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.

翻译：研究者不断借助新型数据形式，旨在理解人们如何感知建成环境并构建城市集体场所认同。生成式人工智能模型的最新进展，使其能够从海量数据中学习并生成逼真的表征。本研究旨在检验生成式AI作为文本与视觉信息源，在通过过滤后的描述与图像评估城市场所认同方面的潜力。我们向ChatGPT与DALL-E2两个生成式AI模型询问了31个全球城市的场所认同相关问题。鉴于生成式AI在可信度方面引发伦理争议，我们通过交叉验证考察其结果是否与现实城市环境呈现相似模式：具体将文本输出与维基百科数据对比，将图像输出与谷歌搜索图像对比。结果表明，生成式AI模型具备捕捉城市可辨识集体意象的潜力。作为探索生成式AI理解人类对建成环境感知能力的早期尝试，本研究通过探讨未来研究机遇与潜在局限性，为城市设计文献提供了新视角。