Mapping the Semantics of the Street: A VLM-Driven Geospatial Analysis in Fatih, Istanbul

Abstract

Investigating the physical and functional character of urban streetscapes requires fine-grained, scalable analysis of visual information. This study deploys Vision-Language Models (VLMs) over a comprehensive street-level image repository across the historical district of Fatih, Istanbul. We present a methodology for extracting multi-attribute urban semantics, evaluating visual decay, and mapping environmental vibrancy, contributing foundational datasets for urban planning and GeoAI applications.

Yunus Serhat Bıçakçı

Assistant Professor

Assistant Professor specializing in GeoAI, Multimodal Vision-Language Models, and Spatial Data Science.