AUTOMATED CLASSIFICATION OF HERITAGE BUILDINGS FOR AS-BUILT BIM USING MACHINE LEARNING TECHNIQUES
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 proces...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-08-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W2/25/2017/isprs-annals-IV-2-W2-25-2017.pdf |
| Summary: | 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.<br><br>
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. |
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| ISSN: | 2194-9042 2194-9050 |