Automatic detection and characerization of ground occlusions in urban point clouds from mobile laser scanning data

J. Balado, E. González, E. Verbree, L. Díaz-Vilariño, H. Lorenzo

    Research output: Contribution to journalConference articleScientificpeer-review

    1 Citation (Scopus)
    4 Downloads (Pure)

    Abstract

    Occlusions accompany serious problems that reduce the applicability of numerous algorithms. The aim of this work is to detect and characterize urban ground gaps based on occluding object. The point clouds for input have been acquired with Mobile Laser Scanning and have been previously segmented into ground, buildings and objects, which have been classified. The method generates various raster images according to segmented point cloud elements, and detects gaps within the ground based on their connectivity and the application of the hit-or-miss transform. The method has been tested in four real case studies in the cities of Vigo and Paris, and an accuracy of 99.6% has been obtained in occlusion detection and labelling. Cars caused 80.6% of the occlusions. Each car occluded an average ground area of 11.9 m2. The proposed method facilitates knowing the percentage of occluded ground, and if this would be reduced in successive multi-temporal acquisitions based on mobility characteristics of each object class.

    Original languageEnglish
    Pages (from-to)13-20
    Number of pages8
    JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Volume6
    Issue number4/W1
    DOIs
    Publication statusPublished - 3 Sep 2020
    Event3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference 2020 - London, United Kingdom
    Duration: 7 Sep 202011 Sep 2020

    Keywords

    • urban environment
    • point clouds
    • occlusion detection
    • image processing
    • raster
    • object classification

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