Classification of mobile laser scanning point clouds from height features

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    35 Downloads (Pure)

    Abstract

    The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of point clouds is labour intensive and thus time consuming and expensive. Hence, the need for rapid and automated methods for 3D mapping of dense point clouds is growing exponentially. The last five years the research on automated 3D mapping of MLS data has tremendously intensified. In this paper, we present our work on automated classification of MLS point clouds. In the present stage of the research we exploited three features - two height components and one reflectance value, and achieved an overall accuracy of 73%, which is really encouraging for further refining our approach.

    Original languageEnglish
    Title of host publicationThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    PublisherISPRS
    Pages321-325
    VolumeXLII-2/W7
    DOIs
    Publication statusPublished - 2017
    EventISPRS Geospatial Week 2017 - Wuhan, China
    Duration: 18 Sep 201722 Sep 2017
    http://www.isprs.org/documents/geoweek.aspx

    Conference

    ConferenceISPRS Geospatial Week 2017
    CountryChina
    CityWuhan
    Period18/09/1722/09/17
    Internet address

    Keywords

    • Classification
    • Feature extraction
    • Mobile laser scanning
    • Point clouds
    • Urban area
    • Vertical objects

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  • Cite this

    Zheng, M., Lemmens, M., & Van Oosterom, P. (2017). Classification of mobile laser scanning point clouds from height features. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. XLII-2/W7, pp. 321-325). ISPRS. https://doi.org/10.5194/isprs-archives-XLII-2-W7-321-2017