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Journal Article
Window-Level Semantic Enrichment of Texture-Mapped Building Models in Urban Digital Twins
- Authors
- Ahyun Lee, Sungpil Woo, Siyeon Park, Ji Sang Park, Sooyoung Jang
- Issue Date
- 2026-09
- Citation
- Applied Sciences (Switzerland), v.16, no.17, pp.1-18
- ISSN
- 2076-3417
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Language
- English
- Type
- Journal Article
- Abstract
- Abstract 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.
- Keyword
- computational modeling, urban digital twin, semantic enrichment, window extraction, texture-to-mesh mapping, super resolution, deep learning
- KSP Keywords
- 3D Model, Building model, Candidate generation, Computational pipeline, Digital Twin, Input matching, Low-resolution(LR), Mean relative error(MRE), Mesh mapping, Ratio-based, Semantic enrichment
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
