Paper Info
Title
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.
Year
DOI
Venue
2018
10.1145/3184558.3186581
WWW '18: The Web Conference 2018 Lyon France April, 2018
DocType
ISBN
Citations 
Conference
978-1-4503-5640-4
1
PageRank 
References 
Authors
0.39
0
6
Name
Order
Citations
PageRank
Wenshan Wang1249.00
Su Yang211014.58
Zhiyuan He3727.19
Minjie Wang421.75
Jiulong Zhang511.40
Weishan Zhang639652.57