title: Combining satellite imagery and machine learning to predict poverty.
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.
authors.affiliation: Department of Computer Science, Stanford University, Stanford, CA, USA. Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
authors.forename: Neal
authors.initials: N
authors.lastname: Jean
authors.affiliation: 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.
authors.forename: Marshall
authors.initials: M
authors.lastname: Burke
authors.affiliation: Department of Computer Science, Stanford University, Stanford, CA, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Xie
authors.affiliation: Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.
authors.forename: W Matthew
authors.initials: WM
authors.lastname: Alampay Davis
authors.affiliation: Department of Earth System Science, Stanford University, Stanford, CA, USA. Center on Food Security and the Environment, Stanford University, Stanford, CA, USA.
authors.forename: David B
authors.initials: DB
authors.lastname: Lobell
authors.affiliation: Department of Computer Science, Stanford University, Stanford, CA, USA.
authors.forename: Stefano
authors.initials: S
authors.lastname: Ermon
mesh_terms: D003906:Developing Countries / Q000191:economics*; D006801:Humans; D007182:Income; D000069550:Machine Learning; D008295:Malawi; D009549:Nigeria; D011203:Poverty / Q000191:economics*; D012432:Rwanda; D063809:Satellite Imagery / Q000379:methods*; D013636:Tanzania; D014454:Uganda