2026-07-21T20:16:27Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18936 2024-03-06T11:03:00Z ddc:004 open_access

Understanding Place Identity with Generative AI (Short Paper) Jang, Kee Moon Chen, Junda Kang, Yuhao Kim, Junghwan Lee, Jinhyung Duarte, Fábio ChatGPT DALL·E2 place identity generative artificial intelligence sense of place Researchers are constantly leveraging new forms of data to understand how people perceive the built environment and the collective place identity of cities. Latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations of real-world settings. In this study, we explore 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 a set of 31 global cities to two generative AI models, ChatGPT and DALL·E2. Since generative AI has raised ethical concerns regarding its trustworthiness, we performed cross-validation to examine whether the results show similar patterns to real urban settings. In particular, we compared the outputs with Wikipedia data for text and images searched from Google for images. Our results indicate that generative AI models have the potential to capture the collective features of cities that can make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in understanding human perceptions of the built environment. It contributes to urban design literature by discussing future research opportunities and potential limitations. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Kee Moon Jang and Junda Chen and Yuhao Kang and Junghwan Kim and Jinhyung Lee and Fábio Duarte 2023 Is Part Of LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2023.41 urn:nbn:de:0030-drops-189363 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.41 eng https://creativecommons.org/licenses/by/4.0/legalcode