WILDS
Collection
WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications. • 9 items • Updated • 4
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This is a processed version of LandSat 5/7/8 satellite imagery originally from Google Earth Engine under the names LANDSAT/LC08/C01/T1_SR,LANDSAT/LE07/C01/T1_SR,LANDSAT/LT05/C01/T1_SR,
nighttime light imagery from the DMSP and VIIRS satellites (Google Earth Engine names NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4 and NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG)
and processed DHS survey metadata obtained from https://github.com/sustainlab-group/africa_poverty and originally from https://dhsprogram.com/data/available-datasets.cfm.
@article{yeh2020using,
author = {Yeh, Christopher and Perez, Anthony and Driscoll, Anne and Azzari, George and Tang, Zhongyi and Lobell, David and Ermon, Stefano and Burke, Marshall},
day = {22},
doi = {10.1038/s41467-020-16185-w},
issn = {2041-1723},
journal = {Nature Communications},
month = {5},
number = {1},
title = {{Using publicly available satellite imagery and deep learning to understand economic well-being in Africa}},
url = {https://www.nature.com/articles/s41467-020-16185-w},
volume = {11},
year = {2020}
}