Researchers Mix Satellite Photos & Machine Learning to Find Poverty Zones

Researchers Mix Satellite Photos & Machine Learning to Find Poverty Zones By SEAN DUFFY       (CN) — 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…


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