This folder contains examples and code snippets for the openEO Python client.
Environments: Python (plain Python code), Jupyter (e.g. Notebooks)
The Demonstrates column summarizes the key openEO functionality used in each community example.
| Title | Environment | Description | Demonstrates |
|---|---|---|---|
| Getting Started | Jupyter |
A beginner's sample notebook , featuring a straightforward workflow that illustrates the fundamental steps of accessing and utilizing available collections. | Loading collection from a backend; openEO process load_collection |
| Anomaly_Detection | Jupyter |
Check the crop growth on your field and compare it with similar fields in the region. | Loading data from WFS; openEO process Anomaly_Detection |
| BasicSentinelMerge | Jupyter |
Merging Sentinel 1 and 2 in a single datacube for further processing. | openEO processes merge_cubes, mask_scl_dilation, aggregate_temporal_period, array_interpolate_linear, sar_backscatter, filter_bbox |
| BurntMapping | Jupyter |
Classical Normalized Burnt Ratio(NBR) difference performed using VITO backend on a chunk polygon. The method followed in this notebook to compute DNBR is inspired from UN SPIDER's recommended practices. | openEO processes run_udf, chunk_polygon with polygon loaded from JSON, reduce_dimension |
| FloodNDWI | Jupyter |
Comparative study between pre and post image for Cologne during 2021 flood using NDWI. Refernce | Adding metadata to a datacube; openEO processes datacube_from_process, merge_datacube, reduce_dimension |
| FloodSAR | Jupyter |
Flood extent detection following UN SPIDER's recommended practices. | Thresholding using udf; openEO processes divide |
| GlobalFloodMonitoring | Jupyter |
Exploring the Global Flood Monitoring (GFM) product over the Pakistan flooding of 2022. Reference | Loading GFM data; saving data in specific tile_grids; plotting with additional data |
| LoadStac | Jupyter |
Load an external file in openEO by creating a Stac Item for it. | creating a simple stac item, openEO processes load_stac |
| ParcelDelineation | Jupyter |
Delineates parcels with Sentinel-2 data using ONNX models. The example focuses on the inference step, using pre-trained models. It demonstrates data loading and preprocessing, inference, post-processing and finally producing vector data as a result. | Selection of best tiles; Running ONNX models using udf; postprocessing using sobel filter and Felzenszwalb's algoritm in udf, openEO processes aggregate_spatial, build_child_callback, filter_labels, apply_neighborhood, raster_to_vector, filter_spatial |
| Publishing a UDP (S1 statistics) | Jupyter |
Computes various statistics for Sentinel-1 data and publishes it as a user-defined process (UDP) that can be re-used by others across multiple languages/environments. | Creating a udp with ProcessBuilder; Saving udpfor public reuse with save_user_defined_process; Publishing a service; credit usage; openEO processes rename_labels, apply_dimension, datacube_from_process |
| RankComposites | Jupyter |
Rank composites: max-NDVI & Best Available Pixel. | openEO processes apply_neigborhood, array_apply, filter_bbox, mask, aggregate_temporal_period |
| RescaleChunks | Jupyter |
The creation of a simple process to rescale Sentinel 2 RGB image along with the use of chunk_polygon apply with a (User Defined Function) UDF. | openEO processes run_udf, chunk_polygon, reduce_dimension |
| WorldCereal | Jupyter |
WorldCereal data extraction sample. | openEO processes merge_cubes, loading WorldCereal data |
| RVI | Jupyter |
Calculate Radar Vegetation Index | openEO processes sar_backscatter, spectral_nidices.compute_indices; plotting mean result and timeseries; Awesome Spectral Indices |
| OilSpill | Jupyter |
Oil Spill mapping with Sentinel-1 layer. | openEO processes sar_backscatter, apply, apply_kernel, rename_labels, merge_cubes; plotting binary image |
| ForestFire | Jupyter |
Wildfire mapping using Sentinel-2 | openEO processes apply_kernel,ndvi spectral_nidices.compute_indices; plotting comparative visualisation; Awesome Spectral Indices |
| Heatwave | Jupyter |
Heatwave mapping using LST layer. | openEO processes mask, apply_dimension, reduce_dimension; plotting Total number of days |
| AirQuality | Jupyter |
Explore Sentinel-5P air quality products | openEO processes merge_cubes, aggregate_temporal_period; plotting mean result and timeseries; product's correlation |
| StatisticalDataFill | Jupyter |
Uses Lowess Regression in openEO for Filling Missing Time Series Data¶ | openEO processes aggregate_temporal_period, apply_dimension; plotting mean result and timeseries ; SENTINEL_5P_L2 |
| DynamicLandCoverMapping | Jupyter |
A usecase of using Random Forest from openEO¶ | openEO processes MlModel, compute_and_rescale_indices, array_create, array_concat, array_interpolate_linear; plotting predicted land cover mapping ; SENTINEL2_L2A, SENTINEL1_GRD |
- Please provide each contribution in a separate folder.