Urban digitalization requires scalable and interoperable methods for accurate building modelling. Traditional approaches to facade extraction and 3D reconstruction often lack seamless generalisation and adaptability, as well as integration with geospatial systems. This work presents an automated pipeline that transforms street-level imagery into georeferenced 3D building models by combining deep learning with texture-based analysis. The pipeline uses window annotations for visual structure detection and integrates geographic information system (GIS) building footprints from OpenStreetMap (OSM) to ensure spatial alignment. Unlike prior approaches that rely on multi-element semantic annotations, our method reduces labeling costs while preserving geometric accuracy. The window detection model achieves a mean Average Precision of 0.94 at 50% IoU threshold (mAP@50), indicating strong detection performance suitable for large-scale 3D modelling tasks. Our automated pipeline—window detection, texture analysis, GIS alignment, and Unity export—supports generation of georeferenced 3D building models at city scale.