Stanford scientists combine satellite data and machine learning to map poverty

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. In the current issue of Science, Stanford researchers propose an accurate way to identify poverty in areas previously void of valuable survey information. The researchers used machine learning - the science of designing computer algorithms that learn from data - to extract information about poverty from high-resolution satellite imagery.