Abstract
Building Information Modeling (BIM) has a crucial role in smart road applications, not only limited to the design and construction stages, but also to traffic monitoring, autonomous vehicle navigation, road condition assessment, and real-time data delivery to drivers, among others. Point clouds collected through LiDAR are a powerful solution to capture as-built conditions, notwithstanding the lack of commercial tools able to automatically reconstruct road geometry in a BIM environment. This paper illustrates a two-step procedure in which roads are automatically detected and classified, providing GIS layers with basic road geometry that are turned into parametric BIM objects. The proposed system is an integrated BIM-GIS with a structure based on multiple proposals, in which a single project file can handle different versions of the model using a variable level of detail. The model is also refined by adding parametric elements for buildings and vegetation. Input data for the integrated BIM-GIS can also be existing cartographic layers or outputs generated with algorithms able to handle LiDAR data. This makes the generation of the BIM-GIS more flexible and not limited to the use of specific algorithms for point cloud processing.
Highlights
Smart roads can be defined as roads coupled with digital information able to provide capabilities for advanced applications such as traffic monitoring, real-time data delivery to drivers, improved safety conditions, or support to the development of connected and autonomous vehicles (CAVs), among others [1,2,3,4,5,6]
We introduce a method for road detection and extraction from Light Detection And Ranging (LiDAR) point clouds, which is the input in the workflow proposed in the paper
Parametric 3D modeling based on Building Information Modeling (BIM) at the scale of infrastructures is becoming more important in a huge variety of applications, opening the use of BIM outside the architecture and construction industry
Summary
Smart roads can be defined as roads coupled with digital information able to provide capabilities for advanced applications such as traffic monitoring, real-time data delivery to drivers, improved safety conditions, or support to the development of connected and autonomous vehicles (CAVs), among others [1,2,3,4,5,6]. Smart road projects can be related to BIM, but they require multiple data sources coming from different domains, among which Geographic Information (GI) and digital surveying technologies. The model of the infrastructure is registered in a mapping reference system through georeferenced geospatial data such as digital terrain/surface models (DTM/DSM), orthophotos, BIM-GIS is, flexible. It just relies on the availability of such data, and it is independent of the procedure used for their generation. Highways InfrastrEunctgulraens d20®2h0,a5s, 5la5unched a Digital Component Library (DCL) for highways projects, which contains of 28 more than 80 road objects. Other algorithms for road extraction using LiDAR can be used to geInnferarstarutcetutrhese20i2n0,p5,ux tFOfRorPEtEhReREinVItEeWgrated BIM-GIS environment
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