# An unsupervised, open-source workflow for 2D and 3D building mapping from airborne LiDAR data

arXiv: https://arxiv.org/abs/2205.14585 · HTML: https://mlsanity.com/paper/2205.14585/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 90 references that are in the map.
- First posted on arXiv 29 May 2022.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data](https://mlsanity.com/paper/2104.07778/) (2021)
- [OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing](https://mlsanity.com/paper/2007.06277/) (2020)
- [Building Extraction at Scale using Convolutional Neural Network: Mapping of the United States](https://mlsanity.com/paper/1805.08946/) (2018)

## Built on by (most cited first, 2 of 2)

- [Scalable Surface Water Mapping up to Fine-scale using Geometric Features of Water from Topographic Airborne LiDAR Data](https://mlsanity.com/paper/2301.06567/) (2023)
- [Assessment of Local Climate Zone Products via Simplified Classification Rule with 3D Building Maps](https://mlsanity.com/paper/2309.15978/) (2023)

## Abstract (arXiv)

Despite the substantial demand for high-quality, large-area building maps, no established open-source workflow for generating 2D and 3D maps currently exists. This study introduces an automated, open-source workflow for large-scale 2D and 3D building mapping utilizing airborne LiDAR data. Uniquely, our workflow operates entirely unsupervised, eliminating the need for any training procedures. We have integrated a specifically tailored DTM generation algorithm into our workflow to prevent errors in complex urban landscapes, especially around highways and overpasses. Through fine rasterization of LiDAR point clouds, we've enhanced building-tree differentiation, reduced errors near water bodies, and augmented computational efficiency by introducing a new planarity calculation. Our workflow offers a practical and scalable solution for the mass production of rasterized 2D and 3D building maps from raw airborne LiDAR data. Also, we elaborate on the influence of parameters and potential error sources to provide users with practical guidance. Our method's robustness has been rigorously optimized and tested using an extensive dataset (> 550 km2), and further validated through comparison with deep learning-based and hand-digitized products. Notably, through these unparalleled, large-scale comparisons, we offer a valuable analysis of large-scale building maps generated via different methodologies, providing insightful evaluations of the effectiveness of each approach. We anticipate that our highly scalable building mapping workflow will facilitate the production of reliable 2D and 3D building maps, fostering advances in large-scale urban analysis. The code will be released upon publication.