/xwjIyM444wz8Oijj+LFF180fJ3Fivj7YLfb8fnPfx5PPfUULrzwQtxwww1obm4Gz/MoKyvDN77xDezZswe7du0yedXFS/r3wel04uKLL8brr7+OrVu3oqWlBUA8Uzlt2jQsWLCAOic1Rnwtrq2tRTQaBQDYbPGQwuFw4LTTTsM777yDiYkJAPpURByav6NO7Ny5E//93/+NwcFBtLS04JprrkFDQwOA+EaLxWKw2WyoqalBdXU1AODOO+/EL37xC2zbts28hRcY4u182GGH4eqrrxa2MwB84xvfEP6f53k4HA60traitbVVeIxStdqRa3985StfwR/+8AdcdNFFeO2117BhwwYEAgGsWLECP/3pT3HaaafB5/PR/lBB+vb/3ve+h8bGRixZsgQzZ85EU1OT8Fx2gj7++OMxc+ZMk1Zc3GTbH5dccgk2bdqEDz/8EL/4xS/w05/+FA5H/PLm8/lQUVEBgM5PWpDvGsGuC7fccgva2trwu9/9DjfffLMu270gMjA7duzACSecgP3792PPnj148cUX0dbWhnXr1gknDRb5+f1+cByHe+65B6tXr8bGjRtx9NFHm7n8giF9O//1r3+dtJ3FUTTHcfjZz36Gjz/+GEuXLhUeI7Qh1/4AAJfLhf/6r//Ca6+9hr/+9a+YO3cuFixYgBUrVuCZZ55BWVkZ7Q8V5Nr+DocDM2fOhNvtBhA/7jmOw3/9139hdHQURxxxhMmrLz6y7Y8XX3wRRx11FB588EF89atfxe9+9ztcdtllePTRR3H99dfjpZdewuWXXw6Azk9qybQPjjnmmJRrBMdxiMViOOyww/Dd734Xf/nLX9DT06PPgnQrTmlEJBLhv/nNb/IXXXQRz/Pxumd3dzf/ne98h/f5fPz//u//pjz/wgsv5B0OB+/z+fj33nvPjCUXJHK386ZNm/gbb7yRnzJlCr9161YTVlzc5NofXq+Xf+aZZ3ie5/mXX35Z2P7UZaEd+bZ/+vfhnXfe4a+//nq+uro6RYNBaEOu/eHxeITvw2effcb/9re/5Y877jh+8eLF/Be/+EXaHxoh9RohPg89/vjjfH19Pd/f36/LmixfQuI4DocOHcKSJUuExxoaGvD73/8eHo8HV1xxBWbNmoX58+cjEomgpqYGtbW1+Mc//oFjjjnGxJUXFnK2c09PD1588UV89tln2LBhA4499lgTV16c5Nsf3/72tzF79mx84QtfSLnzIbRBzvehu7sbzz33HHbu3IkNGzbguOOOM3HlxYmU78OsWbOwcOFCXH311bj66qsRCoUQi8Xg8XhMXHnxIPda7HA4cPnll+Occ85BTU2NPmvieev3Gl9yySXYuXMnNm3aBI7jEI1GYbfbEYvF8LWvfQ379u3DG2+8AZ/Ph+3bt8Pr9WLWrFlmL7vgkLOd+/v7YbfbBb0RoT359sfevXvxz3/+E16v1+ylFiVyvg+HDh2C3W7X7URNSPs+vPnmm/D5fGYvtWiR8p148803DTsnWVoDw2KrSy65BLFYDPfccw/C4TDsdjsikQhsNhuuuuoqDAwMYN++fQCAY445hoIXmSjZzrW1tRS86ITU/TE4OCjsD0I7lHwf6urqKHjRCTnfh/3795u82uJEyXfCCCwdwLCU+BlnnIElS5bghRdewEMPPYRAICAozGfMmAEANMxRBbSdrYWc/UHTd7WHvg/Wgr4P5mPVfWDpAAaInyA8Hg9WrVqFhQsX4plnnsEPfvADDA8P4+DBg3jyySfhcrlS2hkJ+dB2tha0P8yFtr+1oP1hPpbcB7pIgzUiEonwPM/ze/bs4f/0pz/xwWCQX7VqFT9v3jzebrfzxx57LN/U1MRv3rzZ5JUWNrSdrQXtD3Oh7W8taH+Yj1X3gSVEvIcOHRKcXJmCn4mD9u7di5NPPhkXXXQR/vM//xPRaBR+vx8vv/wypk6dihkzZgjOi0RuaDtbC9of5kLb31rQ/jCfgtsHhoZLGfjggw/4uXPn8m1tbTzHcSkzK7q6uviGhgb+mmuuIY8LldB2tha0P8yFtr+1oP1hPoW4D0wNYD799FO+oaGBv+uuu/iPPvqIf/TRR3mO4/j9+/fzPM/z3d3d/C9+8Qs+HA6bucyCh7aztaD9YS60/a0F7Q/zKdR9YGoAc9ddd/Ff/vKXhZ9HR0f5c889l9+8eTO/YcMGfmxszMTVFQ+0na0F7Q9zoe1vLWh/mE+h7gNTnXg7Ozths9kQDofhdDrx0EMP4e9//zsOHTqEjz/+GF/4whdwxx13YPHixWYus+Ch7WwtaH+YC21/a0H7w3wKdh+YGT39/ve/5202G3/ppZfyl1xyCe9yufjnnnuOHx4e5rdu3cofccQR/I9//GMzl1gU0Ha2FrQ/zIW2v7Wg/WE+hboPDO9C4tPmtvzhD3/A/v378cEHH6CxsREPP/wwYrEYbDYbvv3tb2Pv3r146aWXBLMcQhq0na0F7Q9zoe1vLWh/mE8x7APDVtLT04OGhgZh1DbbaN/5zneE/9bV1QGAsNGCwSCOOeYY2GyW99uzDLSdrQXtD3Oh7W8taH+YT1HtAyPSPDt27OA5juOXL18uPBaNRlOes2rVKt7r9fIbN27kN2/ezP/4xz/mp06dyu/YscOIJRYFtJ2tBe0Pc6Htby1of5hPse0D3QOYrq4u/uSTT+ZPO+00vrGxkf/qV78q/C4Wiwk95fv27eMvuuginuM4/uijj+aPPfZYfuvWrXovr2ig7WwtaH+YC21/a0H7w3yKcR/oroF5/vnn8fTTT+O6665DJBLBN7/5TZx00kl49tlnASRd/hhvvPEGKisr0dTUhPr6ej2XVlTQdrYWtD/Mhba/taD9YT5FuQ/0jpAGBwf5v/3tb8LPr7zyCl9fX89/5StfER6LRCKWcvcrRGg7WwvaH+ZC299a0P4wn2LcB4a3UcdiMf7VV1+dtOF++9vf8hs3bjR6OUULbWdrQfvDXGj7WwvaH+ZTDPtA8xLSvn378OGHH6Krqwtf+tKXUFVVBZ/PJ6iZgbiy+fXXX8eFF16Ik08+Gc3NzfjNb36DXbt2YdasWVoup2ih7WwtaH+YC21/a0H7w3xKYh9oGQ29//77fENDAz9//ny+urqab2lp4W+55Rb+s88+43l+stp5/fr1PMdxfE1NDf/ee+9puZSihraztaD9YS60/a0F7Q/zKZV9oFkAMzg4yC9cuJC/9dZb+YGBAZ7nef7uu+/mTznlFP68887jP/30U57neaG+Fo1G+auuuoovKyvjt2/frtUyih7aztaC9oe50Pa3FrQ/zKeU9oFmAczevXv5GTNm8H//+99THn/88cf5U089lb/44ov5gwcPCo+/9tpr/HHHHcdv2rRJqyWUBLSdrQXtD3Oh7W8taH+YTyntA81s9ex2O7xeLw4ePAgAiEQiAIDLL78cl1xyCdrb27F+/Xrh+QsXLsTLL7+MRYsWabWEkoC2s7Wg/WEutP2tBe0P8ymlfaCpiPe8887D/v378eqrr6K6uhqRSESYm/D1r38dnZ2d2LhxI3ieF+yLCfnQdrYWtD/Mhba/taD9YT6lsg8UZ2DGx8cxOjqKkZER4bE//OEPGB4exje+8Q2EQqGUoU9nn302eJ5HKBQq6A1mNLSdrQXtD3Oh7W8taH+YTynvA0UBzI4dO3D++efjtNNOw9FHH43/+Z//QSwWw9SpU/Hkk0/i448/xllnnYWdO3ciEAgAAN59911UVFRAw4RP0UPb2VrQ/jAX2v7WgvaH+ZT8PpArmtm+fTtfW1vL/+u//iv/5JNP8jfffDPvdDr5LVu2CM/58MMP+WOPPZafPXs2v2jRIn758uV8RUUFv23bNsVinVKDtrO1oP1hLrT9rQXtD/OhfSDTyG5gYAAXXXQRjjrqKDz44IPC42eccQaOPfZYPPjggyk1tYcffhgHDhyA1+vFhRdeiCOPPFL7CKwIoe1sLWh/mAttf2tB+8N8aB/EceR/SpJwOIyhoSFccMEFACA4+s2aNQv9/f0AAI7jhKFQ119/vfYrLgFoO1sL2h/mQtvfWtD+MB/aB3FkaWAaGhqwdu1anHLKKQDi0ysBYNq0aYI1MRBv4xodHRV+lpHkIUDb2WrQ/jAX2v7WgvaH+dA+iCNbxDtnzhwA8YjP6XQCiG+8np4e4TmrVq3CI488IvSfF7rS2QxoO1sL2h/mQtvfWtD+MB/aBzJLSGJsNptQY+M4Dna7HQDw4x//GPfccw+2bt2a0rpFKIO2s7Wg/WEutP2tBe0P8ynlfaDKiZelo+x2O1paWvDzn/8c999/P9577z0cf/zxmiyQoO1sNWh/mAttf2tB+8N8SnUfqArLWK3N6XTikUceQWVlJd58800sWLBAk8URcWg7WwvaH+ZC299a0P4wn1LdB5rMQjr77LMBABs3bizIeQqFAm1na0H7w1xo+1sL2h/mU2r7QLNZSOPj4ygrK9PirYgc0Ha2FrQ/zIW2v7Wg/WE+pbQPNB3mSBAEQRAEYQSalJAIgiAIgiCMhAIYgiAIgiAKDgpgCIIgCIIoOCiAIQiCIAii4KAAhiAIgiCIgoMCGIIgCIIgCg4KYAiCsAyvvfYaOI7D0NCQ2UshCMLikA8MQRCmsXTpUsybNw+rV68GAIRCIQwMDKChoaHoJucSBKEtxTmikiCIgsTlcqGxsdHsZRAEUQBQCYkgCFO44oorsGHDBjz44IPgOA4cx+Gxxx5LKSE99thjqK6uxv/93//hyCOPhM/nwwUXXIDx8XE8/vjjmDlzJqZMmYLvf//7iEajwnuHQiHcdtttmDZtGsrKynDCCSfgtddeM+cPJQhCFygDQxCEKTz44IP45JNP0NbWhp/85CcAgO3bt0963sTEBB566CE8/fTTGB0dxfnnn4/zzz8f1dXVePHFF/HZZ5/ha1/7GpYsWYILL7wQAPDtb38be/bswdNPP43m5mY8++yzWLZsGT788EPMmTPH0L+TIAh9oACGIAhTqKqqgsvlgs/nE8pGH3/88aTnhcNhrFmzBrNnzwYAXHDBBXjiiSfQ09OD8vJyzJ07F6effjpeffVVXHjhhdi9ezeeeuopHDhwAM3NzQCAW265BevWrcOjjz6Ke++917g/kiAI3aAAhiAIS+Pz+YTgBQAaGhowc+ZMlJeXpzzW29sLANiyZQt4nscRRxyR8j7BYBC1tbXGLJogCN2hAIYgCEvjdDpTfuY4LuNjsVgMABCLxWC327F582bY7faU54mDHoIgChsKYAiCMA2Xy5UivtWC+fPnIxqNore3F6eccoqm700QhHWgLiSCIExj5syZeOedd7Bnzx709fUJWRQ1HHHEEbjkkktw+eWX489//jM6OjqwadMm3HfffXjxxRc1WDVBEFaAAhiCIEzjlltugd1ux9y5c1FXV4d9+/Zp8r6PPvooLr/8cvzwhz/EkUceifPOOw/vvPMOWlpaNHl/giDMh5x4CYIgCIIoOCgDQxAEQRBEwUEBDEEQBEEQBQcFMARBEARBFBwUwBAEQRAEUXBQAEMQBEEQRMFBAQxBEARBEAUHBTAEQRAEQRQcFMAQBEEQBFFwUABDEARBEETBQQEMQRAEQRAFBwUwBEEQBEEUHP8/lk2SbE0Rnt8AAAAASUVORK5CYII=",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_filtered.plot(y=\"perc_snow\",rot=45,kind=\"line\",marker='o')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dcff56ef-8fe1-4b63-a2b4-5c37b69572da",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "Save the **cloud filtered snow percentage**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "ff1025f1-f2ed-4d5f-afbd-c95df37da869",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "df_filtered.to_csv(\"results_openeo_platform/filtered_snow_perc.csv\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/3.1_data_processing/3.1_exercises/_alternatives/31_data_processing_stac.ipynb b/_sources/3.1_data_processing/3.1_exercises/_alternatives/31_data_processing_stac.ipynb
new file mode 100644
index 0000000..09a5a19
--- /dev/null
+++ b/_sources/3.1_data_processing/3.1_exercises/_alternatives/31_data_processing_stac.ipynb
@@ -0,0 +1,2924 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0c6aed2f-1493-4636-a124-03c81b28bc52",
+ "metadata": {},
+ "source": [
+ "# 3.1 Data Processing\n",
+ "In this exercise we will build a complete EO workflow using cloud provided data (STAC Catalogue), processing it locally; from data access to obtaining the result. In this example we will analyse snow cover in the Alps.\n",
+ "\n",
+ "We are going to follow these steps in our analysis:\n",
+ "\n",
+ "- Load satellite collections\n",
+ "- Specify the spatial, temporal extents and the features we are interested in\n",
+ "- Process the satellite data to retrieve snow cover information\n",
+ "- aggregate information in data cubes\n",
+ "- Visualize and analyse the results\n",
+ "\n",
+ "More information on the openEO Python Client: https://open-eo.github.io/openeo-python-client/index.html\n",
+ "\n",
+ "More information on the Client Side Processing and `load_stac` functionalities: https://open-eo.github.io/openeo-python-client/cookbook/localprocessing.html"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f28075a9-ee07-4ca6-93fe-ac7c4cb75f06",
