Satellite imagery has long been an attractive data source that provides a
wealth of information on human-inhabited areas. While super resolution
satellite images are rapidly becoming available, little study has focused on
how to extract meaningful information about human habitation patterns and
economic scales from such data. We present READ, a new approach for obtaining
essential spatial representation for any given district from high-resolution
satellite imagery based on deep neural networks. Our method combines transfer
learning and embedded statistics to efficiently learn critical spatial
characteristics of arbitrary size areas and represent them into a fixed-length
vector with minimal information loss. Even with a small set of labels, READ can
distinguish subtle differences between rural and urban areas and infer the
degree of urbanization. An extensive evaluation demonstrates the model
outperforms the state-of-the-art in predicting economic scales, such as
population density for South Korea (R^2=0.9617), and shows a high potential use
for developing countries where district-level economic scales are not known.

提出一种名为 READ 的新方法，利用深度神经网络从高分辨率卫星图像中获取任何给定区域的基本空间表示，结合转移学习和嵌入式统计学习显著提取尺寸任意的地区的重要空间特征，在小标签集的情况下，可区分城乡地区的微小差异，预测经济规模且表现最优，为开发中国家提供了潜在利用价值。