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Article

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

1
Department of Cartographic and Terrain Engineering, University of Salamanca, Calle Hornos Caleros 50, 05003 Avila, Spain
2
Universidade de Vigo. Centro de investigación en Tecnoloxías, Enerxía e Procesos Industriais (CINTECX). Applied Geotechnologies Research Group , Campus Universitario de Vigo, As Lagoas, Marcosende, 36310 Vigo, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2301; https://doi.org/10.3390/rs12142301
Received: 17 June 2020 / Revised: 3 July 2020 / Accepted: 16 July 2020 / Published: 17 July 2020
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. View Full-Text
Keywords: mobile laser scanning; point cloud processing; infrastructure information models; building information modeling; Industry Foundation Classes; road alignment modeling mobile laser scanning; point cloud processing; infrastructure information models; building information modeling; Industry Foundation Classes; road alignment modeling
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MDPI and ACS Style

Soilán, M.; Justo, A.; Sánchez-Rodríguez, A.; Riveiro, B. 3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads. Remote Sens. 2020, 12, 2301. https://doi.org/10.3390/rs12142301

AMA Style

Soilán M, Justo A, Sánchez-Rodríguez A, Riveiro B. 3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads. Remote Sensing. 2020; 12(14):2301. https://doi.org/10.3390/rs12142301

Chicago/Turabian Style

Soilán, Mario, Andrés Justo, Ana Sánchez-Rodríguez, and Belén Riveiro. 2020. "3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads" Remote Sensing 12, no. 14: 2301. https://doi.org/10.3390/rs12142301

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