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。

卫星图像轻量级和稳健的经济尺度表征