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 km$^2$), 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.

翻译：尽管对高质量大区域建筑地图的需求巨大，但目前尚无成熟的用于生成2D与3D地图的开源工作流程。本研究提出了一种利用机载LiDAR数据进行大规模2D与3D建筑制图的自动化开源工作流程。该流程的独特之处在于完全无监督运行，无需任何训练过程。我们集成了专门定制的DTM生成算法，以避免在复杂城市场景（尤其是高速公路和立交桥附近）中出现错误。通过LiDAR点云的精细栅格化处理，我们增强了建筑物与树木的区分能力，减少了水域附近的误差，并通过引入新的平面性计算方法提升了计算效率。该工作流程为从原始机载LiDAR数据规模化生产栅格化2D与3D建筑地图提供了实用且可扩展的解决方案。此外，我们详细阐述了参数影响及潜在误差来源，以提供实践指导。方法的鲁棒性已通过大规模数据集（>550 km²）进行严格优化与测试，并通过与深度学习方法及人工数字化产品的对比进一步验证。值得注意的是，通过这些前所未有的大规模比较，我们为基于不同方法论生成的大规模建筑地图提供了有价值的分析，并对每种方法的有效性进行了深入评估。我们预期，这一高度可扩展的建筑制图工作流程将有助于生成可靠的2D与3D建筑地图，推动大规模城市分析领域的发展。代码将在论文发表后公开。