Machine scans millions of satellite images to map poverty

“Basically, we provided the machine-learning system with daytime and nighttime satellite imagery and asked it to make predictions on poverty,” says Stefano Ermon, assistant professor of computer science. “The system essentially learned how to solve the problem by comparing those two sets of images.” (Credit: NASA) Stanford University rightOriginal Study Posted by Stanford on February 25, 2016 You are free to share this article under the Attribution 4.0 International license. One of the biggest challenges in fighting poverty is the lack of reliable information. In order to aid the poor, agencies need to map the dimensions of distressed areas and identify the absence or presence of infrastructure and services. But in many of the poorest areas of the world such information is rare. “There are very few data sets telling…


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