Using publicly available satellite imagery and deep learning to understand economic well-being in Africa

Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 Afr...

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Veröffentlicht in:Nature communications Jg. 11; H. 1; S. 2583 - 11
Hauptverfasser: Yeh, Christopher, Perez, Anthony, Driscoll, Anne, Azzari, George, Tang, Zhongyi, Lobell, David, Ermon, Stefano, Burke, Marshall
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 22.05.2020
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/705/117
706/689/159
Benchmarks
Cellular telephones
Data collection
Deep learning
Economics
Estimates
High resolution
Humanities and Social Sciences
Image resolution
Machine learning
multidisciplinary
Satellite imagery
Science
Science (multidisciplinary)
Variation
Well being
Africa
ISSN:2041-1723, 2041-1723
Online-Zugang:Volltext
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Zusammenfassung:Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectral satellite imagery. Models can explain 70% of the variation in ground-measured village wealth in countries where the model was not trained, outperforming previous benchmarks from high-resolution imagery, and comparison with independent wealth measurements from censuses suggests that errors in satellite estimates are comparable to errors in existing ground data. Satellite-based estimates can also explain up to 50% of the variation in district-aggregated changes in wealth over time, with daytime imagery particularly useful in this task. We demonstrate the utility of satellite-based estimates for research and policy, and demonstrate their scalability by creating a wealth map for Africa’s most populous country. It is generally difficult to scale derived estimates and understand the accuracy across locations for passively-collected data sources, such as mobile phones and satellite imagery. Here the authors show that their trained deep learning models are able to explain 70% of the variation in ground-measured village wealth in held-out countries, outperforming previous benchmarks from high-resolution imagery with errors comparable to that of existing ground data.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-020-16185-w