• DocumentCode
    2829815
  • Title

    Joint combination of point cloud and DSM for 3D building reconstruction using airborne laser scanner data

  • Author

    Tarsha-Kurdi, F. ; Landes, T. ; Grussenmeyer, P.

  • Author_Institution
    Photogrammetry & Geomatics Group, Strasbourg
  • fYear
    2007
  • fDate
    11-13 April 2007
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    More and more cities are looking for service providers able to deliver 3D city models in a short time. Airborne laser scanning techniques make it possible to acquire a three-dimensional point cloud leading almost instantaneously to digital surface models (DSM), but these models are far from a topological 3D model needed by geographers or land surveyors. The aim of this paper is to present the pertinence and advantages of combining simultaneously the point cloud and the normalized DSM (nDSM) in the main steps of a building reconstruction approach. This approach has been implemented in order to exempt any additional data and to automate the process. The proposed workflow firstly extracts the off-terrain mask based on DSM. Then, it combines the point cloud and the DSM for extracting a building mask from the off-terrain. At last, based on the previously extracted building mask, the reconstruction of 3D flat roof models is carried out and analyzed.
  • Keywords
    geophysical signal processing; image reconstruction; remote sensing by laser beam; 3D building reconstruction; 3D city models; DSM; airborne laser scanner data; digital surface models; off-terrain mask; three-dimensional point cloud; topological 3D model; Buildings; Cities and towns; Clouds; Data mining; Image segmentation; Interpolation; Land surface; Laser modes; Laser radar; Remote sensing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Urban Remote Sensing Joint Event, 2007
  • Conference_Location
    Paris
  • Print_ISBN
    1-4244-0712-5
  • Electronic_ISBN
    1-4244-0712-5
  • Type

    conf

  • DOI
    10.1109/URS.2007.371843
  • Filename
    4234442