Understanding Place Identity with Generative AI
Kee Moon Jang Junda Chen Yuhao Kang Junghwan Kim Jinhyung Lee Fábio Duarte
Researchers are constantly leveraging new forms of data with the goal of
understanding how people perceive the built environment and build the
collective place identity of cities. Latest advancements in generative
artificial intelligence (AI) models have enabled the production of realistic
representations learned from vast amounts of data. In this study, we aim to
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 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 image. Our results indicate
that generative AI models have the potential to capture the collective image 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.