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.
@article{arxiv.1912.08197,
title = {Lightweight and Robust Representation of Economic Scales from Satellite Imagery},
author = {Sungwon Han and Donghyun Ahn and Hyunji Cha and Jeasurk Yang and Sungwon Park and Meeyoung Cha},
journal= {arXiv preprint arXiv:1912.08197},
year = {2019}
}