Perceiving Commerial Activeness over Satellite Images

Abstract

Different urban regions usually have different commercial hotness due to the different social contexts inside. As satellite imagery promises high-resolution, low-cost, real-time, and ubiquitous data acquisition, this study aims to solve commercial hotness prediction as well as the correlated social contexts mining problem via visual pattern analysis on satellite images. The goal is to reveal the underlying law correlating visual patterns of satellite images with commercial hotness so as to infer the commercial hotness map of a whole city for government regulation and business planning. We propose a novel deep learning-based model, which learns semantic information from raw satellite images to enable predicting regional commercial hotness. First, we collect satellite images from Google Map and label such images with POI categories according to the annotations from OpenStreetMap. Then, we train a model of deep convolutional networks that leverage raw images to infer the social attributes of the region of interest. Finally, we use three classical regression methods to predict regional commercial hotness from the corresponding social contexts reflected in satellite images in Shanghai, where the applied deep features are learned from the examples of Beijing to guarantee the generality. The result shows that the proposed model is robust enough to reach 82% precision at average. To the best of our knowledge, it is the first work focused on discovering relations between commercial hotness and satellite images. A web service is developed to demonstrate how business planning can be done in reference to the predicted commercial hotness of a given region.