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.

使用多种卫星图像输入的两个卷积神经网络结构有效地结合了来自印度乡村地区的卫星图像和2011年人口普查的人口标签，以准确预测一区域的人口密度，性能优于以往研究以及全球人口分布的标准LandScan。

利用卫星图像进行的深度学习方法：印度农村缺失人口测绘