Machine learning-based segmentation of aerial LiDAR point cloud data on building roof
Article
Dey, Emon Kumar, Awrangjeb, Mohammad, Tarsha Kurdi, Fayez and Stantic, Bela. 2023. "Machine learning-based segmentation of aerial LiDAR point cloud data on building roof." European Journal of Remote Sensing. 56 (1). https://doi.org/10.1080/22797254.2023.2210745
| Article Title | Machine learning-based segmentation of aerial LiDAR point cloud data on building roof |
|---|---|
| ERA Journal ID | 210444 |
| Article Category | Article |
| Authors | Dey, Emon Kumar, Awrangjeb, Mohammad, Tarsha Kurdi, Fayez and Stantic, Bela |
| Journal Title | European Journal of Remote Sensing |
| Journal Citation | 56 (1) |
| Article Number | 2210745 |
| Number of Pages | 18 |
| Year | 2023 |
| Publisher | Taylor & Francis |
| Place of Publication | Italy |
| ISSN | 1129-8596 |
| 2039-7879 | |
| 2279-7254 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1080/22797254.2023.2210745 |
| Web Address (URL) | https://www.tandfonline.com/doi/full/10.1080/22797254.2023.2210745 |
| Abstract | Three-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. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. |
| Keywords | boundary point extraction; Machine learning; building reconstruction; edge point; feature point extraction; segmentation |
| ANZSRC Field of Research 2020 | 4017. Mechanical engineering |
| Byline Affiliations | Griffith University |
| School of Surveying and Built Environment |
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| Machine learning-based segmentation of aerial LiDAR point cloud data on building roof.pdf | ||
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