WILDS
Collection
WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications.
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10 items
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Updated
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4
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image
sequence | label
int64 | lat
float64 | lon
float64 | wealthpooled
float64 | country
int64 | year
int64 | urban
bool | nl_mean
float64 | nl_center
float64 | households
int64 |
|---|---|---|---|---|---|---|---|---|---|---|
[[[-0.6753469109535217,-0.4659774899482727,-0.6317282319068909,-0.862906813621521,-0.915249168872833(...TRUNCATED) | 0 | 6.358924 | 10.372609 | -0.959787 | 3 | 2,011 | false | -0.173862 | -0.173862 | 27 |
[[[-0.6055570840835571,-0.5837477445602417,-0.4659774899482727,-0.36129286885261536,-0.4616157114505(...TRUNCATED) | 0 | 6.498947 | 10.40116 | -0.859114 | 3 | 2,011 | false | -0.173862 | -0.173862 | 25 |
[[[-0.4419873356819153,-0.4572537839412689,-0.5532147288322449,-0.5444909930229187,-0.45725378394126(...TRUNCATED) | 0 | 5.100264 | 9.860473 | -0.356035 | 3 | 2,011 | true | -0.173862 | -0.173862 | 23 |
[[[-0.2151706963777542,-0.4158162474632263,-0.23916083574295044,0.06616932153701782,0.19266344606876(...TRUNCATED) | 1 | 4.968337 | 9.940907 | 1.071792 | 3 | 2,011 | true | 0.032012 | 0.163566 | 20 |
[[[-0.26097017526626587,-0.4179970324039459,-0.32203611731529236,-0.5008724927902222,-0.596833407878(...TRUNCATED) | 0 | 4.948354 | 9.936374 | 0.874895 | 3 | 2,011 | true | 0.021436 | 0.178322 | 21 |
[[[-0.27841758728027344,-0.1344762146472931,-0.4332636296749115,-0.2915032207965851,-0.4136352539062(...TRUNCATED) | 0 | 4.914035 | 9.861978 | 0.046341 | 3 | 2,011 | false | -0.173862 | -0.173862 | 28 |
[[[0.38458529114723206,0.15340669453144073,0.2799004912376404,0.15776830911636353,0.2341008484363556(...TRUNCATED) | 0 | 4.862323 | 9.823747 | -0.044411 | 3 | 2,011 | true | -0.173862 | -0.173862 | 22 |
[[[-0.33948367834091187,-0.3438454568386078,-0.3634738326072693,-0.24134181439876556,-0.265331953763(...TRUNCATED) | 0 | 4.847395 | 9.820931 | 0.702673 | 3 | 2,011 | true | -0.173862 | -0.173862 | 20 |
[[[0.4107562303543091,0.22755827009677887,0.3191572427749634,0.044359974563121796,0.5023552179336548(...TRUNCATED) | 0 | 4.709088 | 9.730045 | 0.580159 | 3 | 2,011 | true | -0.109047 | 0.046684 | 22 |
[[[-0.33730271458625793,-0.4245399236679077,-0.10394316911697388,-0.19118037819862366,-0.33730271458(...TRUNCATED) | 0 | 4.710434 | 9.730012 | 0.850171 | 3 | 2,011 | true | -0.109047 | 0.046684 | 19 |
End of preview.
PovertyMap-Wilds: Poverty mapping across different countries
Description
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.
Citation
@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}
}
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