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  • Automatic evaluation and improvement of roof segments for modelling missing details using Lidar data

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    Awrangjeb374008Accepted.pdf (848.5Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Tarsha Kurdi, F
    Awrangjeb, M
    Griffith University Author(s)
    Awrangjeb, Mohammad
    Year published
    2020
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    Abstract
    Despite the large number of studies conducted during the last three decades concerning 3D building modelling starting from Light detection and ranging (Lidar) data, two persistent problems still exist. The first one is the absence of some roof details, which will not only disappear in the building roof model due to their small areas regarding the point density but are also considered as undesirable noise among the modelling procedures. The second problem consists in that the involved segmentation algorithms do not perform well in the presence of noise in the building point cloud data. These two problems generate undesirable ...
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    Despite the large number of studies conducted during the last three decades concerning 3D building modelling starting from Light detection and ranging (Lidar) data, two persistent problems still exist. The first one is the absence of some roof details, which will not only disappear in the building roof model due to their small areas regarding the point density but are also considered as undesirable noise among the modelling procedures. The second problem consists in that the involved segmentation algorithms do not perform well in the presence of noise in the building point cloud data. These two problems generate undesirable deformation in the final 3D building model. This paper proposes a new automatic approach for detecting and modelling the missing roof details in addition to improving the building roof segments. In this context, the error map matrix, which presents the deviations of points to their fitting planes, is considered. Moreover, this matrix is analysed in order to deduce the mask of missing roof details. At this stage, a new numeric factor is defined for estimating the roof segmentation accuracy in addition to the validity of the roof segmentation result. Then, the building point cloud is enhanced in order to decrease the negative noise influence and, consequently, to improve the building roof segments. Finally, the functionality and the accuracy of the proposed approach are tested and discussed.
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    Journal Title
    International Journal of Remote Sensing
    Volume
    41
    Issue
    12
    DOI
    https://doi.org/10.1080/01431161.2020.1723180
    Copyright Statement
    This is an Author's Accepted Manuscript of an article published in the International Journal of Remote Sensing, Volume 41, Issue 12, Pages 4700-4723, 01 Mar 2020, copyright Taylor & Francis, available online at: https://doi.org/10.1080/01431161.2020.1723180
    Subject
    Physical geography and environmental geoscience
    Engineering
    Geomatic engineering
    Publication URI
    http://hdl.handle.net/10072/392062
    Collection
    • Journal articles

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