Journal
NATURE COMMUNICATIONS
Volume 11, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-020-16185-w
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Funding
- USAID Bureau for Food Security
- Stanford King Center on Global Development
- National Science Foundation [1651565, 1522054]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1522054] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1651565] Funding Source: National Science Foundation
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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 similar to 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.
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