  We develop a machine learning based tool for accurate prediction of
socio-economic indicators from daytime satellite imagery. The diverse set of
indicators are often not intuitively related to observable features in
satellite images, and are not even always well correlated with each other. Our
predictive tool is more accurate than using night light as a proxy, and can be
used to predict missing data, smooth out noise in surveys, monitor development
progress of a region, and flag potential anomalies. Finally, we use predicted
variables to do robustness analysis of a regression study of high rate of
stunting in India.
