Work on Data table

docs/api/datasets.rst

@@ -13,7 +13,7 @@ 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: 50 50
+   :widths: 30 15 20 20 15
    :header-rows: 1
    :align: center
    :file: generic_datasets.csv
@@ -102,7 +102,7 @@ Open Buildings
 ^^^^^^^^^^^^^^
 
 .. autoclass:: OpenBuildings
-    
+
 Sentinel
 ^^^^^^^^
 
@@ -116,11 +116,11 @@ 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, D = change detection
+.. 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: vision_datasets.csv
+   :file: non_geo_datasets.csv
 
 ADVANCE (AuDio Visual Aerial sceNe reCognition datasEt)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

docs/api/generic_datasets.csv

@@ -1,7 +1,15 @@
-Dataset,Type
-Landsat,Imagery
-Sentinel,Imagery
-NAIP,Imagery
-Cropland Data Layer,Labels
-Chesapeake Land Cover,Labels
-Canadian Buildings Footprints,Labels
+Dataset,Type,Source,Size (px),Resolution (m)
+`Aboveground Live Woody Biomass Density`_,Mask,"Landsat, LiDAR","~40,000x40,000",~30
+`Aster Global Digital Evaluation Model`_,Mask,Aster,"3,601x3,601",30
+`Canadian Building Footprints`_,Labels,Generated,,-
+`Chesapeake Bay High-Resolution Land Cover Project`_,"Imagery, Labels",,,1
+`CMS Global Mangrove Canopy Dataset`_,Mask,Generated,,3
+`Cropland Data Layer (CDL)`_,Labels,Aerial,,
+`EnviroAtlas`_,"Imagery, Labels",Aerial,,1
+`Esri2020`_,Labels,Sentinel-2,,10
+`EU-DEM`_,Labels,"Aster, SRTM, Russian Topomaps",,25
+`GlobBiomass`_,Labels,Landsat,"45,000x45,000",~100
+`Landsat`_,Imagery,Landsat,,30
+`National Agriculture Imagery Program (NAIP)`_,Imagery,Aerial,,1
+`Open Buildings`_,Labels,Generated,,-
+`Sentinel`_,Imagery,Sentinel,,10

docs/api/non_geo_datasets.csv

@@ -0,0 +1,32 @@
+Dataset,Task,Source,# Samples,# Classes,Size (px),Resolution (m),Bands
+`ADVANCE (AuDio Visual Aerial sceNe reCognition datasEt)`_,C,"Google Earth, Freesound",5075,13,512x512,0.5,RGB
+`BigEarthNet`_,C,Sentinel-1/2,590326,19--43,120x120,10,"SAR, MSI"
+`Cars Overhead With context (COWC)`_,"C, R","CSUAV AFRL, ISPRS, LINZ, AGRC",388435,2,256x256,0.15,RGB
+`CV4A Kenya Crop Type Competition`_,S,Sentinel-2,4688,7,"3,035x2,016",10,MSI
+`2022 IEEE GRSS Data Fusion Contest (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 (fine-grAined object recognition in high-Resolution imagery)`_,OD,Gaofen/Google Earth,15000,37,"1,024x1,024",0.3–0.8,RGB
+`GID-15 (Gaofen Image Dataset)`_,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 (Land Cover from Aerial Imagery)`_,S,Aerial,10674,5,512x512,0.25–0.5,RGB
+`LEVIR-CD+ (LEVIR Change Detection +)`_,CD,Google Earth,985,2,"1,024x1,024",0.5,RGB
+`LoveDA (Land-cOVEr Domain Adaptive semantic segmentation)`_,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 (Onera Satellite Change Detection)`_,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 (Remote Sensing Image Scene Classification)`_,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"
+`Smallholder Cashew Plantations in Benin`_,S,Airbus Pléiades,70,6,"1,186x1,122",0.5,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 Wind Estimation Competition`_,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