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---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: id_val
path: data/id_val-*
- split: id_test
path: data/id_test-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
sequence:
sequence:
sequence: float32
- name: label
dtype: int64
- name: lat
dtype: float64
- name: lon
dtype: float64
- name: wealthpooled
dtype: float64
- name: country
dtype: int64
- name: year
dtype: int64
- name: urban
dtype: bool
- name: nl_mean
dtype: float64
- name: nl_center
dtype: float64
- name: households
dtype: int64
splits:
- name: train
num_bytes: 15801660900.87406
num_examples: 9797
- name: id_val
num_bytes: 1611295216.9003966
num_examples: 999
- name: id_test
num_bytes: 1612908125.025422
num_examples: 1000
- name: val
num_bytes: 6304857860.724375
num_examples: 3909
- name: test
num_bytes: 6391954899.475747
num_examples: 3963
download_size: 16974411052
dataset_size: 31722677003
license: other
license_details: LandSat/DMSP/VIIRS data is U.S. Public Domain
task_categories:
- image-classification
tags:
- map
- poverty
- satellite
size_categories:
- 10K<n<100K
---
# PovertyMap-Wilds: Poverty mapping across different countries
## Dataset Description
- **Homepage**: [github.com:sustainlab-group/africa_poverty](https://github.com/sustainlab-group/africa_poverty)
- **DOI**: https://doi.org/10.1038/s41467-020-16185-w
- **Publication Date** 2020-05-22
## 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
```bibtex
@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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