Stanford scientists combine satellite data and machine learning to map poverty

In the current issue of Science, Stanford researchers propose an accurate way to identify poverty in areas previously void of valuable survey information, says the news statement. "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 study coauthor Marshall Burke, an assistant professor of Earth system science at Stanford and a fellow at the Center on Food Security and the Environment. "At the same time, we collect all sorts of other data in these areas – like satellite imagery – constantly." According to Stanford, the researchers sought to understand whether high-resolution satellite imagery – an unconventional but readily available data source – could inform estimates of where impoverished people live. "The difficulty was that while standard machine learning approaches work best when they can access vast amounts of data, in this case there was little data on poverty to start with," the release says.