|   | 
Details
   web
Record
Author Jean, N.; Burke, M.; Xie, M.; Davis, W.M.; Lobell, D.B.; Ermon, S.
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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0036-8075 ISBN Medium (up)
Area Expedition Conference
Notes Approved no
Call Number LoNNe @ kyba @ Serial 1507
Permanent link to this record