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Science. 2016 Aug 19;353(6301):790-4. doi: 10.1126/science.aaf7894.

Combining satellite imagery and machine learning to predict poverty.

Author information

1
Department of Computer Science, Stanford University, Stanford, CA, USA. Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
2
Department of Earth System Science, Stanford University, Stanford, CA, USA. Center on Food Security and the Environment, Stanford University, Stanford, CA, USA. National Bureau of Economic Research, Boston, MA, USA. mburke@stanford.edu.
3
Department of Computer Science, Stanford University, Stanford, CA, USA.
4
Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.
5
Department of Earth System Science, Stanford University, Stanford, CA, USA. Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.

Abstract

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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PMID:
27540167
DOI:
10.1126/science.aaf7894
[Indexed for MEDLINE]
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