Scientists using machine learning and satellite imagery to map poverty

Locating people living in poverty, such as through door-to-door surveys, sometimes is difficult. Therefore, scientists are now turning to satellite images. In a study published on Thursday in the US journal Science, researchers from the Stanford University used machine learning — the science of designing computer algorithms that learn from data — to extract information about poverty from high-resolution satellite imagery, Xinhua news agency reported. They found the newly developed approach was able to “make fairly accurate predictions” of impoverished areas across five African countries: Nigeria, Tanzania, Uganda, Malawi, and Rwanda. “Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries,” the researchers said in their paper. According to World Bank data from 2000 to 2010, 39 out of 59 African countries conducted less…


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