Add dataset table to the documentation

docs/api/datasets.rst

@@ -12,6 +12,12 @@ Geospatial Datasets
 
 :class:`GeoDataset` is designed for datasets that contain geospatial information, like latitude, longitude, coordinate system, and projection. Datasets containing this kind of information can be combined using :class:`IntersectionDataset` and :class:`UnionDataset`.
 
+.. csv-table:: Geospatial Datasets
+   :widths: 30 15 20 20 15
+   :header-rows: 1
+   :align: center
+   :file: geo_datasets.csv
+
 Aboveground Woody Biomass
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -125,6 +131,12 @@ Non-geospatial Datasets
 
 :class:`VisionDataset` is designed for datasets that lack geospatial information. These datasets can still be combined using :class:`ConcatDataset <torch.utils.data.ConcatDataset>`.
 
+.. csv-table:: C = classification,  R = regression, S = semantic segmentation, I = instance segmentation, T = time series, CD = change detection, OD = object detection
+   :widths: 15 7 15 12 11 12 15 13
+   :header-rows: 1
+   :align: center
+   :file: non_geo_datasets.csv
+
 ADVANCE
 ^^^^^^^
 

docs/api/geo_datasets.csv

@@ -0,0 +1,17 @@
+Dataset,Type,Source,Size (px),Resolution (m)
+`Aboveground Woody Biomass`_,Mask,"Landsat, LiDAR","~40,000x40,000",~30
+`Aster Global DEM`_,Mask,Aster,"3,601x3,601",30
+`Canadian Building Footprints`_,Labels,Generated,,
+`Chesapeake Land Cover`_,"Imagery, Labels",,,1
+`Global Mangrove Distribution`_,Mask,Generated,,3
+`Cropland Data Layer`_,Labels,Aerial,,
+`EDDMapS`_,Labels,Vector,,
+`EnviroAtlas`_,"Imagery, Labels",Aerial,,1
+`Esri2020`_,Labels,Sentinel-2,,10
+`EU-DEM`_,Labels,"Aster, SRTM, Russian Topomaps",,25
+`GBIF`_,Labels,Vector,,
+`GlobBiomass`_,Labels,Landsat,"45,000x45,000",~100
+`Landsat`_,Imagery,Landsat,,30
+`NAIP`_,Imagery,Aerial,,1
+`Open Buildings`_,Labels,Generated,,
+`Sentinel`_,Imagery,Sentinel,,10

docs/api/non_geo_datasets.csv

@@ -0,0 +1,33 @@
+Dataset,Task,Source,# Samples,# Classes,Size (px),Resolution (m),Bands
+`ADVANCE`_,C,"Google Earth, Freesound","5,075",13,512x512,0.5,RGB
+`Benin Cashew Plantations`_,S,Airbus Pléiades,70,6,"1,186x1,122",0.5,MSI
+`BigEarthNet`_,C,Sentinel-1/2,590326,19--43,120x120,10,"SAR, MSI"
+`COWC`_,"C, R","CSUAV AFRL, ISPRS, LINZ, AGRC",388435,2,256x256,0.15,RGB
+`Kenya Crop Type`_,S,Sentinel-2,4688,7,"3,035x2,016",10,MSI
+`DFC2022`_,S,Aerial,,15,"2,000x2,000",0.5,RGB
+`ETCI2021 Flood Detection`_,S,Sentinel-1,66810,2,256x256,5--20,SAR
+`EuroSAT`_,C,Sentinel-2,27000,10,64x64,10,MSI
+`FAIR1M`_,OD,Gaofen/Google Earth,15000,37,"1,024x1,024",0.3-0.8,RGB
+`Forest Damage`_,OD,Drone imagery,1543,4,1500x1500,,RGB
+`GID-15`_,S,Gaofen-2,150,15,"6,800x7,200",3,RGB
+`IDTReeS`_,"OD,C",Aerial,591,33,200x200,0.1-1,RGB
+`Inria Aerial Image Labeling`_,O,Aerial,360,,5000x5000,0.3,RGB
+`LandCover.ai`_,S,Aerial,10674,5,512x512,0.25-0.5,RGB
+`LEVIR-CD+`_,CD,Google Earth,985,2,"1,024x1,024",0.5,RGB
+`LoveDA`_,S,Google Earth,5987,7,"1,024x1,024",0.3,RGB
+`NASA Marine Debris`_,OD,PlanetScope,707,1,256x256,3,RGB
+`NWPU VHR-10`_,I,"Google Earth, Vaihingen",800,10,"358--1,728",0.08-2,RGB
+`OSCD`_,CD,Sentinel-2,24,2,"40--1,180",60,MSI
+`PatternNet`_,C,Google Earth,30400,38,256x256,0.06-5,RGB
+`Potsdam`_,S,Aerial,38,6,"6,000x6,000",0.05,MSI
+`RESISC45`_,C,Google Earth,31500,45,256x256,0.2-30,RGB
+`Seasonal Contrast`_,T,Sentinel-2,100K--1M,,264x264,10,MSI
+`SEN12MS`_,S,"Sentinel-1/2, MODIS",180662,33,256x256,10,"SAR, MSI"
+`So2Sat`_,C,Sentinel-1/2,400673,17,32x32,10,"SAR, MSI"
+`SpaceNet`_,I,WorldView-2/3 Planet Lab Dove,"1,889--28,728",2,102--900,0.5-4,MSI
+`Tropical Cyclone`_,R,GOES 8--16,108110,,256x256,4K--8K,MSI
+`UC Merced`_,C,USGS National Map,21000,21,256x256,0.3,RGB
+`USAVars`_,S,NAIP Aerial,~100K,,,4,"RGB, Near-Infrared"
+`Vaihingen`_,S,Aerial,33,6,"1,281--3,816",0.09,RGB
+`xView2`_,CD,Maxar,3732,4,"1,024x1,024",0.8,RGB
+`ZueriCrop`_,"I, T",Sentinel-2,116K,48,24x24,10,MSI

docs/user/contributing.rst

@@ -157,3 +157,15 @@ TorchGeo has a number of tutorials included in the documentation that can be run
 .. code-block:: console
 
    $ pytest --nbmake docs/tutorials
+
+
+Datasets
+--------
+
+A major component of TorchGeo is the large collection of :mod:`torchgeo.datasets` that have been implemented. Adding new datasets to this list is a great way to contribute to the library. A brief checklist to follow when implementing a new dataset:
+
+* Implement the dataset extending either :class:`~torchgeo.datasets.GeoDataset` or :class:`~torchgeo.datasets.GeoDataset`
+* Add the dataset definition to ``torchgeo/datasets/__init__.py``
+* Add appropriate tests with 100% test coverage to ``tests/datasets/``
+* Add the dataset to ``docs/api/datasets.rst``
+* Add the dataset metadata to either ``docs/api/geo_datasets.csv`` or ``docs/api/non_geo_datasets.csv``