Scientists turn to artificial intelligence to map poverty

Stanford scientists have found a low-cost method to map poverty in areas previously devoid of data, by combining satellite images and making use of machine learning. These improved poverty maps could help organisations and policymakers distribute funds more efficiently and evaluate policies, researchers said. 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 in African countries. Aid groups and other international organisations often fill in the gaps with door-to-door surveys, but these can be expensive and time-consuming to conduct. Researchers at Stanford University in the U.S. used machine learning — the science of designing computer algorithms that learn from data —…

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