Building Floor Number Estimation from Crowdsourced Street-Level Images: Munich Dataset and Baseline Method

• URL: http://arxiv.org/abs/2505.18021v1
• Date: Fri, 23 May 2025 15:27:46 GMT
• Title: Building Floor Number Estimation from Crowdsourced Street-Level Images: Munich Dataset and Baseline Method
• Authors: Yao Sun, Sining Chen, Yifan Tian, Xiao Xiang Zhu,
• Abstract summary: Large-scale floor-count data are rarely available in cadastral and 3D city databases.<n>This study proposes an end-to-end deep learning framework that infers floor numbers directly from street-level imagery.<n>The proposed classification-regression network attains 81.2% exact accuracy and predicts 97.9% of buildings within +/-1 floor.
• Score: 17.492721759864505
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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