+ "metadata": {},
+ "source": [
+ "## Install missing and update packages:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "057019b0-17d5-4cb0-a1a3-5abd47dee312",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "%%capture\n",
+ "pip install rioxarray geopandas leafmap"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "81ae631f-4b40-4a37-8e9b-05df68850218",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%capture\n",
+ "pip install openeo[localprocessing] --upgrade"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f9d64c64-39db-4869-917c-ed24bef18204",
+ "metadata": {},
+ "source": [
+ "## Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1abf270c-d73e-4109-854c-3cf36a1b36d7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Did not load machine learning processes due to missing dependencies: Install them like this: `pip install openeo-processes-dask[implementations, ml]`\n"
+ ]
+ }
+ ],
+ "source": [
+ "# platform libraries\n",
+ "from openeo.local import LocalConnection\n",
+ "\n",
+ "# utility libraries\n",
+ "from datetime import date\n",
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "import rioxarray\n",
+ "import json\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import geopandas as gpd\n",
+ "import leafmap.foliumap as leafmap"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e95d456-a831-4cca-ab1f-5a2ec8805291",
+ "metadata": {},
+ "source": [
+ "## Region of Interest"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a324185-d64a-4e69-9601-8f63af0805ac",
+ "metadata": {},
+ "source": [
+ "Load the catchment area."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "b1679451-2868-4109-ab17-38532a8033e3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "catchment_outline = gpd.read_file('../31_data/catchment_outline.geojson')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "3cd3057b-39f8-4644-886d-bdbad43c6cb5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "center = (float(catchment_outline.centroid.y), float(catchment_outline.centroid.x))\n",
+ "m = leafmap.Map(center=center, zoom=10)\n",
+ "m.add_vector('../31_data/catchment_outline.geojson', layer_name=\"catchment\")\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "284cd7b6-e646-4950-b747-4e1a734f148b",
+ "metadata": {},
+ "source": [
+ "## Inspect STAC Metadata\n",
+ "We need to set the following configurations to define the content of the data cube we want to access:\n",
+ "- STAC Collection URL\n",
+ "- band names\n",
+ "- time range\n",
+ "- the area of interest specifed via bounding box coordinates\n",
+ "\n",
+ "We use the Sentinel-2 L2A Collection from Microsoft: https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-2-l2a"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ed91226f-a9ea-45d5-9663-98ed56fb067f",
+ "metadata": {},
+ "source": [
+ "## Define a workflow\n",
+ "We will define our workflow now. And chain all the processes together we need for analyzing the snow cover in the catchment."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f786a79c-897d-4858-83c0-af9d4c4efede",
+ "metadata": {},
+ "source": [
+ "### Define the data cube\n",
+ "We define all extents of our data cube. We use the catchment as spatial extent. As a time range we will focus on the snow melting season 2018, in particular from Febraury to June 2018."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "139b411c-b401-4af8-9f7a-d046e24e00e1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "minx 11.020833\n",
+ "miny 46.653599\n",
+ "maxx 11.366667\n",
+ "maxy 46.954167\n",
+ "Name: 0, dtype: float64"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bbox = catchment_outline.bounds.iloc[0]\n",
+ "bbox"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e21d3764-31f5-46a0-ba44-759864ea0b2e",
+ "metadata": {},
+ "source": [
+ "We know that the catchment area is almost fully covered by the Sentinel-2 32TPS tile and therefore we use this information in the properties filter, along with a first filter on the cloud coverage."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "2a136b78-6eba-42df-a71b-f1b86c4a27ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "local_conn = LocalConnection(\"./\")\n",
+ "\n",
+ "url = \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-2-l2a\"\n",
+ "spatial_extent = {\"west\":bbox[0],\"east\":bbox[2],\"south\":bbox[1],\"north\":bbox[3]}\n",
+ "temporal_extent = [\"2018-02-01\", \"2018-06-30\"]\n",
+ "\n",
+ "bands_11_scl = [\"B11\", \"SCL\"]\n",
+ "band_03 = [\"B03\"]\n",
+ "\n",
+ "properties = {\"eo:cloud_cover\": dict(lt=75),\n",
+ " \"s2:mgrs_tile\": dict(eq=\"32TPS\")}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "450db68c-ffdc-4037-9885-045afec86637",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Load the data cube\n",
+ "We have defined the extents we are interested in. Now we use these definitions to load the data cube.\n",
+ "\n",
+ "Since the B03 band have resolution of 10m and B11 and SCL 20m, we load them separately and then align in a second step using openEO."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "7d89755c-ef86-464f-a5e0-187bd44dfd53",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s2_B11_SCL = local_conn.load_stac(\n",
+ " url=url,\n",
+ " spatial_extent=spatial_extent,\n",
+ " temporal_extent=temporal_extent,\n",
+ " bands=bands_11_scl,\n",
+ " properties=properties,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "cc2690a5-8f93-483a-b908-14f2ba871488",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s2_B03 = local_conn.load_stac(\n",
+ " url=url,\n",
+ " spatial_extent=spatial_extent,\n",
+ " temporal_extent=temporal_extent,\n",
+ " bands=band_03,\n",
+ " properties=properties,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "62ee1371-3bf1-4c28-88b9-3f096e9f582d",
+ "metadata": {},
+ "source": [
+ "Uncomment the content of the next three cells if you would like to download the data first and then use the netCDFs to proceed.\n",
+ "\n",
+ "It will download ~3 GB of data, make sure to have enough free space."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c420b652-c1b3-466c-a603-43f171578427",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %%time\n",
+ "# s2_11_scl_xr = s2_B11_SCL.execute()\n",
+ "# # Remove problematic attributes and coordinates, which prevent to write a valid netCDF file\n",
+ "# for at in s2_11_scl_xr.attrs:\n",
+ "# # allowed types: str, Number, ndarray, number, list, tuple\n",
+ "# if not isinstance(s2_11_scl_xr.attrs[at], (int, float, str, np.ndarray, list, tuple)):\n",
+ "# s2_11_scl_xr.attrs[at] = str(s2_11_scl_xr.attrs[at])\n",
+ "\n",
+ "# for c in s2_11_scl_xr.coords:\n",
+ "# if s2_11_scl_xr[c].dtype==\"object\":\n",
+ "# s2_11_scl_xr = s2_11_scl_xr.drop_vars(c)\n",
+ "\n",
+ "# s2_11_scl_xr.to_dataset(dim=\"band\").to_netcdf(\"s2_11_scl_xr.nc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "f89a9497-3c01-40b6-82e8-bfb5cbf29b9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %%time\n",
+ "# s2_03_xr = s2_B03.execute()\n",
+ "# # Remove problematic attributes and coordinates, which prevent to write a valid netCDF file\n",
+ "# for at in s2_03_xr.attrs:\n",
+ "# # allowed types: str, Number, ndarray, number, list, tuple\n",
+ "# if not isinstance(s2_03_xr.attrs[at], (int, float, str, np.ndarray, list, tuple)):\n",
+ "# s2_03_xr.attrs[at] = str(s2_03_xr.attrs[at])\n",
+ "\n",
+ "# for c in s2_03_xr.coords:\n",
+ "# if s2_03_xr[c].dtype==\"object\":\n",
+ "# s2_03_xr = s2_03_xr.drop_vars(c)\n",
+ "\n",
+ "# s2_03_xr.to_dataset(dim=\"band\").to_netcdf(\"s2_03_xr.nc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "c317075d-d428-4cd6-83ec-e428ef515dde",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# s2_B03 = local_conn.load_collection(\"s2_03_xr.nc\")\n",
