---
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

![](https://cdn-uploads.huggingface.co/production/uploads/6364f1784f773b7e4cede70c/liSf9zd0uvbH-Sf9a-wrm.png)

## 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}
}
```