Scientists Map Poverty Using Satellite Data, Machine Learning

Advertisement Organizations often conduct door-to-door surveys to identify people living in poverty, but the downside is that these surveys are often time-consuming and expensive. Indeed, locating impoverished environments is still a challenging process for researchers, and the availability of accurate information is still lacking. Now, in a new study, scientists from Stanford University propose a more reliable method to map poverty in areas previously void of data — by combining satellite images and making use of machine learning. Novel Method Led by Stanford computer science doctoral student Neal Jean, researchers sought to determine whether the combination of high-satellite imagery and machine learning — the science of designing algorithms that learn from data — could predict estimates of areas where impoverished people lived. Specifically, they extracted information about poverty from these…


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