+ "# s2_B11_SCL = local_conn.load_collection(\"s2_11_scl_xr.nc\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "1bf5a64c-685a-47a5-9792-ead9d995efeb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s2_20m = s2_B03.resample_cube_spatial(target=s2_B11_SCL,method=\"average\").merge_cubes(s2_B11_SCL)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f93a67e6-6746-4de7-9142-635eee9a1196",
+ "metadata": {},
+ "source": [
+ "### NDSI - Normalized Difference Snow Index\n",
+ "The Normalized Difference Snow Index (NDSI) is computed as:\n",
+ "\n",
+ "$$ NDSI = \\frac {GREEN - SWIR} {GREEN +SWIR} $$\n",
+ "\n",
+ "We have created a Sentinel-2 data cube with bands B03 (green), B11 (SWIR) and the scene classification mask (SCL). We will use the green and SWIR band to calculate a the NDSI. This process is reducing the band dimension of the data cube to generate new information, the NDSI."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "26cd85ae-fddf-42c5-975a-1e911420e063",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "green = s2_20m.band(\"B03\")\n",
+ "swir = s2_20m.band(\"B11\")\n",
+ "ndsi = (green - swir) / (green + swir)\n",
+ "ndsi"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "746ef0f4-9c9f-4884-8806-730d91261acb",
+ "metadata": {},
+ "source": [
+ "### Creating the Snow Map\n",
+ "So far we have a timeseries of NDSI values. We are intereseted in the presence of snow though. Ideally in a binary classification: snow and no snow.\n",
+ "To achieve this we are setting a threshold of 0.42 on the NDSI. This gives us a binary snow map."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "591d44b1-20d1-447a-a956-291495f7a1c1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ndsi_mask = ( ndsi > 0.42 )\n",
+ "snowmap = ndsi_mask.add_dimension(name=\"band\",label=\"snow_map\",type=\"bands\")\n",
+ "snowmap"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1b4ae963-05f1-48d0-937f-f162783f3fe3",
+ "metadata": {},
+ "source": [
+ "### Creating a cloud mask\n",
+ "We are going to use \"SCL\" band for creating a cloud mask and then applying it to the NDSI. \n",
+ "`8 = cloud medium probability`, `9 = cloud high probability`, `3 = cloud shadow`\n",
+ "\n",
+ "Here is more information on the Scene Classification https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm-overview\n",
+ "\n",
+ "| Value | Label |\n",
+ "|--------------|-----------|\n",
+ "| 0\t | NO_DATA |\n",
+ "| 1\t | SATURATED_OR_DEFECTIVE |\n",
+ "| 2\t | CAST_SHADOWS |\n",
+ "| 3\t | CLOUD_SHADOWS |\n",
+ "| 4\t | VEGETATION |\n",
+ "| 5\t | NOT_VEGETATED |\n",
+ "| 6\t | WATER \t \t |\n",
+ "| 7\t | UNCLASSIFIED \t |\n",
+ "| 8\t | CLOUD_MEDIUM_PROBABILITY |\n",
+ "| 9\t | CLOUD_HIGH_PROBABILITY |\n",
+ "| 10\t| THIN_CIRRUS |\n",
+ "| 11\t| SNOW or ICE |"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "99556d13-fae5-4726-a656-979a1945e7c1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "scl_band = s2_20m.band(\"SCL\")\n",
+ "cloud_mask = ( (scl_band == 8) | (scl_band == 9) | (scl_band == 3) ).add_dimension(name=\"band\",label=\"cloud_mask\",type=\"bands\")\n",
+ "cloud_mask"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e7d0d18-2c39-4b4f-b493-d0370d7f1c92",
+ "metadata": {},
+ "source": [
+ "### Applying the cloud mask to the snowmap\n",
+ "We will mask out all pixels that are covered by clouds. This will result in: 0 = no_snow, 1 = snow, 2 = cloud"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "8e188e75-57d6-4140-920d-937bea779934",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "snowmap_cloudfree = snowmap.mask(cloud_mask,replacement=2) # replacement is null by default\n",
+ "snowmap_cloudfree"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9b7feb93-d4ce-44fa-bbc5-4b062b6a1400",
+ "metadata": {},
+ "source": [
+ "### Visualize one time step of the timeseries\n",
+ "Let's create the lazy xarray view of the result and look how our first results look like"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "efb5cb0e-5d48-4f74-8e6a-ffcfac5cafe0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowmap_cloudfree_1d = snowmap_cloudfree.filter_temporal('2018-02-10', '2018-02-12').mask_polygon(catchment_outline[\"geometry\"][0])\n",
+ "snowmap_cloudfree_1d_xr = snowmap_cloudfree_1d.execute()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "a1b7ced8-8bde-4496-8f03-fa17ed718da8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "snowmap_cloudfree_1d_xr[0,0].plot.imshow()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6d65f20-1266-44ad-b9e8-e956364fe4f3",
+ "metadata": {},
+ "source": [
+ "## Calculate Catchment Statistics\n",
+ "\n",
+ "We are looking at a region over time. We need to make sure that the information content meets our expected quality. Therefore, we calculate the **cloud percentage** for the catchment for each timestep. We use this information to filter the timeseries. All timesteps that have a cloud coverage of over 25% will be discarded.\n",
+ "\n",
+ "Ultimately we are interested in the **snow covered area (SCA)** within the catchment. We count all snow covered pixels within the catchment for each time step. Multiplied by the pixel size that would be the snow covered area. Divided the pixel count by the total number of pixels in the catchment is the percentage of pixels covered with snow. We will use this number.\n",
+ "\n",
+ "Get number of pixels in catchment: total, clouds, snow."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "1f74e1d2-a48c-4f18-b1ec-10bf85eab5ab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snow_cloud_map_0 = (snowmap_cloudfree == 1).merge_cubes(cloud_mask)\n",
+ "snow_cloud_map = (ndsi_mask > -1).add_dimension(name=\"band\",label=\"valid_px\",type=\"bands\").merge_cubes(snow_cloud_map_0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "730d84f0-fae1-4693-95a0-0700685c13ca",
+ "metadata": {},
+ "source": [
+ "Aggregate to catchment using the `aggregate_spatial` process."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "e72f1c76-5433-4d7a-9d7d-d3243b0bc94a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "snow_cloud_map_timeseries = snow_cloud_map.aggregate_spatial(geometries=catchment_outline[\"geometry\"][0],reducer=\"sum\")\n",
+ "snow_cloud_map_timeseries"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64d9e1fe-c12d-45ce-a53a-d21847b361eb",
+ "metadata": {},
+ "source": [
+ "Get the result as a Dask based xArray object"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "0583d4bc-c381-4663-af61-d27ecf74b0b0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Did not load machine learning processes due to missing dependencies: Install them like this: `pip install openeo-processes-dask[implementations, ml]`\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "catchment_stations_gpd_plata.plot(x=\"id\", y=\"HS_after_gapfill\",rot=45,kind=\"line\",marker='o')\n",
+ "plt.axhline(y = 0.4, color = \"r\", linestyle = \"-\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16752292-e134-4f28-b325-ead839532951",
+ "metadata": {},
+ "source": [
+ "## Validate the SCA results with the snow station measurements \n",
+ "Now that we have combined the SCA results with the snow station measurements we can start the actual validation. A **confusion matrix** compares the classes of the station data to the classes of the SCA result. The numbers can be used to calculate the accuracy (correctly classified cases / all cases).\n",
+ "\n",
+ "| | no_snow | snow |\n",
+ "|-------------|---------|---------|\n",
+ "| **no_snow** | correct | error |\n",
