Mapping urban change using streetview imagery and computer vision : case of Karaköy

Abstract

Traditional urban studies have relied on fieldwork and archival research. Yet, recent advances in AI and remote sensing provide scalable methods to monitor urban transformation. By com paring historical and recent Street View imagery, computer vision enables the systematic detec tion of spatial modifications. However, if not critically linked to socio-historical frameworks, such outputs risk becoming decontextualized. This study introduces the concepts of pings and signals within a mapping-based model that combines computational detection with contextual interpretation. Micro-scale interventions, such as demolitions, sign changes, or function shifts, are treated as pings. When accumulated over time, these pings evolve into signals that indicate broader shifts in land use and socio-economic dynamics. Karak¨ oy, Istanbul, is examined as a site where redevelopment and the growth of creative industries have reshaped the urban fabric. These processes have altered the area’s identity, accessibility, and spatial practices. The findings suggest that Google Street View, when used in conjunction with diachronic models, can effec tively support spatial analysis by capturing patterns of change. Nevertheless, this approach has several limitations. These include data biases, uneven temporal coverage, and limited qualita tive integration, all of which require careful and critical interpretation. Despite these challenges, street-level imagery remains a valuable source. This article demonstrates that the integration of computational and experiential data enables richer interpretations of urban change. In this con text, mapping emerges as a relational practice that links physical traces to broader contextual narratives.