Journal article
Automatic segmentation of raw LIDAR data for extraction of building roofs
M Awrangjeb, CS Fraser
Remote Sensing | Published : 2014
DOI: 10.3390/rs6053716
Abstract
Automatic extraction of building roofs from remote sensing data is important for many applications, including 3D city modeling. This paper proposes a new method for automatic segmentation of raw LIDAR (light detection and ranging) data. Using the ground height from a DEM (digital elevation model), the raw LIDAR points are separated into two groups. The first group contains the ground points that form a "building mask". The second group contains non-ground points that are clustered using the building mask. A cluster of points usually represents an individual building or tree. During segmentation, the planar roof segments are extracted from each cluster of points and refined using rules, such ..
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Awarded by Australian Research Council
Funding Acknowledgements
M. Awrangjeb is a recipient of the Discovery Early Career Researcher Award by the Australian Research Council (project number DE120101778). The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation [36]. The Aitkenvale and Hervey Bay data sets were provided by Ergon Energy in Queensland, Australia. The Hobart data set was provided by Photomapping Services in Melbourne, Australia. The Knox and Eltham data sets were provided by the Department of Environment and Primary Industries of Victoria, Australia.