Combining satellite imagery and machine learning to predict poverty

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-resolut...

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Vydáno v:	Science (American Association for the Advancement of Science) Ročník 353; číslo 6301; s. 790
Hlavní autoři:	Jean, Neal, Burke, Marshall, Xie, Michael, Alampay Davis, W Matthew, Lobell, David B, Ermon, Stefano
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States 19.08.2016
Témata:	Developing Countries - economics Humans Income Machine Learning Malawi Nigeria Poverty - economics Rwanda Satellite Imagery - methods Tanzania Uganda Uganda Nigeria Malawi Rwanda Tanzania
ISSN:	1095-9203, 1095-9203
On-line přístup:	Zjistit podrobnosti o přístupu
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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.
AbstractList	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. 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.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.
Author	Alampay Davis, W Matthew Jean, Neal Lobell, David B Ermon, Stefano Burke, Marshall Xie, Michael
Author_xml	– sequence: 1 givenname: Neal surname: Jean fullname: Jean, Neal organization: Department of Computer Science, Stanford University, Stanford, CA, USA. Department of Electrical Engineering, Stanford University, Stanford, CA, USA – sequence: 2 givenname: Marshall surname: Burke fullname: Burke, Marshall email: mburke@stanford.edu organization: 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 – sequence: 3 givenname: Michael surname: Xie fullname: Xie, Michael organization: Department of Computer Science, Stanford University, Stanford, CA, USA – sequence: 4 givenname: W Matthew surname: Alampay Davis fullname: Alampay Davis, W Matthew organization: Center on Food Security and the Environment, Stanford University, Stanford, CA, USA – sequence: 5 givenname: David B surname: Lobell fullname: Lobell, David B organization: Department of Earth System Science, Stanford University, Stanford, CA, USA. Center on Food Security and the Environment, Stanford University, Stanford, CA, USA – sequence: 6 givenname: Stefano surname: Ermon fullname: Ermon, Stefano organization: Department of Computer Science, Stanford University, Stanford, CA, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/27540167$$D View this record in MEDLINE/PubMed
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SubjectTerms	Developing Countries - economics Humans Income Machine Learning Malawi Nigeria Poverty - economics Rwanda Satellite Imagery - methods Tanzania Uganda
Title	Combining satellite imagery and machine learning to predict poverty
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