Urban Visual Intelligence: Studying Cities with Artificial Intelligence and Street-Level Imagery

Abstract

The visual dimension of cities has been a fundamental subject in urban studies since the pioneering work of late-nineteenth- to mid-twentieth-century scholars such as Camillo Sitte, Kevin Lynch, Rudolf Arnheim, and Jane Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This article 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 the socioeconomic environment at various scales. The article argues that these new approaches would allow researchers to revisit the classic urban theories and themes and potentially help cities create environments that align with human behaviors and aspirations in today’s AI-driven and data-centric era.