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3D Point cloud to BIM: semi-automated framework to define IFC alignment entities from MLS-acquired LiDAR data of highway roads

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3D Point cloud to BIM: semi-automated framework to define IFC alignment entities from MLS-acquired LiDAR data of highway roads

Soilán Rodríguez, Mario; Justo Dominguez, Andrés; Sánchez Rodríguez, Ana; Riveiro Rodríguez, Belén
 
DATE : 2020-07-17
UNIVERSAL IDENTIFIER : http://hdl.handle.net/11093/1718
UNESCO SUBJECT : 3311.02 Ingeniería de Control ; 3305.06 Ingeniería Civil ; 3307.07 Dispositivos láser
DOCUMENT TYPE : article

ABSTRACT :

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 ... [+]
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. [-]

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