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Urban Perception of Commercial Activeness from Satellite Images and Streetscapes

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Weishan ZhangAuthors Info & Claims

WWW '18: Companion Proceedings of the The Web Conference 2018

Pages 647 - 654

https://doi.org/10.1145/3184558.3186581

Published: 23 April 2018 Publication History

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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.

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Index Terms

Urban Perception of Commercial Activeness from Satellite Images and Streetscapes
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology

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cover image ACM Other conferences

WWW '18: Companion Proceedings of the The Web Conference 2018

April 2018

2023 pages

ISBN:9781450356404

General Chairs:
Pierre-Antoine Champin
Université Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, CNRS, LIRIS, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

Copyright © 2018 ACM.

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Published: 23 April 2018

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NSFC
Shanghai Science and Technology Commision

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WWW '18

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WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

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Yong XZhou XLarson K(2024)MuseCLProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/834(7536-7544)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/834
Xiao CZhou JXiao YHuang JXiong HBaeza-Yates RBonchi F(2024)ReFound: Crafting a Foundation Model for Urban Region Understanding upon Language and Visual FoundationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671992(3527-3538)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671992
Yan YWen HZhong SChen WChen HWen QZimmermann RLiang YChua TNgo CKa-Wei Lee RKumar RLauw H(2024)UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the WebProceedings of the ACM Web Conference 202410.1145/3589334.3645378(4006-4017)Online publication date: 13-May-2024
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