What makes london work like london?

Sawsan Alhalawani, Yong Liang Yang, Peter Wonka, Niloy J. Mitra

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Urban 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.

Original languageEnglish (US)
Pages (from-to)157-165
Number of pages9
JournalComputer Graphics Forum
Volume33
Issue number5
DOIs
StatePublished - Aug 2014

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ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

Cite this

Alhalawani, S., Yang, Y. L., Wonka, P., & Mitra, N. J. (2014). What makes london work like london? Computer Graphics Forum, 33(5), 157-165. https://doi.org/10.1111/cgf.12441