Accurate information on the number of building floors, or above-ground storeys, is essential for household estimation, utility provision, risk assessment, evacuation planning, and energy modeling. Yet large-scale floor-count data are rarely available in cadastral and 3D city databases. This study proposes an end-to-end deep learning framework that infers floor numbers directly from unrestricted, crowdsourced street-level imagery, avoiding hand-crafted features and generalizing across diverse facade styles. To enable benchmarking, we release the Munich Building Floor Dataset, a public set of over 6800 geo-tagged images collected from Mapillary and targeted field photography, each paired with a verified storey label. On this dataset, the proposed classification-regression network attains 81.2% exact accuracy and predicts 97.9% of buildings within +/-1 floor. The method and dataset together offer a scalable route to enrich 3D city models with vertical information and lay a foundation for future work in urban informatics, remote sensing, and geographic information science. Source code and data will be released under an open license at https://github.com/ya0-sun/Munich-SVI-Floor-Benchmark.
@article{arxiv.2505.18021,
title = {Building Floor Number Estimation from Crowdsourced Street-Level Images: Munich Dataset and Baseline Method},
author = {Yao Sun and Sining Chen and Yifan Tian and Xiao Xiang Zhu},
journal= {arXiv preprint arXiv:2505.18021},
year = {2025}
}
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Code and data: https://github.com/ya0-sun/Munich-SVI-Floor-Benchmark