Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery
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 rev...
| Main Authors: | , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Informa UK Limited
2024
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| Online Access: | https://hdl.handle.net/1721.1/156390 |
| _version_ | 1824457879089840128 |
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| author | Zhang, Fan Salazar-Miranda, Arianna Duarte, Fábio Vale, Lawrence Hack, Gary Chen, Min Liu, Yu Batty, Michael Ratti, Carlo |
| author2 | Senseable City Laboratory |
| author_facet | Senseable City Laboratory Zhang, Fan Salazar-Miranda, Arianna Duarte, Fábio Vale, Lawrence Hack, Gary Chen, Min Liu, Yu Batty, Michael Ratti, Carlo |
| author_sort | Zhang, Fan |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:18:27Z |
| format | Article |
| id | mit-1721.1/156390 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:17:00Z |
| publishDate | 2024 |
| publisher | Informa UK Limited |
| record_format | dspace |
| spelling | mit-1721.1/1563902024-12-23T06:23:15Z Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery Zhang, Fan Salazar-Miranda, Arianna Duarte, Fábio Vale, Lawrence Hack, Gary Chen, Min Liu, Yu Batty, Michael Ratti, Carlo Senseable City Laboratory Massachusetts Institute of Technology. Department of Urban Studies and Planning 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. 2024-08-23T20:46:39Z 2024-08-23T20:46:39Z 2024-05-27 2024-08-23T20:42:15Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/156390 Zhang, F., Salazar-Miranda, A., Duarte, F., Vale, L., Hack, G., Chen, M., … Ratti, C. (2024). Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery. Annals of the American Association of Geographers, 114(5), 876–897. en 10.1080/24694452.2024.2313515 Annals of the American Association of Geographers Creative Commons Attribution-NonCommercial-NoDerivs License https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Informa UK Limited Informa |
| spellingShingle | Zhang, Fan Salazar-Miranda, Arianna Duarte, Fábio Vale, Lawrence Hack, Gary Chen, Min Liu, Yu Batty, Michael Ratti, Carlo Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery |
| title | Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery |
| title_full | Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery |
| title_fullStr | Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery |
| title_full_unstemmed | Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery |
| title_short | Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery |
| title_sort | urban visual intelligence studying cities with artificial intelligence and street level imagery |
| url | https://hdl.handle.net/1721.1/156390 |
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