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 ID210444
Article CategoryArticle
AuthorsDey, Emon Kumar, Awrangjeb, Mohammad, Tarsha Kurdi, Fayez and Stantic, Bela
Journal TitleEuropean Journal of Remote Sensing
Journal Citation56 (1)
Article Number2210745
Number of Pages18
Year2023
PublisherTaylor & Francis
Place of PublicationItaly
ISSN1129-8596
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
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. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Keywordsboundary point extraction; Machine learning; building reconstruction; edge point; feature point extraction; segmentation
ANZSRC Field of Research 20204017. Mechanical engineering
Byline AffiliationsGriffith University
School of Surveying and Built Environment
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