SHAPE BASED CLASSIFICATION OF SEISMIC BUILDING STRUCTURAL TYPES
This paper investigates automatic prediction of seismic building structural types described by the Global Earthquake Model (GEM) taxonomy, by combining remote sensing, cadastral and inspection data in a supervised machine learning approach. Our focus lies on the extraction of detailed geometric info...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-09-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W10/179/2018/isprs-archives-XLII-4-W10-179-2018.pdf |
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| author | R. Sulzer P. Nourian M. Palmieri J. C. van Gemert |
| author_facet | R. Sulzer P. Nourian M. Palmieri J. C. van Gemert |
| author_sort | R. Sulzer |
| collection | DOAJ |
| description | This paper investigates automatic prediction of seismic building structural types described by the Global Earthquake Model (GEM) taxonomy, by combining remote sensing, cadastral and inspection data in a supervised machine learning approach. Our focus lies on the extraction of detailed geometric information from a point cloud gained by aerial laser scanning. To describe the geometric shape of a building we apply Shape-DNA, a spectral shape descriptor based on the eigenvalues of the Laplace-Beltrami operator. In a first experiment on synthetically generated building stock we succeed in predicting the roof type of different buildings with accuracies above 80 %, only relying on the Shape-DNA. The roof type of a building thereby serves as an example of a relevant feature for predicting GEM attributes, which cannot easily be identified and described by using traditional methods for shape analysis of buildings. Further research is necessary in order to explore the usability of Shape-DNA on real building data. In a second experiment we use real-world data of buildings located in the Groningen region in the Netherlands. Here we can automatically predict six GEM attributes, such as the type of lateral load resisting system, with accuracies above 75 % only by taking a buildings footprint area and year of construction into account. |
| first_indexed | 2024-04-12T08:44:41Z |
| format | Article |
| id | doaj.art-11e19f6387944091a5149cb54df8ece4 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-12T08:44:41Z |
| publishDate | 2018-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-11e19f6387944091a5149cb54df8ece42022-12-22T03:39:45ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342018-09-01XLII-4-W1017918610.5194/isprs-archives-XLII-4-W10-179-2018SHAPE BASED CLASSIFICATION OF SEISMIC BUILDING STRUCTURAL TYPESR. Sulzer0P. Nourian1M. Palmieri2J. C. van Gemert3Delft University of Technology, the NetherlandsDelft University of Technology, the NetherlandsArup Amsterdam, the NetherlandsDelft University of Technology, the NetherlandsThis paper investigates automatic prediction of seismic building structural types described by the Global Earthquake Model (GEM) taxonomy, by combining remote sensing, cadastral and inspection data in a supervised machine learning approach. Our focus lies on the extraction of detailed geometric information from a point cloud gained by aerial laser scanning. To describe the geometric shape of a building we apply Shape-DNA, a spectral shape descriptor based on the eigenvalues of the Laplace-Beltrami operator. In a first experiment on synthetically generated building stock we succeed in predicting the roof type of different buildings with accuracies above 80 %, only relying on the Shape-DNA. The roof type of a building thereby serves as an example of a relevant feature for predicting GEM attributes, which cannot easily be identified and described by using traditional methods for shape analysis of buildings. Further research is necessary in order to explore the usability of Shape-DNA on real building data. In a second experiment we use real-world data of buildings located in the Groningen region in the Netherlands. Here we can automatically predict six GEM attributes, such as the type of lateral load resisting system, with accuracies above 75 % only by taking a buildings footprint area and year of construction into account.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W10/179/2018/isprs-archives-XLII-4-W10-179-2018.pdf |
| spellingShingle | R. Sulzer P. Nourian M. Palmieri J. C. van Gemert SHAPE BASED CLASSIFICATION OF SEISMIC BUILDING STRUCTURAL TYPES The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | SHAPE BASED CLASSIFICATION OF SEISMIC BUILDING STRUCTURAL TYPES |
| title_full | SHAPE BASED CLASSIFICATION OF SEISMIC BUILDING STRUCTURAL TYPES |
| title_fullStr | SHAPE BASED CLASSIFICATION OF SEISMIC BUILDING STRUCTURAL TYPES |
| title_full_unstemmed | SHAPE BASED CLASSIFICATION OF SEISMIC BUILDING STRUCTURAL TYPES |
| title_short | SHAPE BASED CLASSIFICATION OF SEISMIC BUILDING STRUCTURAL TYPES |
| title_sort | shape based classification of seismic building structural types |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W10/179/2018/isprs-archives-XLII-4-W10-179-2018.pdf |
| work_keys_str_mv | AT rsulzer shapebasedclassificationofseismicbuildingstructuraltypes AT pnourian shapebasedclassificationofseismicbuildingstructuraltypes AT mpalmieri shapebasedclassificationofseismicbuildingstructuraltypes AT jcvangemert shapebasedclassificationofseismicbuildingstructuraltypes |