Window-Level Semantic Enrichment of Texture-Mapped Building Models in Urban Digital Twins

This paper presents a computational pipeline for window-level semantic enrichment of texture-mapped building models used in urban digital twins (UDTs). The pipeline combines SAM-based candidate generation, super-resolution-based input matching (SRIM), a fine-tuned ResNet-50 window/non-window classifier, and texture-to-mesh mapping to instantiate verified regions as independent 3D window objects. Rather than proposing a new segmentation or super-resolution model, the study integrates existing components for low-resolution facade textures attached to 3D models. In experiments on 130 real building models, the EDSR-based SRIM configuration achieved the best mean accuracy of 95.0% and F1 score of 0.951 over 10 runs. An auxiliary experiment on cropped Open Images samples showed a consistent advantage of SRIM-based conditioning. A small-scale aspect-ratio-based evaluation of the generated 3D windows yielded an overall mean relative error of 14.93%, indicating suitability for semantic enrichment rather than precision-grade reconstruction. The method is relevant to downstream UDT applications such as facade editing, maintenance planning, and simulation-oriented model refinement.