An Unsupervised Deep Learning Approach for Satellite Image Analysis with Applications in Demographic Analysis

High resolution satellite imagery is a growing source of data with potential applications in many diverse domains. Efficient large scale analysis of this rich data can lead to unprecedented discoveries with societal impact. We present a new framework for organizing collections of satellite images into demographically relevant categories using unsupervised learning techniques. Our framework first extracts features using pre-trained Convolutional Neural Networks from tiles of high resolution satellite images of a city. The k-means algorithm is then applied to these features to organize images into visually similar groups. The resulting clustered images are validated using demographic data. The cluster model is then applied to six different cities around the world to test the transferability of our methods. Finally, the discovered image clusters are visualized in our customized web interface to enable demographers, social scientists, and economists to understand the organization of a city.