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Automatic Segmentation of Lidar Data | OMICS International | Abstract
ISSN: 2168-9717

Journal of Architectural Engineering Technology
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Review Article

Automatic Segmentation of Lidar Data

Abdelmounaim Bellakaout1*, Cherkaoui Omari Mohammed2, Ettarid Mohamed2 and Touzani Abderrahmane2

1Engineer Surveyor - Ph.D, Technical Director SELBATOP, Morocco

2Agronomic and Veterinary Institute Hassan II, Morocco

*Corresponding Asuthor:
Abdelmounaim Bellakaout
Engineer Surveyor - Ph.D
Technical Director SELBATOP, Morocco
Tel: 0212-0537801279
Fax: 0212-0537810978
E-mail: [email protected]

Received March 10, 2014; Accepted April 17, 2014; Published April 21, 2014

Citation: Bellakaout A, Omari Mohammed C, Mohamed E, Abderrahmane T (2014) Automatic Segmentation of Lidar Data. J Archit Eng Tech 3:128. doi: 10.4172/2168-9717.1000128

Copyright: © 2014 Bellakaout A, et al., This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Topographical technology by Airborne LIDAR (Light Detection and Ranging) generates a precise points cloud with a density of several points per square meter, LIDAR data processing is a crucial step to be used. Extraction of 3D information in automatic way and especially in urban areas from LIDAR data is one of the most difficult problems in computer vision; it is also a necessary step for implementation of several applications that require a high level interpretation of LASER data. Therefore, there is recently an increased interest in this research field and a vast literature. The problematic discussed in this article lies in the differentiation between the sets of points that represent a specified layer of information (construction, vegetation, roads, lines, etc.). This step is called segmentation. The aim of this study is to provide a set of automatic segmentation techniques tailored to different types of 3D data and proposes a methodology to classify LIDAR data with a maximum degree of automaticity using only point cloud data.