  Millions of people worldwide are absent from their country's census.
Accurate, current, and granular population metrics are critical to improving
government allocation of resources, to measuring disease control, to responding
to natural disasters, and to studying any aspect of human life in these
communities. Satellite imagery can provide sufficient information to build a
population map without the cost and time of a government census. We present two
Convolutional Neural Network (CNN) architectures which efficiently and
effectively combine satellite imagery inputs from multiple sources to
accurately predict the population density of a region. In this paper, we use
satellite imagery from rural villages in India and population labels from the
2011 SECC census. Our best model achieves better performance than previous
papers as well as LandScan, a community standard for global population
distribution.
