CNS - Researchers Mix Satellite Photos & Machine Learning to Find Poverty Zones

Logistical problems in identifying impoverished communities may become relics of the past, as researchers are now combining satellite data with advanced computer algorithms to bypass traditional hurdles. In a study published Friday in the journal Science, Stanford University researchers proposed a way to use machine learning -- the science of designing computer algorithms that learn from data -- to interpret data acquired from high-resolution satellite imagery. The availability of accurate and reliable information on the location of impoverished zones is sorely lacking, which forces aid groups and other international organizations to conduct door-to-door surveys to supplement existing data -- an expensive and time-consuming process. Using earlier machine-learning methods, the team found pockets of poverty across five African nations which have previously been void of valuable survey information. "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 co-author Marshall Burke.