Place identity: a generative AI’s perspective

Kee Moon Jang, Junda Chen, Yuhao Kang (), Junghwan Kim, Jinhyung Lee, Fabio Duarte and Carlo Ratti
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Kee Moon Jang: Massachusetts Institute of Technology
Junda Chen: University of California San Diego
Yuhao Kang: Massachusetts Institute of Technology
Junghwan Kim: Virginia Tech
Jinhyung Lee: Western University
Fabio Duarte: Massachusetts Institute of Technology
Carlo Ratti: Massachusetts Institute of Technology

Palgrave Communications, 2024, vol. 11, issue 1, 1-16

Abstract: Abstract Do cities have a collective identity? The latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations learned from vast amounts of data. In this study, we test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of 64 global cities to two generative AI models, ChatGPT and DALL·E2. Furthermore, given the ethical concerns surrounding the trustworthiness of generative AI, we examined whether the results were consistent with real urban settings. In particular, we measured similarity between text and image outputs with Wikipedia data and images searched from Google, respectively, and compared across cases to identify how unique the generated outputs were for each city. Our results indicate that generative models have the potential to capture the salient characteristics of cities that make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in simulating the built environment in regard to place-specific meanings. It contributes to urban design and geography literature by fostering research opportunities with generative AI and discussing potential limitations for future studies.

Date: 2024
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DOI: 10.1057/s41599-024-03645-7

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