Place identity: a generative AI’s perspective
Name
s41599-024-03645-7.pdf
Description
Published version
Size
3.72 MB
Format
Adobe PDF
Checksum (MD5)
acc90bcbfac27602c7980733d849c07d
Author(s)
Jang, Kee Moon
Chen, Junda
Kang, Yuhao
Kim, Junghwan
Lee, Jinhyung
Duarte, Fabio
Ratti, Carlo
Journal
Humanities and Social Sciences Communications
Publisher
Springer Science and Business Media LLC
Citation
Jang, K.M., Chen, J., Kang, Y. et al. Place identity: a generative AI’s perspective. Humanit Soc Sci Commun 11, 1156 (2024).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Senseable City Laboratory
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Creative Commons Attribution-NonCommercial-NoDerivatives
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DOI of Published Version
https://doi.org/10.1057/s41599-024-03645-7
