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Understanding Place Identity with Generative AI (Short Paper)

Authors Kee Moon Jang , Junda Chen, Yuhao Kang , Junghwan Kim , Jinhyung Lee , Fábio Duarte

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Kee Moon Jang
  • MIT Senseable City Lab, Cambridge, MA, USA
Junda Chen
  • DataChat, Madison, WI, USA
Yuhao Kang
  • MIT Senseable City Lab, Cambridge, MA, USA
Junghwan Kim
  • Department of Geography, Virginia Tech, Blacksburg, VA, USA
Jinhyung Lee
  • Department of Geography and Environment, Western University, London, Canada
Fábio Duarte
  • MIT Senseable City Lab, Cambridge, MA, USA

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Kee Moon Jang, Junda Chen, Yuhao Kang, Junghwan Kim, Jinhyung Lee, and Fábio Duarte. Understanding Place Identity with Generative AI (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 41:1-41:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.GIScience.2023.41

Abstract

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.

Subject Classification

ACM Subject Classification
  • Social and professional topics → Geographic characteristics
Keywords
  • ChatGPT
  • DALL·E2
  • place identity
  • generative artificial intelligence
  • sense of place

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