Satellite Images, Machine Learning Map Poverty

"The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we require more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide." Those are the opening words on the website of Stanford University's Sustainability and Artificial Intelligence Lab, and its researchers have come up with an unusual -- and effective -- way to map and predict the distribution of poverty; their method combines high-resolution satellite imagery with machine learning. The researchers explain their methodology, which they call "cheap and scalable," in a video. The study, titled "Combining satellite imagery and machine learning to predict poverty," was published in the journal Science.