原文链接：
https://link.springer.com/article/10.1007/s40789-023-00645-x或点击文末“阅读原文”
具有人造工程结构的采矿工业区是现实世界最显著的特征之一。现实世界的三维模型在数字孪生和智能工厂管理等众多应用中越来越受欢迎。本研究基于CityGML标准建立了采矿工程结构的三维模型。在收集空间数据时，采用了两种最流行的地理空间技术，即UAV-SfM和TLS。无人机测量的精度达到了厘米级，满足了根据CityGML标准创建所有详细程度（LoD）的绝对定位精度要求。因此，UAV-SfM点云数据集被用于建立LoD2模型。此外，UAV-SfM和TLS外墙和屋顶子云之间的比较表明，这些对象的UAV-SfM和TLS点云高度一致，因此, UAV-SfM和TLS整合后提供的点云具有更高的细节和精度水平，可用于建立LoD3模型。生成的三维CityGML模型包括LoD2的39栋建筑物，以及LoD3的两座矿井，其中包括提升机、顶架和滚轮。
Abstract Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world. 3D models of the real world have been increasingly popular with numerous applications, such as digital twins and smart factory management. In this study, 3D models of mining engineering structures were built based on the CityGML standard. For collecting spatial data, the two most popular geospatial technologies, namely UAV-SfM and TLS were employed. The accuracy of the UAV survey was at the centimeter level, and it satisfied the absolute positional accuracy requirement of creating all levels of detail (LoD) according to the CityGML standard. Therefore, the UAV-SfM point cloud dataset was used to build LoD 2 models. In addition, the comparison between the UAV-SfM and TLS sub-clouds of facades and roofs indicates that the UAV-SfM and TLS point clouds of these objects are highly consistent, therefore, point clouds with a higher level of detail and accuracy provided by the integration of UAV-SfM and TLS were used to build LoD 3 models. The resulting 3D CityGML models include 39 buildings at LoD 2, and two mine shafts with hoistrooms, headframes, and sheave wheels at LoD 3.
Fig. 1 Study area: the green boundary lines for UAV surveys and the red boundary circle for TLS surveys
Fig. 2 Workflow of building 3D models for mine structures using UAV-SfM and TLS
Fig. 4 C2C distance maps and histograms of roofs (a–c) and facades (d–g)
引用格式：Van, C.L., Cao, C.X., Nguyen, A.N. et al. Building 3D CityGML models of mining industrial structures using integrated UAV and TLS point clouds. Int J Coal Sci Technol 10, 69 (2023). https://doi.org/10.1007/s40789-023-00645-x