+ "| **snow** | error | correct |"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "48fefa34-ae4d-4e43-985c-098ced246af3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import seaborn as sns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "a4a6438d-bf24-4b93-b684-901f6faf0e30",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10, 6))\n",
+ "\n",
+ "fig.suptitle(\"Error matrices for snow stations within our selected Catchment\")\n",
+ "sns.heatmap(validation_metrics(smartino_snow)[1], annot=True, xticklabels=[\"No Snow\", \"Snow\"], yticklabels=[\"No Snow\", \"Snow\"], ax=ax1)\n",
+ "ax1.set_title(\"San Martino in Passiria Osservatore\")\n",
+ "ax1.set(xlabel=\"Predicted label\", ylabel=\"True label\")\n",
+ "\n",
+ "\n",
+ "sns.heatmap(validation_metrics(rifiano_snow)[1], annot=True, xticklabels=[\"No Snow\", \"Snow\"], yticklabels=[\"No Snow\", \"Snow\"], ax=ax2)\n",
+ "ax2.set_title(\"Rifiano Beobachter\")\n",
+ "ax2.set(xlabel=\"Predicted label\", ylabel=\"True label\")\n",
+ "\n",
+ "\n",
+ "sns.heatmap(validation_metrics(plata_snow)[1], annot=True, xticklabels=[\"No Snow\", \"Snow\"], yticklabels=[\"No Snow\", \"Snow\"], ax=ax3)\n",
+ "ax3.set_title(\"Plata Osservatore\")\n",
+ "ax3.set(xlabel=\"Predicted label\", ylabel=\"True label\")\n",
+ "\n",
+ "\n",
+ "sns.heatmap(validation_metrics(scena_snow)[1], annot=True, xticklabels=[\"No Snow\", \"Snow\"], yticklabels=[\"No Snow\", \"Snow\"], ax=ax4)\n",
+ "ax4.set_title(\"Scena Osservatore\")\n",
+ "ax4.set(xlabel=\"Predicted label\", ylabel=\"True label\")\n",
+ "\n",
+ "fig.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "12ad0abc-1d43-4574-b6d8-916e45fbde61",
+ "metadata": {},
+ "source": [
+ "The **accuracy** of the snow estimate from the satellite image computation for each station is shown below: \n",
+ "\n",
+ "\n",
+ "| **On-site snow station** | **Accuracy**|\n",
+ "|--------------------------------------|-------------|\n",
+ "| San Martino in Passiria Osservatore | **100.00%** |\n",
+ "| Rifiano Beobachter | **100.00%** |\n",
+ "| Plata Osservatore | 82.61% |\n",
+ "| San Leonardo in Passiria Osservatore | NaN |\n",
+ "| Scena Osservatore | 96.15% |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "372191d3-ae11-4df6-abcd-f0bcb2fa3273",
+ "metadata": {},
+ "source": [
+ "The fifth and last station **San Leonardo in Passiria Osservatore** recorded **_NaNs_** for snow depths for our selected dates, which could potentially be as a results of malfunctioning on-site equipments. Hence, we are not able to verify for it. But overall, the validation shows a 100% accuracy for stations **San Martino in Passiria Osservatore** and **Rifiano Beobachter**, while station **Plata Osservatore** has a lot more False Positive (4) than the other stations.This shows a good match between estimated snow values from satellite datasets and on-the ground measurements of the presence of snow. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1f454452-c2e7-4c0c-a8ab-260c7b0e48f0",
+ "metadata": {},
+ "source": [
+ "## Compare to discharge data\n",
+ "In addition to computing metrics for validating the data, we also check the plausibility of our results. We compare our results with another measure with a known relationship. In this case, we compare the **snow cover area** time series with the **discharge** time-series at the main outlet of the catchment. We suspect that after snow melting starts, with a temporal lag, the runoff will increase. Let's see if this holds true."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2b2ddcd-7493-477a-8fcd-81ea3bc308a6",
+ "metadata": {},
+ "source": [
+ "Load the discharge data at Meran, the main outlet of the catchment. We have prepared this data set for you, it's extracted from Eurac's [Environmental Data Platform Alpine Drought Observatory Discharge Hydrological Datasets](https://edp-portal.eurac.edu/discovery/9e195271-02ae-40be-b3a7-525f57f53c80)). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "bc096eb8-b4d3-4056-9a1f-8c3921009f13",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
discharge_m3_s
\n",
+ "
\n",
+ "
\n",
+ "
Time
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
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+ "
1994-01-01 01:00:00
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+ "
4.03
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+ "
\n",
+ "
\n",
+ "
1994-01-02 01:00:00
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+ "
3.84
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+ "
\n",
+ "
\n",
+ "
1994-01-03 01:00:00
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+ "
3.74
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+ "
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+ "
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+ "
1994-01-04 01:00:00
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+ "
3.89
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+ "
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+ "
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+ "
1994-01-05 01:00:00
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+ "
3.80
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+ "
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+ " \n",
+ "
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+ "
"
+ ],
+ "text/plain": [
+ " discharge_m3_s\n",
+ "Time \n",
+ "1994-01-01 01:00:00 4.03\n",
+ "1994-01-02 01:00:00 3.84\n",
+ "1994-01-03 01:00:00 3.74\n",
+ "1994-01-04 01:00:00 3.89\n",
+ "1994-01-05 01:00:00 3.80"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "discharge_ds = pd.read_csv('32_data/ADO_DSC_ITH1_0025.csv', \n",
+ " sep=',', index_col='Time', parse_dates=True)\n",
+ "discharge_ds.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6412118f-b9a3-4e00-811e-99ff77b85515",
+ "metadata": {},
+ "source": [
+ "Load the SCA time series we have generated in a previous exercise. It's the time series of the aggregated snow cover area percentage for the whole catchment."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "f10dfcdf-28f5-4d7a-854c-d674f4a34f34",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "snow_perc_df = pd.read_csv(\"32_data/filtered_snow_perc.csv\", \n",
+ " sep=',', index_col='time', parse_dates=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b40c88f7-f520-4889-a79e-731b5540e9ed",
+ "metadata": {},
+ "source": [
+ "Let's plot the relationship between the snow covered area and the discharge in the catchment."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "c9e61b7a-5db1-40ef-9fd5-db3896ac0faa",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "start_date = date(2018, 2, 1)\n",
+ "end_date = date(2018, 6, 30)\n",
+ "# filter discharge data to start and end dates\n",
+ "discharge_ds = discharge_ds.loc[start_date:end_date]\n",
+ "\n",
+ "ax1 = discharge_ds.discharge_m3_s.plot(label='Discharge', xlabel='', ylabel='Discharge (m$^3$/s)')\n",
+ "ax2 = snow_perc_df[\"perc_snow\"].plot(marker='o', secondary_y=True, label='SCA', xlabel='', ylabel='Snow cover area (%)')\n",
+ "ax1.legend(loc='center left', bbox_to_anchor=(0, 0.6))\n",
+ "ax2.legend(loc='center left', bbox_to_anchor=(0, 0.5))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5c6b8f01-5cff-4300-8c59-19f321458d38",
+ "metadata": {},
+ "source": [
+ "The relationship looks as expected! Once the snow cover decreases the runoff increases!"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "cubes-and-clouds-cubes-and-clouds2",
+ "language": "python",
+ "name": "conda-env-cubes-and-clouds-cubes-and-clouds2-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/3.2_validation/3.2_validation.md b/_sources/3.2_validation/3.2_validation.md
