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Improvements to dataset tables #649
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@@ -164,8 +164,8 @@ Kenya Crop Type | |||
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.. autoclass:: CV4AKenyaCropType | |||
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Deep Globe Land Cover Challenge |
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Name was too long, cut off in miniviewer.
`Aster Global DEM`_,Mask,Aster,"3,601x3,601",30 | ||
`Canadian Building Footprints`_,Labels,Generated,, |
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"Labels" is ambiguous. When I hear "labels" I think classification or regression. I think "geometries" is more clear for this dataset. Also, "generated" doesn't tell me anything.
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"generated" to me is model output vs. manual interpretation
`Aster Global DEM`_,Mask,Aster,"3,601x3,601",30 | ||
`Canadian Building Footprints`_,Labels,Generated,, | ||
`Chesapeake Land Cover`_,"Imagery, Labels",,,1 |
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I think "mask" is better than "labels" for semantic segmentation problems
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Would go with "Masks"
`Canadian Building Footprints`_,Labels,Generated,, | ||
`Chesapeake Land Cover`_,"Imagery, Labels",,,1 | ||
`Global Mangrove Distribution`_,Mask,Generated,,3 | ||
`Cropland Data Layer`_,Labels,Aerial,, |
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Imagery is created using Landsat, so same resolution as Landsat
`EU-DEM`_,Mask,"Aster, SRTM, Russian Topomaps",-,25 | ||
`GBIF`_,Points,Citizen Scientists,-,- | ||
`GlobBiomass`_,Mask,Landsat,"45,000x45,000",100 | ||
`iNaturalist`_,Points,Citizen Scientists,-,- |
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iNaturalist was missing from the table.
For GBIF/EDDMapS/iNaturalist I used "points" for the dataset type and "citizen scientists" for the label sources
@@ -1,34 +1,34 @@ | |||
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 | |||
`Benin Cashew Plantations`_,S,Airbus Pléiades,70,6,"1,122x1,186",10,MSI |
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If you run gdalinfo
on the files in @calebrob6's VM, the pixel size is 10, not 0.5. Also, dimensions are reversed.
`ETCI2021 Flood Detection`_,S,Sentinel-1,"66,810",2,256x256,5--20,SAR | ||
`EuroSAT`_,C,Sentinel-2,"27,000",10,64x64,10,MSI | ||
`FAIR1M`_,OD,Gaofen/Google Earth,"15,000",37,"1,024x1,024",0.3--0.8,RGB | ||
`Forest Damage`_,OD,Drone imagery,"1,543",4,"1,500x1,500",,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`_,S,Aerial,360,,"5,000x5,000",0.3,RGB | ||
`Inria Aerial Image Labeling`_,S,Aerial,360,2,"5,000x5,000",0.3,RGB |
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Binary semantic segmentation so 2 classes
`Deep Globe Land Cover Challenge`_,S,DigitalGlobe +Vivid,803,7,"2,448x2,448",0.5,RGB | ||
`DFC2022`_,S,Aerial,,15,"2,000x2,000",0.5,RGB | ||
`Deep Globe Land Cover`_,S,DigitalGlobe +Vivid,803,7,"2,448x2,448",0.5,RGB | ||
`DFC2022`_,S,Aerial,"3,981",15,"2,000x2,000",0.5,RGB | ||
`ETCI2021 Flood Detection`_,S,Sentinel-1,"66,810",2,256x256,5--20,SAR | ||
`EuroSAT`_,C,Sentinel-2,"27,000",10,64x64,10,MSI | ||
`FAIR1M`_,OD,Gaofen/Google Earth,"15,000",37,"1,024x1,024",0.3--0.8,RGB | ||
`Forest Damage`_,OD,Drone imagery,"1,543",4,"1,500x1,500",,RGB |
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Still don't know the resolution of this. It looks like Dan Morris (AI for Earth) is associated with this project and may know. There is also contact info on the dataset webpage: https://lila.science/datasets/forest-damages-larch-casebearer/
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Sent an email to the author
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FYI Dan is at google now
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Can you change "mask" --> "masks"? I'll open an issue to update resolution on Forest Damages dataset.
* Improvements to dataset tables * mask -> masks
Various improvements to dataset tables in the docs. Will comment in-line.