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Combining satellite imagery and machine learning to predict poverty
Ist Teil von
Science (American Association for the Advancement of Science), 2016-08, Vol.353 (6301), p.790-794
Ort / Verlag
United States: American Association for the Advancement of Science
Erscheinungsjahr
2016
Link zum Volltext
Quelle
American Association for the Advancement of Science
Beschreibungen/Notizen
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.