# Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation

arXiv: https://arxiv.org/abs/2307.02574 · HTML: https://mlsanity.com/paper/2307.02574/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability](https://mlsanity.com/paper/2012.14349/) (2020)
- [CBHE: Corner-based Building Height Estimation for Complex Street Scene Images](https://mlsanity.com/paper/1904.11128/) (2019)

## Built on by (most cited first, 6 of 6)

- [Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN](https://mlsanity.com/paper/2408.06761/) (2024)
- [CMAB: A First National-Scale Multi-Attribute Building Dataset in China Derived from Open Source Data and GeoAI](https://mlsanity.com/paper/2408.05891/) (2024)
- [Fine-Grained Building Function Recognition from Street-View Images via Geometry-Aware Semi-Supervised Learning](https://mlsanity.com/paper/2408.09460/) (2024)
- [Assessing Building Heat Resilience Using UAV and Street-View Imagery with Coupled Global Context Vision Transformer](https://mlsanity.com/paper/2601.11357/) (2026)
- [GATA2Floor: Graph attention for floor counting in street-view facades](https://mlsanity.com/paper/2605.11863/) (2026)
- [Multi-Modal Building Inspection via Perceiver IO Fusion of Satellite and Street-Level Imagery](https://mlsanity.com/paper/2605.26381/) (2026)

## Abstract (arXiv)

Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which is competitive to state-of-the-art approaches. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data, with possibilities in even regions and areas with diverse data quality and availability.