Urban Perception of Commercial Activeness from Satellite Images and Streetscapes

Abstract

People can percept social attributes from streetscapes such as safety, richness, and happiness by means of visual perception, which inspires the research in terms of urban perception. To the best of our knowledge, this is the first work focused on revealing the relationship between visual patterns of satellite images as well as streetscapes and commercial activeness. We propose to make use of bag of features (BoF) in the context of computer vision and sparse representation in the sense of machine learning to predict commercial activeness of urban commercial districts. After obtaining the urban commercial districts via clustering, we predict the commercial activeness degrees of them using four image features, namely, Histogram of Oriented Gradients (HOG), Autoencoder, GIST, and multifractal spectra for satellite images and street view images, respectively. The performance evaluation with four large-scale datasets demonstrates that the presented computational framework can not only predict the commercial activeness with satisfactory precision compared with that based on Point of Interest (POI) data but also discover the visual patterns related.