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Author Jean, N.; Burke, M.; Xie, M.; Davis, W.M.; Lobell, D.B.; Ermon, S. url  doi
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  Title Combining satellite imagery and machine learning to predict poverty Type Journal Article
  Year 2016 Publication Science Abbreviated Journal Science  
  Volume 353 Issue 6301 Pages 790-794  
  Keywords Remote Sensing  
  Abstract Nighttime lighting is a rough proxy for economic wealth, and nighttime maps of the world show that many developing countries are sparsely illuminated. Jean et al. combined nighttime maps with high-resolution daytime satellite images (see the Perspective by Blumenstock). With a bit of machine-learning wizardry, the combined images can be converted into accurate estimates of household consumption and assets, both of which are hard to measure in poorer countries. Furthermore, the night- and day-time data are publicly available and nonproprietary.  
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  Series Volume Series Issue Edition  
  ISSN 0036-8075 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number LoNNe @ kyba @ Serial 1507  
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