World Library  


Add to Book Shelf
Flag as Inappropriate
Email this Book

Automatic Building Extraction from Lidar Data Covering Complex Urban Scenes : Volume Xl-3, Issue 1 (11/08/2014)

By Awrangjeb, M.

Click here to view

Book Id: WPLBN0004014783
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Automatic Building Extraction from Lidar Data Covering Complex Urban Scenes : Volume Xl-3, Issue 1 (11/08/2014)  
Author: Awrangjeb, M.
Volume: Vol. XL-3, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Historic
Publication Date:
2014
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Lu, G., Fraser, C., & Awrangjeb, M. (2014). Automatic Building Extraction from Lidar Data Covering Complex Urban Scenes : Volume Xl-3, Issue 1 (11/08/2014). Retrieved from http://kindle.worldlibrary.net/


Description
Description: School of Information Technology, Federation University – Australia Churchill Vic 3842 Australia. This paper presents a new method for segmentation of LIDAR point cloud data for automatic building extraction. Using the ground height from a DEM (Digital Elevation Model), the non-ground points (mainly buildings and trees) are separated from the ground points. Points on walls are removed from the set of non-ground points by applying the following two approaches: If a plane fitted at a point and its neighbourhood is perpendicular to a fictitious horizontal plane, then this point is designated as a wall point. When LIDAR points are projected on a dense grid, points within a narrow area close to an imaginary vertical line on the wall should fall into the same grid cell. If three or more points fall into the same cell, then the intermediate points are removed as wall points. The remaining non-ground points are then divided into clusters based on height and local neighbourhood. One or more clusters are initialised based on the maximum height of the points and then each cluster is extended by applying height and neighbourhood constraints. Planar roof segments are extracted from each cluster of points following a region-growing technique. Planes are initialised using coplanar points as seed points and then grown using plane compatibility tests. If the estimated height of a point is similar to its LIDAR generated height, or if its normal distance to a plane is within a predefined limit, then the point is added to the plane. Once all the planar segments are extracted, the common points between the neghbouring planes are assigned to the appropriate planes based on the plane intersection line, locality and the angle between the normal at a common point and the corresponding plane. A rule-based procedure is applied to remove tree planes which are small in size and randomly oriented. The neighbouring planes are then merged to obtain individual building boundaries, which are regularised based on long line segments. Experimental results on ISPRS benchmark data sets show that the proposed method offers higher building detection and roof plane extraction rates than many existing methods, especially in complex urban scenes.

Summary
Automatic Building Extraction From LIDAR Data Covering Complex Urban Scenes

 

Click To View

Additional Books


  • Examining Map Projection Distortions Usi... (by )
  • High Resolution Airborne Shallow Water M... (by )
  • A Framework of Cognitive Indoor Navigati... (by )
  • Learning Image Descriptors for Matching ... (by )
  • Dem Construction Using Dinsar : Volume X... (by )
  • Method for Stereo Mapping Based on Objec... (by )
  • The Performance Analysis of an Indoor Mo... (by )
  • Fusion of Hyperspectral and Lidar Data B... (by )
  • Extracting Rail Track Geometry from Stat... (by )
  • Development of Gis Tool for the Solution... (by )
  • Space Moving Objects Spatio-temporal Mod... (by )
  • Integration of Image Data for Refining B... (by )
Scroll Left
Scroll Right

 



Copyright © World Library Foundation. All rights reserved. eBooks from World eBook Library on the Kindle are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.