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

arXiv: https://arxiv.org/abs/2505.18021 · HTML: https://mlsanity.com/paper/2505.18021/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 8 of 8)

- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlands](https://mlsanity.com/paper/2201.01191/) (2021)
- [Large-scale Building Height Retrieval from Single SAR Imagery based on Bounding Box Regression Networks](https://mlsanity.com/paper/2111.09460/) (2021)
- [CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images](https://mlsanity.com/paper/2011.08362/) (2020)
- [Instance segmentation of buildings using keypoints](https://mlsanity.com/paper/2006.03858/) (2020)
- [FloorLevel-Net: Recognizing Floor-Level Lines with Height-Attention-Guided Multi-task Learning](https://mlsanity.com/paper/2107.02462/) (2021)
- [DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience](https://mlsanity.com/paper/2306.06269/) (2023)
- [Towards Large-scale Building Attribute Mapping using Crowdsourced Images: Scene Text Recognition on Flickr and Problems to be Solved](https://mlsanity.com/paper/2309.08042/) (2023)

## Built on by (most cited first, 1 of 1)

- [GATA2Floor: Graph attention for floor counting in street-view facades](https://mlsanity.com/paper/2605.11863/) (2026)

## Abstract (arXiv)

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.