Urban Visual Intelligence: Studying Cities with AI and Street-level Imagery
This work addresses the problem of understanding and improving urban environments for researchers and policymakers, though it is incremental as it builds on existing data and AI techniques.
The paper introduces Urban Visual Intelligence, a conceptual framework that uses AI and street-level imagery to study cities, enabling researchers to revisit classic urban theories and measure physical and socioeconomic environments at various scales.
The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age.