Stanford scientists combine satellite data, machine learning to map poverty Stanford News

One of the biggest challenges in providing relief to people living in poverty is locating them. The availability of accurate and reliable information on the location of impoverished zones is surprisingly lacking for much of the world, particularly on the African continent. Aid groups and other international organizations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct. Stanford researchers combined satellite images and machine learning to predict poverty. Their improved poverty maps could help aid organizations and policymakers distribute funds more efficiently and enact and evaluate policies more effectively.