Roads Detection and Parametrization in Integrated BIM-GIS Using LiDAR
Auteur(s): |
Luigi Barazzetti
Mattia Previtali Marco Scaioni |
---|---|
Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Infrastructures, juillet 2020, n. 7, v. 5 |
Page(s): | 55 |
DOI: | 10.3390/infrastructures5070055 |
Abstrait: |
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. |
Copyright: | © 2020 the Authors. Licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
6.92 MB
- Informations
sur cette fiche - Reference-ID
10723188 - Publié(e) le:
22.04.2023 - Modifié(e) le:
10.05.2023