new file mode 100644
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+++ b/_sources/3.2_validation/3.2_validation.md
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+# Validation
+
+## Learning objectives
+- Get to know typical validation strategies in EO
+- Understand where uncertainties appear in a workflow by design
+- Understand why validation is important
+- The validation of large scale mapping products
+- Validate some pixels of the snow cover area map
+
+## What is validation
+The validation process typically involves comparing a model or a developed Earth Observation (EO) product with reference data, and the level of agreement is assessed using validation metrics. Validating EO products is crucial to prevent misinterpretation or error propagation when utilizing the data for purposes such as area quantification, subsequent modeling, or planning, particularly in contexts like nature conservation or risk assessment. However, validating EO products poses significant challenges.
+
+In this tutorial, we will explain how to derive validation metrics and how to interpret them. Our primary focus will center on the difficulties and limitations inherent in the accuracy assessment process, especially in the context of large-scale (global) mapping products based on Earth Observation.
+
+## Critically Analyse a workflow
+- Identify sources of uncertainty in the applied workflow
+- Process graph with pop-ups of sources of uncertainties
+- Strategies of how to improve
+
+Now that we have carried out a very basic approach to solve our research question we should take some time to identify possible sources of uncertainty and think about how to improve them:
+
+- Optical earth observation has some inherent drawbacks, most importantly: clouds. Especially in mountain regions.
+ - We are excluding images where a certain cloud coverage is exceeded. There would still be some information available.
+ - We are not filling in the gaps that clouds generate. This leaves us with some uncertainty.
+- Use data fusion techniques and include SAR data, that can penetrate the clouds.
+ - Sentinel-2 has a 6 day repeat rate. This means we do not know what happens with the snow cover in between two acquisitions.
+ - Use data fusion techniques and include other optical sensors and SAR data
+- Use physical snow models or heuristics to estimate the snow cover in between
+ - We are using a threshold for discriminating between snow and no snow. Changing this arbitrary value will influence our results.
+ - There are better, more complex ways to identify snow.
+- Snow Cover does not represent the amount of snow.
+ - Therefore we would need to calculate the snow depth.
+ - Or better the Snow Water Equivalent.
+
+
+## Typical validation approaches
+### Reference data
+Reference data for EO are commonly obtained through field surveys or visual expert assessments of the underlying EO data. These reference datasets play a dual role, serving not only for validation purposes but also as essential components for training models, particularly when the EO product relies on predictions from a data-driven model. Consequently, when referring to reference data, a broad distinction is made between training and test data. The training dataset is employed in the model development phase, while the test dataset is crucial for evaluating the quality of the resulting product, specifically assessing the accuracy of predictions generated by the model.
+
+### Model validation and map validation
+Many EO products are generated using data-driven models, which can range from simple rule-based models to more complex machine learning models. In the process of creating such EO products, two distinct validation steps are crucial: model validation and map validation.
+During model validation, we evaluate the model's ability to predict the target variable (e.g., snow cover) based on EO data (e.g., optical satellite imagery). This evaluation often involves cross-validation, where the training data are divided into multiple folds. Iteratively, one fold is withheld during model training, and these reserved data are used to test the model's performance in predicting unseen data. Cross-validation typically includes tuning the model (adjusting hyperparameters, selecting variables) to identify the optimal model for predicting the held-back data.
+If (and only if) the training data and the derived cross-validation folds are representative for the prediction area (see discussion later), the cross-validation performance may be used as an indicator for the map accuracy.
+
+To properly measure the map accuracy, a probability sample of the *prediction area* is required. This might be a random sample of the entire area that is used to describe the fit between the prediction (i.e. the map) and the reference.
+However, in numerous scientific publications, this essential step is often omitted, and model performance alone is presented as the sole indicator for map accuracy. The following section outlines the risks associated with this practice.
+
+### Validation metrics
+Validation metrics summarize the fit between predictions and reference. For continuous variables (e.g. snow depth), Root Mean Square Error or Coefficient of Determination are commonly used validation metrics. For categorical variables (e.g. land cover), Accuracy or F1 score are frequently used summary statistics. For binary classifications, the area under the ROC curve may be used. However, there are many more validation metrics expressing the fit between prediction and reference by focusing on different aspects.
+
+## Validation strategies in the absence of a probability sample
+When reference data are randomly distributed across the prediction area, validation metrics can be computed by comparing predictions and reference through a randomly selected subset of the entire reference dataset used as test data. Alternatively, in cross-validation, the training data may be randomly partitioned into multiple folds. However, the availability of design-based samples is infrequent, particularly in large-scale mapping endeavors like global applications.
+Typically, reference data are sourced from extensive databases, such as soil profiles or vegetation surveys, resulting in high clustering within areas that have been extensively studied or are easily accessible. Conversely, certain areas may entirely lack reference data, as illustrated in the accompanying figure.
+
+
+> Figure 1: Comparison between 1000 randomly sampled reference data (left) and a highly clustered sample of the same size (right) that is typical for many environmental data sets. Reference: https://doi.org/10.1038/s41467-022-29838-9
+
+When such data are randomly split into training and test sets or cross-validation folds, a significant issue arises: the lack of independence between training and test data. This stems from the fact that both sets originate from the same geographic areas, whereas the trained model is deployed to make predictions for much larger areas without available reference data. Ploton et al., 2020, illustrate the consequences: overly optimistic validation statistics that deviate from the actual quality of the map.
+
+To address this challenge, various spatial data splitting methods have been proposed. These methods involve splitting reference data based on spatial units, spatial blocks, or by considering spatial autocorrelation, all with the aim of ensuring independence between training and test data (e.g. Brenning 2012, Roberts et al., 2017, Valavi et al., 2019) or representativeness for the prediction task (Mila 2022, Linnenbrink 2023).
