An Unsupervised, Open-Source Workflow for 2D and 3D Building Mapping From Airborne LiDAR Data

被引:2
作者
Song, Hunsoo [1 ]
Jung, Jinha [1 ]
机构
[1] Purdue Univ, Lyles Sch Civil Engn, W Lafayette, IN 47907 USA
关键词
Buildings; Three-dimensional displays; Laser radar; Vegetation; Point cloud compression; Remote sensing; Optical sensors; 3D building; airborne laser scanning (ALS); building mapping; open-source; unsupervised; DOMAIN ADAPTATION; OBJECT DETECTION; CLASSIFICATION; EXTRACTION; POINT; RECONSTRUCTION; GENERATION; BENCHMARK; MODELS; IMAGES;
D O I
10.1109/JSTARS.2023.3329773
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This study introduces an automated, open-source workflow for large-scale 2D and 3D building mapping using airborne LiDAR data. Uniquely, our workflow operates entirely unsupervised, eliminating the need for any training procedures. We have integrated a specially tailored digital terrain model 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 have enhanced building-tree differentiation. In addition, we have 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. Our method's robustness has been rigorously validated across a diverse dataset in comparison with deep learning-based and hand-digitized products. Through these extensive comparisons, we provide a valuable analysis of building maps generated via different methodologies. 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.
引用
收藏
页码:6067 / 6084
页数:18
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