Satellite data and machine learning used to map poverty

Researchers with Stanford University have used machine learning to extract information about poverty from satellite imagery of areas where ground-level data are unavailable.

"We have a limited number of surveys conducted in scattered villages across the African continent, but otherwise we have very little local-level information on poverty," said Marshall Burke, an assistant professor of earth system science at Stanford and co-author of a study in the current issue of journal Science.

"At the same time, we collect all sorts of other data in these areas - like satellite imagery - constantly."