Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery
Name
Urban Visual Intelligence Studying Cities with Artificial Intelligence and Street-Level Imagery.pdf
Description
Published version
Size
1.59 MB
Format
Adobe PDF
Checksum (MD5)
aac5698c40ebf168163f013825bc9547
Author(s)
Zhang, Fan
Salazar-Miranda, Arianna
Duarte, Fábio
Vale, Lawrence
Hack, Gary
Chen, Min
Liu, Yu
Batty, Michael
Ratti, Carlo
Date Issued
May 27, 2024
Journal
Annals of the American Association of Geographers
Publisher
Informa UK Limited
Citation
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.
Version
Final published version
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.
MIT Department
Senseable City Laboratory
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/24694452.2024.2313515
