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1 Jean, N.; Burke, M.; Xie, M.; Davis, W. M.; Lobell, D. B.; Ermon, S.. 2016. Combining satellite imagery and machine learning to predict poverty. Science, 353(6301):790-794. [doi: https://doi.org/10.1126/science.aaf7894]
Poverty ; Satellite imagery ; Forecasting ; Living standards ; Household consumption ; Household expenditure ; Machine learning ; Neural networks ; Models ; Performance evaluation ; Economic aspects ; Assets / Nigeria / Tanzania / Uganda / Malawi / Rwanda
(Location: IWMI HQ Call no: e-copy only Record No: H047755)
(7.12 MB)
Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries—Nigeria, Tanzania, Uganda, Malawi, and Rwanda—we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains.
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