4.7 Article

3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads

期刊

REMOTE SENSING
卷 12, 期 14, 页码 -

出版社

MDPI
DOI: 10.3390/rs12142301

关键词

mobile laser scanning; point cloud processing; infrastructure information models; building information modeling; Industry Foundation Classes; road alignment modeling

资金

  1. European Union's Horizon 2020 research and innovation programme [769255]
  2. Spanish Ministry of Science, Innovation and Universities through the LASTING project [RTI2018-095893-B-C21]
  3. Spanish Ministry of Science and Innovation [FJC2018-035550-I]

向作者/读者索取更多资源

Building information modeling (BIM) is a process that has shown great potential in the building industry, but it has not reached the same level of maturity for transportation infrastructure. There is a standardization need for information exchange and management processes in the infrastructure that integrates BIM and Geographic Information Systems (GIS). Currently, the Industry Foundation Classes standard has harmonized different infrastructures under the Industry Foundation Classes (IFC) 4.3 release. Furthermore, the usage of remote sensing technologies such as laser scanning for infrastructure monitoring is becoming more common. This paper presents a semi-automated framework that takes as input a raw point cloud from a mobile mapping system, and outputs an IFC-compliant file that models the alignment and the centreline of each road lane in a highway road. The point cloud processing methodology is validated for two of its key steps, namely road marking processing and alignment and road line extraction, and a UML diagram is designed for the definition of the alignment entity from the point cloud data.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据