KAUST DepartmentComputer Science Program
Visual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2014-08-23
Print Publication Date2014-08
Permanent link to this recordhttp://hdl.handle.net/10754/575717
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AbstractUrban data ranging from images and laser scans to traffic flows are regularly analyzed and modeled leading to better scene understanding. Commonly used computational approaches focus on geometric descriptors, both for images and for laser scans. In contrast, in urban planning, a large body of work has qualitatively evaluated street networks to understand their effects on the functionality of cities, both for pedestrians and for cars. In this work, we analyze street networks, both their topology (i.e., connectivity) and their geometry (i.e., layout), in an attempt to understand which factors play dominant roles in determining the characteristic of cities. We propose a set of street network descriptors to capture the essence of city layouts and use them, in a supervised setting, to classify and categorize various cities across the world. We evaluate our method on a range of cities, of various styles, and demonstrate that while standard image-level descriptors perform poorly, the proposed network-level descriptors can distinguish between different cities reliably and with high accuracy. © 2014 The Eurographics Association and John Wiley & Sons Ltd.
SponsorsWe thank the reviewers for their comments and suggestions for improving the paper; Jun Wang for creating renderings in CityEngine; Han Liu for proofreading the paper. This work was supported in part by the Marie Curie Career Integration Grant 303541, the ERC Starting Grant SmartGeometry (StG-2013-335373), an Anita Borg Goggle PhD scholarship award, and gifts from Adobe Research.
JournalComputer Graphics Forum