Stanford is Using Machine Learning on Satellite Images to Predict Poverty

Eliminating poverty is the number one goal of most countries around the world. However, the process of going around rural areas and manually tracking census data is time consuming, labor intensive and expensive. Considering that, a group of researchers at Stanford have pioneered an approach that combines machine learning with satellite images to make predicting poverty quicker, easier and less expensive. Using this machine learning algorithm, the model is able to predict per capita consumption expenditure of a particular location when provided with it's satellite images. The algorithm runs through millions of images of rural regions throughout the world. It then compares the presence of light in a region during the day and at night to predict it's economic activity.