  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.
