Zhang, Fan;
Salazar-Miranda, Arianna;
Duarte, Fábio;
Vale, Lawrence;
Hack, Gary;
Chen, Min;
Liu, Yu;
... Ratti, Carlo; + view all
(2024)
Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery.
Annals of the American Association of Geographers
10.1080/24694452.2024.2313515.
(In press).
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Abstract
The visual dimension of cities has been a fundamental subject in urban studies since the pioneering work of late-nineteenth- to mid-twentieth-century scholars such as Camillo Sitte, Kevin Lynch, Rudolf Arnheim, and Jane Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This article reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, urban visual intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with the socioeconomic environment at various scales. The article argues that these new approaches would allow researchers to revisit the classic urban theories and themes and potentially help cities create environments that align with human behaviors and aspirations in today’s AI-driven and data-centric era.
| Type: | Article |
|---|---|
| Title: | Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/24694452.2024.2313515 |
| Publisher version: | http://dx.doi.org/10.1080/24694452.2024.2313515 |
| Language: | English |
| Additional information: | © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10190521 |
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