Urban visual uniqueness: A landmark-free framework to quantify city's identity and distinctiveness from everyday scenes

The visual appearance of a city is shaped by a complex interplay of factors, including cultural backgrounds, geographical features, historical developments, and policy decisions. But measuring cities' visual uniqueness remains a challenge. Previous studies often focused on iconic landmarks, neglecting everyday scenes that people are likely to encounter. By examining how and to what extent different visual patterns build up unique characteristics of cities, we propose a data-driven framework to measure visual uniqueness in terms of identity and distinctiveness. We performed bottom-up visual clustering on Google Street View (GSV) images in the six most visited Japanese cities. We found that 8 representative visual clusters explain each city's visual identity and relative distinctiveness. This research demonstrates how artificial intelligence applied to visual data can reveal subtle differences in urban environments. In the era of growing globalization, with frequent tourism and intercity visits, the cultivation of a city's unique visual characteristics can help avoid the homogenization of urban landscapes, and stimulate the development of urban tourism by shaping an imageable city.