Machine learning algorithm taps satellite imagery to better map impoverished regions

According to the World Bank Group, almost 900 million people around the world live on less than US$1.90 a day. Although such an enormous global issue, there’s actually not that much concrete data available on the exact location of the world’s impoverished zones. Stanford University researchers have come up with a method that could start to fill in some of the blanks, feeding satellite imagery into a machine learning algorithm to identify poverty-stricken areas across the African continent. For many impoverished regions around the world, such as parts of Africa, there is only very limited, local-level information available on poverty, collected via on-the-ground surveys. While this method is time-consuming, expensive and results in limited data, high-resolution satellite imagery of such regions is collected almost constantly and possibly presents a more…


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