Urban Visual Intelligence: Studying Cities with AI and Street-level Imagery

TL;DR

This paper reviews how AI and street-level imagery are transforming urban studies by providing new ways to analyze city appearance and function, enabling a deeper understanding of physical and socioeconomic interactions.

Contribution

It introduces the concept of Urban Visual Intelligence, a framework for leveraging new image data and AI techniques to study cities systematically.

Findings

AI and street imagery enable new urban analysis methods

The framework revisits classic urban theories with modern data

Potential to align urban environments with human behaviors

Abstract

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.