Building roof plane extraction from LIDAR data

- Awrangjeb, Mohammad, Lu, Guojun

Title
Building roof plane extraction from LIDAR data
Creator
Awrangjeb, Mohammad; Lu, Guojun
Date
2013
Type
Text; Conference paper
Identifier
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161415
Identifier
vital:12450
Identifier
https://doi.org/10.1109/DICTA.2013.6691490
Abstract
This paper presents a new segmentation technique to use LIDAR point cloud data for automatic extraction of building roof planes. The raw LIDAR points are first classified into two major groups: ground and non-ground points. The ground points are used to generate a 'building mask' in which the black areas represent the ground where there are no laser returns below a certain height. The non-ground points are segmented to extract the planar roof segments. First, the building mask is divided into small grid cells. The cells containing the black pixels are clustered such that each cluster represents an individual building or tree. Second, the non-ground points within a cluster are segmented based on their coplanarity and neighbourhood relations. Third, the planar segments are refined using a rule-based procedure that assigns the common points among the planar segments to the appropriate segments. Finally, another rule-based procedure is applied to remove tree planes which are generally small in size and randomly oriented. Experimental results on three Australian sites have shown that the proposed method offers high building detection and roof plane extraction rates.
Publisher
IEEE
Relation
2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Rights
© 2013 IEEE
Rights
This metadata is freely available under a CCO license
Subject
0801 Artificial Intelligence and Image Processing
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