Place identity: a generative AI’s perspective
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 informat...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2025
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| Online Access: | https://hdl.handle.net/1721.1/158146 |
| _version_ | 1824458151437533184 |
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| author | Jang, Kee Moon Chen, Junda Kang, Yuhao Kim, Junghwan Lee, Jinhyung Duarte, Fabio Ratti, Carlo |
| author2 | Massachusetts Institute of Technology. Department of Urban Studies and Planning |
| author_facet | Massachusetts Institute of Technology. Department of Urban Studies and Planning Jang, Kee Moon Chen, Junda Kang, Yuhao Kim, Junghwan Lee, Jinhyung Duarte, Fabio Ratti, Carlo |
| author_sort | Jang, Kee Moon |
| collection | MIT |
| description | 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. |
| first_indexed | 2025-02-19T04:21:20Z |
| format | Article |
| id | mit-1721.1/158146 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:21:20Z |
| publishDate | 2025 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1581462025-01-31T19:11:40Z Place identity: a generative AI’s perspective Jang, Kee Moon Chen, Junda Kang, Yuhao Kim, Junghwan Lee, Jinhyung Duarte, Fabio Ratti, Carlo Massachusetts Institute of Technology. Department of Urban Studies and Planning Senseable City Laboratory 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. 2025-01-31T19:11:38Z 2025-01-31T19:11:38Z 2025-01-31T18:57:46Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/158146 Jang, K.M., Chen, J., Kang, Y. et al. Place identity: a generative AI’s perspective. Humanit Soc Sci Commun 11, 1156 (2024). en 10.1057/s41599-024-03645-7 Humanities and Social Sciences Communications Creative Commons Attribution-NonCommercial-NoDerivatives https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Springer Science and Business Media LLC Springer Science and Business Media LLC |
| spellingShingle | Jang, Kee Moon Chen, Junda Kang, Yuhao Kim, Junghwan Lee, Jinhyung Duarte, Fabio Ratti, Carlo Place identity: a generative AI’s perspective |
| title | Place identity: a generative AI’s perspective |
| title_full | Place identity: a generative AI’s perspective |
| title_fullStr | Place identity: a generative AI’s perspective |
| title_full_unstemmed | Place identity: a generative AI’s perspective |
| title_short | Place identity: a generative AI’s perspective |
| title_sort | place identity a generative ai s perspective |
| url | https://hdl.handle.net/1721.1/158146 |
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