Machine learning-based segmentation of aerial LiDAR point cloud data on building roof
ABSTRACTThree-dimensional (3D) reconstruction of a building can be facilitated by correctly segmenting different feature points (e.g. in the form of boundary, fold edge, and planar points) over the building roof, and then, establishing relationships among the constructed feature lines and planar pat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2023.2210745 |
| _version_ | 1797829363660488704 |
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| author | Emon Kumar Dey Mohammad Awrangjeb Fayez Tarsha Kurdi Bela Stantic |
| author_facet | Emon Kumar Dey Mohammad Awrangjeb Fayez Tarsha Kurdi Bela Stantic |
| author_sort | Emon Kumar Dey |
| collection | DOAJ |
| description | ABSTRACTThree-dimensional (3D) reconstruction of a building can be facilitated by correctly segmenting different feature points (e.g. in the form of boundary, fold edge, and planar points) over the building roof, and then, establishing relationships among the constructed feature lines and planar patches using the segmented points. Present machine learning-based segmentation approaches of Light Detection and Ranging (LiDAR) point cloud data are confined only to different object classes or semantic labelling. In the context of fine-grained feature point classification over the extracted building roof, machine learning approaches have not yet been explored. In this paper, after generating the ground truth data for the extracted building roofs from three different datasets, we apply machine learning methods to segment the roof point cloud based on seven different effective geometric features. The goal is not to semantically enhance the point cloud, but rather to facilitate the application of 3D building reconstruction algorithms, making them easier to use. The calculated F1-scores for each class confirm the competitive performances over the state-of-the-art techniques, which are more than 95% almost in each area of the used datasets. |
| first_indexed | 2024-04-09T13:19:16Z |
| format | Article |
| id | doaj.art-c900ad33764a4c27a4bf84525d3ace4a |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-04-09T13:19:16Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-c900ad33764a4c27a4bf84525d3ace4a2023-05-11T10:02:50ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542023-12-0156110.1080/22797254.2023.2210745Machine learning-based segmentation of aerial LiDAR point cloud data on building roofEmon Kumar Dey0Mohammad Awrangjeb1Fayez Tarsha Kurdi2Bela Stantic3School of Information and Communication Technology, Griffith University, Nathan, QLD, AustraliaSchool of Information and Communication Technology, Griffith University, Nathan, QLD, AustraliaSchool of Civil Engineering & Surveying, Faculty of Health, Engineering and Sciences, University of Southern Queensland, Springfield, QLD, AustraliaSchool of Information and Communication Technology, Griffith University, Nathan, QLD, AustraliaABSTRACTThree-dimensional (3D) reconstruction of a building can be facilitated by correctly segmenting different feature points (e.g. in the form of boundary, fold edge, and planar points) over the building roof, and then, establishing relationships among the constructed feature lines and planar patches using the segmented points. Present machine learning-based segmentation approaches of Light Detection and Ranging (LiDAR) point cloud data are confined only to different object classes or semantic labelling. In the context of fine-grained feature point classification over the extracted building roof, machine learning approaches have not yet been explored. In this paper, after generating the ground truth data for the extracted building roofs from three different datasets, we apply machine learning methods to segment the roof point cloud based on seven different effective geometric features. The goal is not to semantically enhance the point cloud, but rather to facilitate the application of 3D building reconstruction algorithms, making them easier to use. The calculated F1-scores for each class confirm the competitive performances over the state-of-the-art techniques, which are more than 95% almost in each area of the used datasets.https://www.tandfonline.com/doi/10.1080/22797254.2023.2210745Machine learningbuilding reconstructionedge pointfeature point extractionsegmentationboundary point extraction |
| spellingShingle | Emon Kumar Dey Mohammad Awrangjeb Fayez Tarsha Kurdi Bela Stantic Machine learning-based segmentation of aerial LiDAR point cloud data on building roof European Journal of Remote Sensing Machine learning building reconstruction edge point feature point extraction segmentation boundary point extraction |
| title | Machine learning-based segmentation of aerial LiDAR point cloud data on building roof |
| title_full | Machine learning-based segmentation of aerial LiDAR point cloud data on building roof |
| title_fullStr | Machine learning-based segmentation of aerial LiDAR point cloud data on building roof |
| title_full_unstemmed | Machine learning-based segmentation of aerial LiDAR point cloud data on building roof |
| title_short | Machine learning-based segmentation of aerial LiDAR point cloud data on building roof |
| title_sort | machine learning based segmentation of aerial lidar point cloud data on building roof |
| topic | Machine learning building reconstruction edge point feature point extraction segmentation boundary point extraction |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2023.2210745 |
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