metadata

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
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

@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}
}