Urban Visual Appeal According to ChatGPT: Contrasting AI and Human Insights

Abstract

The visual appeal of urban environments significantly impacts residents' satisfaction with their living spaces and their overall mood, which in turn, affects their health and well-being. Given the resource-intensive nature of gathering evaluations on urban visual appeal through surveys or inquiries from residents, there is a constant quest for automated solutions to streamline this process and support spatial planning. In this study, we applied an off-the-shelf AI model to automate the analysis of urban visual appeal, using over 1,800 Google Street View images of Helsinki, Finland. By incorporating the GPT-4 model with specified criteria, we assessed these images. Simultaneously, 24 participants were asked to rate the images. Our results demonstrated a strong alignment between GPT-4 and participant ratings, although geographic disparities were noted. Specifically, GPT-4 showed a preference for suburban areas with significant greenery, contrasting with participants who found these areas less appealing. Conversely, in the city centre and densely populated urban regions of Helsinki, GPT-4 assigned lower visual appeal scores than participant ratings. While there was general agreement between AI and human assessments across various locations, GPT-4 struggled to incorporate contextual nuances into its ratings, unlike participants, who considered both context and features of the urban environment. The study suggests that leveraging AI models like GPT-4 allows spatial planners to gather insights into the visual appeal of different areas efficiently, aiding decisions that enhance residents' and travellers' satisfaction and mental health. Although AI models provide valuable insights, human perspectives are essential for a comprehensive understanding of urban visual appeal. This will ensure that planning and design decisions promote healthy living environments effectively.