New Research Combines Machine Learning And Satellite Data To Map Out Poverty

In what could prove to be a real breakthrough for impoverished regions around the world, scientists from Stanford University have proposed an accurate way to identify poverty in areas previously void of valuable survey information. This was made possible through machine learning, extracting information about poverty from high-resolution satellite imagery. These improved poverty maps could certainly help aid organizations and policymakers distribute funds more efficiently and enact policies more effectively. ​ The researchers built upon their knowledge of few places in the world where one can tell the computer with certainty whether the people living there are rich or poor. They used the “nightlight” data to identify features in the higher-resolution daytime imagery that are correlated with economic development since the areas that are brighter at night are usually more…


Link to Full Article: New Research Combines Machine Learning And Satellite Data To Map Out Poverty