+
+## Limits to accuracy assessment
+Employing validation strategies tailored for spatial data enables us to offer the most accurate estimates of map accuracy, albeit with certain limitations. Reflecting on the reference data illustrated in Figure 1, large areas lack coverage from any reference data. While it is technically feasible to generate predictions for these areas, the question arises: is this a reasonable approach?
+
+Assuming that new geographical spaces often goes along with new environmental conditions, it becomes likely that our models may not be applicable to these environments due to non-applicable relationships. For instance, consider reference data for vegetation traits sampled in low elevations; it raises questions about the model's applicability to high elevations where the traits might be influenced by different factors. This challenge is particularly pronounced when employing machine learning models, as their extrapolation abilities are often limited. When making predictions, the model is compelled to extend its predictions into unknown areas, making predictions for regions beyond the trained data range highly uncertain.
+This, however, is not reflected by the validation statistics that were calculated based on the reference data, hence knowledge on the performance in the data-poor regions is not included. As a result, the statistics fail to reflect the accuracy of the model in these areas, and predictions for regions outside the original data range should be approached with caution due to their inherent uncertainty.
+
+It is therefore important to limit predictions to the area where the model was trained and validated for. Meyer and Pebesma 2021 provide one suggestion to derive the "area of applicability" of prediction models that is based on distances to reference data in the predictor space. Other suggestion limit predictions to the geographic proximity of reference data (Sabatini et al., 2022).
+
+## Communication of validation
+As outlined above, the step of accuracy assessment involves considerable considerations on the data used for evaluation and requires awareness on the area these statistics are considered valid for. This challenge becomes particularly crucial when reference data fail to represent a comprehensive sample of the entire prediction area, a common scenario in many geoscience applications - to avoid overly optimistic performance estimates and hence false conclusions on the map accuracy. The validation procedure hence needs to be carefully communicated alongside the predictions. The resulting EO product (i.e. the prediction map) should be limited to the area for which the model was enabled to learn about relationships and for which performance estimates can be reliably provided for. This can be done by either masking the map or by providing an additional quality layer.
+
+[](https://www.youtube.com/watch?v=ZvNsXDbz_W4)
+> Video content in cooperation with [Hannah Meyer](https://www.uni-muenster.de/RemoteSensing/team/meyer/index.html) (University of Münster).
+> *"Validation isn't optional. It's a must."*
+
+## Exercise
+Let's apply some validation steps on a cloud platform in practice!
+
+[Exercise 3.2 Validation](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/3.2_validation/exercises/32_validation.ipynb)
+
+## Quiz
+
+### Theory
+
+What are common problems in creating and validating global maps?
+
+ [[x]] The spatial distribution of reference data: There are usually areas in the world where reference data is clustered and areas where there is hardly any data available.
+ [[ ]] None: We have cloud computing that can scale to produce global maps and machine learning models can automatically account for data sparse regions.
+ [[x]] The availablility of reference data: Some biophysical indicators are not measured frequently in space and time in the field (e.g. leaf area index, snow water equivalent)
+
+What is the Area of Applicability?
+
+ [( )] It's the topic the map covers (e.g. vegetation cover)
+ [( )] It's the extent of the map.
+ [(x)] It's the area of the map where the values are representable.
+
+
+### Exercises
+
+How many snow stations are in the catchment? _Answer in the exercise: 33_validation.ipynb section **'Load snow-station in-situ data'**_
+
+ [( )] 3
+ [( )] 7
+ [(x)] 5
+
+Which openEO process is used to extract the time series of the snow covered area at the station locations? _Answer in the exercise: 33_validation.ipynb_
+
+ [( )] reduce_spatial
+ [( )] resample_cube
+ [(x)] aggregate_spatial
+
+Which is the station where the mapped snow cover has the lowest accuracy? _Answer in the exercise: 33_validation.ipynb section **'validate the SCA results with the snow station measurements'**_
+
+ [( )] Rifiano Beobachter
+ [( )] Saint Leonardo in Passiria Osservatore
+ [(x)] Plata Osservatore
+
+When is the date with the maximum runoff/discharge? _Answer in the exercise: 33_validation.ipynb section **'compare to discharge data'**_
+
+ [( )] 2018-06-03
+ [( )] 2018-04-17
+ [(x)] 2018-05-03
+
+How is the relation between snow cover and runoff/discharge? _Answer in the exercise: 33_validation.ipynb section **'compare to discharge data'**_
+
+ [( )] When the snow cover is high, also the runoff is high.
+ [(x)] Snow melt is followed by increased runoff.
+ [( )] Snow melt is followed by reduced runoff.
+
+
+## Further Reading
+- Video: [Meyer, Hanna: Machine learning-based maps of the environment: challenges of extrapolation and overfitting. OpenGeoHub Summer School 2022 - KISTE project workshop, OpenGeoHub Foundation, 2022. https://doi.org/10.5446/59412](https://doi.org/10.5446/59412)
+
+## References
+- Meyer, H., Pebesma, E. Machine learning-based global maps of ecological variables and the challenge of assessing them. Nat Commun 13, 2208 (2022). https://doi.org/10.1038/s41467-022-29838-9
+- Meyer, H., & Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12, 1620–1633. https://doi.org/10.1111/2041-210X.13650
+- Loew et al. (2017): https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2017RG000562
+- Brenning et al. (2012): https://doi.org/10.1109/IGARSS.2012.6352393
+- Valavi et al. (2019): https://doi.org/10.1111/2041-210X.13107
+- Roberts et al. (2017): https://doi.org/10.1111/ecog.02881
+- Ploton et al. (2020): https://doi.org/10.1038/s41467-020-18321-y
+- Milà et al. (2022): https://doi.org/https://doi.org/10.1111/2041-210X.13851
+- Linnenbrink et al. (2023): https://doi.org/10.5194/egusphere-2023-1308
+- Sabatini et al. (2022): https://doi.org/10.1038/s41467-022-32063-z
diff --git a/_sources/3.3_data_sharing/3.3_data_sharing.md b/_sources/3.3_data_sharing/3.3_data_sharing.md
new file mode 100644
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+# 3.3 Data Sharing
+
+## Learning Objectives
+- Carry out an EO workflow on a cloud platform independently
+- Make our results `open` and `FAIR`
+- Collaborate with a community of researchers to reach a common goal
+
+## Introduction
+We have reached the last chapter of the course. You know about data cubes, cloud platforms and open science. Now it's time to prove it! We will apply everything we have learned so far and complete our own EO workflow on a cloud platform adhering to the open science principles.
+We have carried out a full EO workflow to produce snow cover information in an alpine catchment. To make our results impactful we need to make them openly available to other researchers and the general public. Therefore we are going to learn how to share our data set (and code) properly - following the FAIR principles. We have learned about the concepts of open science in lecture [1.3 open science](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/1.3_openscience/1.3.1_openscienceandfair.md). Now we are going to apply them! We are going to create a snow cover area map of the alps together with all the participants of the course. Everyone adds their contribution to a shared map. With every participant another small patch of the alps gets mapped! The map is openly available so that everybody can track our progress, the data is openly available and you can point to the patch you have provided!
+
+[](https://www.youtube.com/watch?v=-8rHmtEMCx8)
+> Video content in cooperation with [Leandro Parente](https://opengeohub.org/people/leandro-parente/) (OpenGeoHub).
+> *"Connect - Create - Share - Repeat"*
+> Links to OpenGeoHub's open science projects mentioned in the video:
+> - [opengeohub](https://opengeohub.org/)
+> - open environmental data cube: [webgis](http://ecodatacube.eu), [stac catalogue](http://stac.openlandmap.org), [code and documentation](http://eumap.readthedocs.org), [publication](https://doi.org/10.7717/peerj.15478),
+> - [open earth monitor cyberinfrastructure](https://earthmonitor.org/)
+
+## The steps of your open science journey
+
+### Produce your own map
+ - Reuse the workflow to generate the snow covered area data cube
+ - Adapt the workflow to generate your personal contribution to mapping the snow covered area of the alps
+ - Choose an extent on the map that hasn't been mapped yet. We are producing patches of roughly 1km by 1km.
+ - Choose a time extent. The winter months of a given year.
+ - Add a step to reduce the time dimension. We want to create one layer for the winter season of the given year.
+ - Adapt the file format to Cloud Optimized GeoTiff (perfectly suitable for a raster file with one time step and one band).
+ - Download the result consisting of the STAC metadata and the COG
+
+### Validate the result
+ - This is still work in progress
+
+### Make the data FAIR and open
+
+
+
+
+ - Customize the STAC metadata (e.g. adding you as the author)
+ - Trigger the update of the STAC catalogue and web map by submitting your results. Your data will be openly available!
