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Building Change Detection from Lidar Point Cloud Data Based on Connected Component Analysis : Volume Ii-3/W5, Issue 1 (20/08/2015)

By Awrangjeb, M.

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Book Id: WPLBN0004013931
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Building Change Detection from Lidar Point Cloud Data Based on Connected Component Analysis : Volume Ii-3/W5, Issue 1 (20/08/2015)  
Author: Awrangjeb, M.
Volume: Vol. II-3/W5, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

APA MLA Chicago

Fraser, C. S., Awrangjeb, M., & Lu, G. (2015). Building Change Detection from Lidar Point Cloud Data Based on Connected Component Analysis : Volume Ii-3/W5, Issue 1 (20/08/2015). Retrieved from http://kindle.worldlibrary.net/


Description
Description: School of Engineering and Information Technology, Federation University, Churchill Vic 3842, Australia. Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.

Summary
BUILDING CHANGE DETECTION FROM LIDAR POINT CLOUD DATA BASED ON CONNECTED COMPONENT ANALYSIS

 

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