  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.
