How satellite images and machine learning are helping identify global poverty
Besides streaming down signals to help smartphones map driving routes, or televisions deliver programs, data from some of the thousands of satellites orbiting Earth are now helping track things like crop conditions on rural farms, illegal deforestation, and increasingly, poverty in the hard-to-reach places around the globe.But as much as that data has the potential to provide invaluable information to humanitarian organizations, watchdog groups and policymakers, there is too much of it to sift through in order to draw insights that could influence important decisions. A team of researchers from Stanford University says it has developed an efficient way. By creating a deep-learning algorithm that can recognize signs of poverty in satellite images – such as condition of roads – the team could sort through a million images to accurately…
Link to Full Article: How satellite images and machine learning are helping identify global poverty
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