Статья

Combining satellite imagery and machine learning to predict poverty

Neal JeanDepartment of Computer Science, Stanford University, Stanford, CA, USAMarshall BurkeCenter on Food Security and the Environment, Stanford University, Stanford, CA, USASang Michael XieDepartment of Computer Science, Stanford University, Stanford, CA, USAW. Matthew DavisCenter on Food Security and the Environment, Stanford University, Stanford, CA, USADavid B. LobellCenter on Food Security and the Environment, Stanford University, Stanford, CA, USAStefano ErmonDepartment of Computer Science, Stanford University, Stanford, CA, USA

2016en

ABI

Аннотация

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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Идентификаторы

DOI: 10.1126/science.aaf7894

Цитирования и источники

Цитирований: 2Использованных источников: 0

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