03
Set
16

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-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.

Combining satellite imagery and machine learning to predict poverty
Neal Jean, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, Stefano Ermon

Science  19 Aug 2016:
Vol. 353, Issue 6301, pp. 790-794
DOI: 10.1126/science.aaf7894

See on science.sciencemag.org



Time is real? I think not

settembre: 2016
L M M G V S D
« Ago   Ott »
 1234
567891011
12131415161718
19202122232425
2627282930  

Commenti recenti

Inserisci il tuo indirizzo e-mail per iscriverti a questo blog e ricevere notifiche di nuovi messaggi per e-mail.

Segui assieme ad altri 833 follower

Latest Tweets

Alessandro Cerboni


%d blogger cliccano Mi Piace per questo: