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Combining satellite imagery and machine learning to predict poverty
Journal Article

Policy-makers in the world's poorest countries are often forced to make decisions based on limited data. Consider Angola, which recently conducted its first postcolonial census. In the 44 years that elapsed between the prior census and the recent one, the country's population grew from 5.6 million to 24.3 million, and the country experienced a protracted civil war that displaced millions of citizens. In situations where reliable survey data are missing or out of date, a novel line of research offers promising alternatives. On page 790 of this issue, Jean et al.(1) apply recent advances in machine learning to high-resolution satellite imagery to accurately measure regional poverty in Africa.

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