+ - A license is assigned to the whole collection of all the produced patches
+ - A doi is assigned to the whole collection of all the produced patches
+
+### Evaluate how FAIR the result is
+ - Do the FAIR self assessment tool after you've created your results
+ - You will get a score on how FAIR the dataset you have produced really is
+
+## Exercise
+Time to start your own open science journey. Produce a snow cover area map for a region that hasn't been mapped yet. FAIRify your results and make them publicly available!
+
+[Exercise 3.3 Sharing](https://github.com/EO-College/cubes-and-clouds/blob/main/lectures/3.3_data_sharing/3.3_exercises/33_data_sharing.ipynb)
+
+## FAIR Assessment
+This tool allows you to check how FAIR your results are. Give it a shot!
+
+**Embed: https://github.com/au-research/FAIR-Data-Assessment-Tool**
+
+## Community Mapping Project
+Have a look at your results! Have a look at the community mapping project you have contributed to!
+
+[STAC Browser Cubes and Clouds - Snow Cover](https://esa.pages.eox.at/cubes-and-clouds-catalog/browser/#/)
+
+## Congratulations
+Congrats! You've made it through the Cubes and Clouds online course!
+
+[](https://youtu.be/xoCBLAPPn0k)
+
+
+
diff --git a/_sources/3.3_data_sharing/3.3_exercises/33_data_sharing.ipynb b/_sources/3.3_data_sharing/3.3_exercises/33_data_sharing.ipynb
new file mode 100644
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--- /dev/null
+++ b/_sources/3.3_data_sharing/3.3_exercises/33_data_sharing.ipynb
@@ -0,0 +1,2118 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "3d8f1015-699c-4c22-b3be-61608b6d993a",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c6aed2f-1493-4636-a124-03c81b28bc52",
+ "metadata": {},
+ "source": [
+ "# 3.3 Data Sharing\n",
+ "Science is much more impactful once it's shared. Therefore, we are going to learn how to \n",
+ "open up our scientific output from a cloud platform, so that is openly available - and \n",
+ "has the chance to make the impact it should.\n",
+ "- Reuse the workflow we have used before for creating the snow covered area\n",
+ "- Select AOI,\n",
+ "- Recreate process graph, \n",
+ "- Download results for one time-step\n",
+ " - A Snow Cover Area map in the COG format\n",
+ " - A STAC metadata item that is provided with the result from openEO at CDSE\n",
+ "- Adapt the STAC item\n",
+ "- Upload the results and make them available openly via a STAC browser and web map\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7d102aad-5b61-422c-acb1-9c89621d70e3",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Libraries"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bfd758d4-a3d3-44ae-9cd0-7689af34effe",
+ "metadata": {},
+ "source": [
+ "Start by creating the folders and data files needed to complete the exercise"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "cc4a7712-62f8-4459-85a7-f6ac46e6d9d2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "!cp -r $DATA_PATH/33_results/ $HOME/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "54507438-b159-49cb-85e0-b98d3ddb690a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "!cp $DATA_PATH/_33_cubes_utilities.py $HOME/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1abf270c-d73e-4109-854c-3cf36a1b36d7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "import os\n",
+ "import subprocess\n",
+ "from datetime import datetime\n",
+ "\n",
+ "import openeo\n",
+ "import numpy as np\n",
+ "import leafmap\n",
+ "import geopandas as gpd\n",
+ "import shapely\n",
+ "from shapely.geometry import Polygon\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import PercentFormatter\n",
+ "\n",
+ "import rioxarray as rio\n",
+ "import xarray\n",
+ "from osgeo import gdal\n",
+ "\n",
+ "from _33_cubes_utilities import (\n",
+ " calculate_sca,\n",
+ " visualize_bbox,\n",
+ " create_bounding_box,\n",
+ " extract_metadata_geometry, \n",
+ " extract_metadata_time\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cf32192c-da37-4ae0-8b5c-5391b4616244",
+ "metadata": {},
+ "source": [
+ "## Login"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ad2ba48-40ef-4cc5-9bb6-de59d4b9a67e",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "Connect to the copernicus dataspace ecosystem."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "35624db3-17ef-4efb-a203-8d79113e2203",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "conn = openeo.connect('https://openeo.dataspace.copernicus.eu/')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0046a654-ba6f-4dae-84f2-7f11e099592f",
+ "metadata": {},
+ "source": [
+ "Authenticate login"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "de26fd5c-71da-4d28-b895-f5108a97a1d5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Authenticated using refresh token.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "conn.authenticate_oidc()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a1a2af45-ba11-41b8-9aaa-2b8169103f7b",
+ "metadata": {},
+ "source": [
+ "Check if the login worked"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bf1eb435-8e42-425f-9470-75081325787a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "conn.describe_account()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b2d634d7-e719-4cab-8066-ed0cc1c278f6",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Select an Area of Interest and Time Frame\n",
+ "\n",
+ "Start by selecting a center point of the area you would like to analyse from the map shown below. The starting extent is the full alps. Zoom in to an area and choose a region that has not been mapped yet. *Make sure not to overlap too much with already mapped areas by having a look at the [STAC Collection](https://esa.pages.eox.at/cubes-and-clouds-catalog/browser/#/?.language=en)*. It's a community mapping project :)\n",
+ "Create a 1 km bounding box around it. This will be the area you are calculating the snow covered area for. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3cfc19ce-a624-4ecf-8890-4f615e25ad63",
+ "metadata": {},
+ "source": [
+ "**Attention:**\n",
+ " Execute the cell below to show the map. Zoom to a location you want to analyze. Use the location symbol to select a point. A marker appears on the map. This is the center of your area of interest"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "c2999d0e-210e-4188-8c03-31719c06a70d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2f462abf085248ab9c7e82fe548a5d6f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Map(center=[47.005, 11.507], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "m = leafmap.Map(center=(47.005, 11.507), zoom=7.5)\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "35370594-f936-4789-8cc4-838b4913320a",
+ "metadata": {},
+ "source": [
+ "**Attention:**\n",
+ " Now this cell will get the coordinates of the marker you have placed. This will create a 1 km bounding box around the chosen location. And visualize it in the map above. *The marker moves to the center when you zoom in*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "42c6cb21-afff-4b5c-a7d0-403e81192995",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "feat = m.draw_features\n",
+ "geom = feat[0]['geometry']['coordinates']\n",
+ "\n",
+ "# set distance of 1 km around bbox\n",
+ "distance_km = 1\n",
+ "\n",
+ "# Create a bounding box around the point\n",
+ "bbox = create_bounding_box(geom[0], geom[1], distance_km)\n",
+ "visualize_bbox(m, bbox)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b8fc1d08-5464-4a04-a98a-b4f6d8c2bf82",
+ "metadata": {},
+ "source": [
+ "Now we'll select the time frame. We'll start with the winter months of 2023. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "553852f1-24dd-41be-96b7-6cabafc161a9",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "temporal_extent = [\"2023-02-01\", \"2023-06-01\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26325c28-6329-491f-9cdf-15c573a6d0f5",
+ "metadata": {},
+ "source": [
+ "## Reuse the process graph of the snow covered area data cube\n",
+ "We've saved the python code that we had used to create the snow cover area data cube into a python function `calculate_sca()`. It's stored in `cubes_utilities.py`. It creates a 4 dimensional data cube with the dimensions: x, y, time, bands.\n",
+ "As parameters we have exposed the bounding box and temporal extent. We will update them with the choices we have made above. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "af2c5198-c846-4478-97cd-28f051336af9",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "snow_map_4dcube = calculate_sca(conn, bbox, temporal_extent)\n",
+ "snow_map_4dcube"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7973af9d-15e0-4fae-bdfa-c35faf7f652a",
+ "metadata": {},
+ "source": [
+ "## Reduce the time dimension\n",
+ "We want to calculate the SCA for the winter period of a given year. Therefore, we need to reduce the values along the time dimension. We'll use the process `reduce_dimension()` with a `median()` to accomplish this. We are directly continuing to build on our process graph that we have loaded above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "1d8e9005-f0b7-49af-b83a-9a2983a2965b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "snow_map_3dcube = snow_map_4dcube.reduce_dimension(reducer=\"median\", dimension=\"t\")\n",
+ "snow_map_3dcube"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "251ba878-3841-479a-868e-886d701f50ef",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Download result\n",
+ "To finish our process graph we add the `save_result()` process choosing the `GTiff` format. It creates a COG out of the box with openEO on CDSE."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "d654adb3-fcc0-413c-bd5c-28de5564e570",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# create a batch job\n",
+ "snowmap_cog = snow_map_3dcube.save_result(format = \"GTiff\") #, options = {\"overviews\": \"AUTO\"})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ceb0edb7-c349-467d-9a42-bfbe5538a299",
