Detection of building regions using airborne LiDAR : a new combination of raster and point cloud based GIS methods

B. Höfle, W. Mücke, M. Dutter, M. Rutzinger, P. Dorninger

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review


In this paper, a new GIS workflow for fully automated building detection from airborne LiDAR data is introduced. The strengths of both raster and point cloud based methods are combined, in order to derive reliable building candidate regions serving as input for 3D building outline extraction and modeling algorithms. Input data are a normalized Digital Surface Model (nDSM) and a slope-adaptive echo ratio raster, which is a significant pa-rameter for solid objects with low surface roughness, such as buildings. In contrast, high vegetation exhibits a local vertical distribution of laser echoes leading to a low echo ratio value. Potential building areas are detected in the raster domain using standard tools pro-vided by GRASS GIS. Seed regions are identified by using a threshold on (i) object height >2.0 m and (ii) echo ratio >75%. The following growing of the seed regions provides that building walls, overhanging roof parts, and areas obstructed by high vegetation are in-cluded. Finally, non-building regions are removed by an object-based classification using a threshold on average laser point surface roughness. The presented candidate region detec-tion achieves high completeness (>97%) with already moderate correctness (>70%). By applying an existing 3D building outline extraction and modeling algorithm, the applicabil-ity of the derived candidate building regions is demonstrated.
Original languageEnglish
Title of host publicationGeospatial crossroads @ GI_Forum '09
Subtitle of host publicationProceedings of the Geoinformatics Forum Salzburg, Geoinformatics on stage, July 7-10, 2009
EditorsA. Car, G. Griesebner, J. Strobl
Place of PublicationHeidelberg, Germany
PublisherWichmann Verlag
ISBN (Print)978-3-87907-481-5
Publication statusPublished - 2009


  • ADLIB-ART-345
  • EOS


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