Stanford researchers combine satellite data, machine learning to map poverty Mirage News

Researchers with Stanford University have used machine learning to extract information about poverty from satellite imagery of areas where survey information from sources on the ground is previously 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." In trying to understand whether high-resolution satellite imagery, an unconventional but readily available data source, could inform estimates of where impoverished people live, the researchers based their solution on an assumption that areas that are brighter at night are usually more developed, therefore used the "nightlight" data to identify features in the higher-resolution daytime imagery that are correlated with economic development. However, while machine learning, the science of designing computer algorithms that learn from data, works best when it can access vast amounts of data, there was little data on poverty to start with for the researchers.