Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery
This provides accurate population maps for resource allocation and disaster response in rural India, but is incremental as it builds on existing deep learning approaches.
The paper tackled the problem of missing population data in rural India by developing two CNN architectures that combine satellite imagery to predict population density, achieving better performance than previous methods and the LandScan standard.
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.