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.

本研究探讨了生成人工智能在理解城市人类感知方面的能力，在通过筛选的文字和图像描述来捕捉城市地方认同感方面具有潜力，因此它可以为城市设计文献提供未来研究机会和潜在局限性。