Volume IV-2/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W2, 25-30, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W2-25-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W2, 25-30, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W2-25-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  16 Aug 2017

16 Aug 2017

AUTOMATED CLASSIFICATION OF HERITAGE BUILDINGS FOR AS-BUILT BIM USING MACHINE LEARNING TECHNIQUES

M. Bassier, M. Vergauwen, and B. Van Genechten M. Bassier et al.
  • Dept. of Civil Engineering, TC Construction - Geomatics KU Leuven - Faculty of Engineering Technology Ghent, Belgium

Keywords: Scan-to-BIM, Classification, Digital Heritage, As-built BIM, Laser Scanning

Abstract. Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects.

In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.