• Title of article

    3D building reconstruction based on given ground plan information and surface models extracted from spaceborne imagery

  • Author/Authors

    Tack، نويسنده , , Frederik and Buyuksalih، نويسنده , , Gurcan and Goossens، نويسنده , , Rudi، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2012
  • Pages
    13
  • From page
    52
  • To page
    64
  • Abstract
    3D surface models have gained field as an important tool for urban planning and mapping. However, urban environments have a complex nature to model and they provide a challenge to investigate the current limits of automatic digital surface modeling from high resolution satellite imagery. An approach is introduced to improve a 3D surface model, extracted photogrammetrically from satellite imagery, based on the geometric building information embodied in existing 2D ground plans. First buildings are clipped from the extracted DSM based on the 2D polygonal building ground plans. To generate prismatic shaped structures with vertical walls and flat roofs, building shape is retrieved from the cadastre database while elevation information is extracted from the DSM. Within each 2D building boundary, a constant roof height is extracted based on statistical calculations of the height values. After buildings are extracted from the initial surface model, the remaining DSM is further processed to simplify to a smooth DTM that reflects bare ground, without artifacts, local relief, vegetation, cars and city furniture. In a next phase, both models are merged to yield an integrated city model or generalized DSM. The accuracy of the generalized surface model is assessed according to a quantitative-statistical analysis by comparison with two different types of reference data.
  • Keywords
    3D city modeling , 2D ground plans , photogrammetry , DSM , High resolution satellite imagery
  • Journal title
    ISPRS Journal of Photogrammetry and Remote Sensing
  • Serial Year
    2012
  • Journal title
    ISPRS Journal of Photogrammetry and Remote Sensing
  • Record number

    2228928