+ "metadata": {},
+ "source": [
+ "We register the job as a batch job on the backend and start the processing. Depending on the traffic on the backend, this usually takes between 1 to 5 minutes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "1fc7c255-2f67-44a1-978b-2bb51b05eab3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0:00:00 Job 'j-2401318f19a84122b62657ed798f0c9e': send 'start'\n",
+ "0:00:17 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:00:22 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:00:29 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:00:37 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:00:47 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:01:00 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:01:16 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:01:36 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:02:01 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:02:31 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:03:09 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:03:56 Job 'j-2401318f19a84122b62657ed798f0c9e': created (progress N/A)\n",
+ "0:04:54 Job 'j-2401318f19a84122b62657ed798f0c9e': running (progress N/A)\n",
+ "0:05:54 Job 'j-2401318f19a84122b62657ed798f0c9e': finished (progress N/A)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "job = snowmap_cog.create_job(title=\"snowmap_cog\")\n",
+ "job.start_and_wait()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "28aada6d-8687-4370-ba52-e33e23f7d8ec",
+ "metadata": {},
+ "source": [
+ "Now let's wait until the job is finished and then download the results."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "219830d8-edec-48d8-b8b0-317112b32899",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "if job.status() == \"finished\":\n",
+ " results = job.get_results()\n",
+ " results.download_files(\"33_results/\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1eb74eb4-de0c-467e-97f6-0487c4334d6c",
+ "metadata": {},
+ "source": [
+ "Add statistics to the dataset via gdal, such as a summary of the values within the dataset and also some metadata, i.e. the legend (TIFFTAGS). And we reduce the datatype to the lowest possible datatype supported by COG uint8, since only have three values to represent (0, 1, 2). If you're interested you can check what happened via `!gdalinfo 33_results/openEO_uint8.tif`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "45b911a3-62fb-41a2-a34c-96ca436d921b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Input file size is 145, 210\n",
+ "0...10...20...30...40...50...60...70...80...90...100 - done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!gdal_translate -mo {TIFFTAG_IMAGEDESCRIPTION}=SnowCoveredArea_0=nosnow_1=snow_2-nodatavalue=cloud -ot Byte -of COG -a_nodata 2 -stats \"33_results/openEO.tif\" \"33_results/openEO_uint8.tif\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ba5d74b4-ee86-48c4-a6a6-ff8a667ccafd",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Load results\n",
+ "Now we can open the COG and visualize it. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "96022b16-5714-4f8b-949f-e9183270b451",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
+ 
+ 
+
+
+
+
+
+
+
+ 
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ![\"Cubes](\"https://raw.githubusercontent.com/EO-College/cubes-and-clouds/main/icons/cnc_3icons_process_circle.svg\"\n",){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "43f2c4b3-88f7-4ebf-8248-a72728d14012",
+ "metadata": {},
+ "source": [
+ "# 2.3 Data Access and Basic Processing\n",
+ "\n",
+ "## Aggregate Operators"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5691278c-4d66-4ee2-8592-75431cbff3c7",
+ "metadata": {},
+ "source": [
+ "### `aggregate_temporal_period`: temporal aggregation with predefined intervals"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40dbc4e1-929a-4256-9e62-605d5a424fca",
+ "metadata": {},
+ "source": [
+ "Start importing the necessary libraries and initialize a local connection for Client-Side Processing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4eca5284-2873-4b5c-9505-d67545fdec05",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import openeo\n",
+ "from openeo.local import LocalConnection\n",
+ "local_conn = LocalConnection('')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0420def5-ebd5-446e-a6d0-799e3534e5d2",
+ "metadata": {},
+ "source": [
+ "Create the starting Sentinel-2 datacube:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "053869f1-540f-48c1-b3ab-113d5be08bc6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "url = \"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a\"\n",
+ "\n",
+ "spatial_extent = {\"west\": 11.4, \"east\": 11.42, \"south\": 45.5, \"north\": 45.52}\n",
+ "temporal_extent = [\"2022-01-01\", \"2022-12-31\"]\n",
+ "bands = [\"red\",\"green\",\"blue\"]\n",
+ "\n",
+ "s2_cube = local_conn.load_stac(url=url,\n",
+ " spatial_extent=spatial_extent,\n",
+ " temporal_extent=temporal_extent,\n",
+ " bands=bands\n",
+ ")\n",
+ "s2_cube.execute()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49a04782-d412-4229-aa4b-d8e0283d5feb",
+ "metadata": {},
+ "source": [
+ "We might be interested in aggregating our data over periods like week, month, year etc., defining what operation to use to combine the data available in the chosen period.\n",
+ "\n",
+ "Using `aggregate_temporal_period` we can achieve this easily:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b652338d-5f31-4d33-9d46-b7ea76f1d23d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "s2_monthly_min = s2_cube.aggregate_temporal_period(period=\"month\",reducer=\"min\")\n",
+ "s2_monthly_min"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "420f8621-fb50-4fc1-89a2-4dfc7ac0beea",
+ "metadata": {},
+ "source": [
+ "Check what happens to the datacube inspecting the resulting xArray object. Now the `time` dimension has 12 labels, one for each month."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f38e5288-7b95-4693-a912-b51c31a60e16",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "s2_monthly_min.execute()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_sources/2.3_data_access/exercises/23_data_access_apply.ipynb b/_sources/2.3_data_access/exercises/23_data_access_apply.ipynb
new file mode 100644
index 0000000..1d6ba29
--- /dev/null
+++ b/_sources/2.3_data_access/exercises/23_data_access_apply.ipynb
@@ -0,0 +1,803 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5f4a0cab-e26e-4942-96ee-78de70890ad9",
+ "metadata": {},
+ "source": [
+ "![\"Cubes](\"https://raw.githubusercontent.com/EO-College/cubes-and-clouds/main/icons/cnc_3icons_share_cirlce.svg\"\n",){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c6aed2f-1493-4636-a124-03c81b28bc52",
+ "metadata": {},
+ "source": [
+ "# 3.3 Data Sharing\n",
+ "Science is much more impactful once it's shared. Therefore, we are going to learn how to \n",
+ "open up our scientific output from a cloud platform, so that is openly available - and \n",
+ "has the chance to make the impact it should.\n",
+ "- Reuse the workflow we have used before for creating the snow covered area\n",
+ "- Select AOI,\n",
+ "- Recreate process graph, \n",
+ "- Download results for one time-step\n",
+ " - A Snow Cover Area map in the COG format\n",
+ " - A STAC metadata item that is provided with the result from openEO at CDSE\n",
+ "- Adapt the STAC item\n",
+ "- Upload the results and make them available openly via a STAC browser and web map\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7d102aad-5b61-422c-acb1-9c89621d70e3",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Libraries"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bfd758d4-a3d3-44ae-9cd0-7689af34effe",
+ "metadata": {},
+ "source": [
+ "Start by creating the folders and data files needed to complete the exercise"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "cc4a7712-62f8-4459-85a7-f6ac46e6d9d2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "!cp -r $DATA_PATH/33_results/ $HOME/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "54507438-b159-49cb-85e0-b98d3ddb690a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "!cp $DATA_PATH/_33_cubes_utilities.py $HOME/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1abf270c-d73e-4109-854c-3cf36a1b36d7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "import os\n",
+ "import subprocess\n",
+ "from datetime import datetime\n",
+ "\n",
+ "import openeo\n",
+ "import numpy as np\n",
+ "import leafmap\n",
+ "import geopandas as gpd\n",
+ "import shapely\n",
+ "from shapely.geometry import Polygon\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import PercentFormatter\n",
+ "\n",
+ "import rioxarray as rio\n",
+ "import xarray\n",
+ "from osgeo import gdal\n",
+ "\n",
+ "from _33_cubes_utilities import (\n",
+ " calculate_sca,\n",
+ " visualize_bbox,\n",
+ " create_bounding_box,\n",
+ " extract_metadata_geometry, \n",
+ " extract_metadata_time\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cf32192c-da37-4ae0-8b5c-5391b4616244",
+ "metadata": {},
+ "source": [
+ "## Login"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ad2ba48-40ef-4cc5-9bb6-de59d4b9a67e",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "Connect to the copernicus dataspace ecosystem."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "35624db3-17ef-4efb-a203-8d79113e2203",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "conn = openeo.connect('https://openeo.dataspace.copernicus.eu/')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0046a654-ba6f-4dae-84f2-7f11e099592f",
+ "metadata": {},
+ "source": [
+ "Authenticate login"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "de26fd5c-71da-4d28-b895-f5108a97a1d5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Authenticated using refresh token.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "