用街景图自动估建筑层数，数据集+模型开源。
Building Floor Number Estimation from Crowdsourced Street-Level Images: Munich Dataset and Baseline Method
- 直接从街景图端到端预测楼层，无需人工特征
- 准确率81.2%，97.9%预测误差在±1层内
- 适合城市建模、遥感与地理信息研究者
建筑层数信息对住户估算、资源分配、风险评估、疏散规划和能源建模至关重要，但大范围的楼层数数据在地籍和三维城市数据库中极为稀缺。本研究提出一种端到端深度学习框架，直接从非受限的众包街景图像中推断楼层数量，避免手工特征并适应多样立面风格。为支持基准测试，我们发布了慕尼黑建筑层数数据集（Munich Building Floor Dataset），包含6800多张来自Mapillary及实地拍摄的地理标记图像，每张均配有验证过的楼层数标签。在该数据集上，所提分类-回归网络实现81.2%的精确准确率，97.9%的建筑预测误差在±1层以内。该方法与数据集共同提供了一种可扩展的途径，用于向三维城市模型补充垂直信息，并为城市信息学、遥感与地理信息科学的后续研究奠定基础。源代码与数据将通过开源许可在https://github.com/ya0-sun/Munich-SVI-Floor-Benchmark发布。
原文摘要 · Abstract (English)
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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