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Remote Sens. 2015, 7(10), 14119-14150; doi:10.3390/rs71014119

Effective Generation and Update of a Building Map Database Through Automatic Building Change Detection from LiDAR Point Cloud Data

School of Engineering and Information Technology, Federation University Australia, Melbourne, VIC 3842, Australia
Academic Editors: Juha Hyyppä, Norman Kerle and Prasad S. Thenkabail
Received: 21 August 2015 / Revised: 15 October 2015 / Accepted: 16 October 2015 / Published: 27 October 2015
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
View Full-Text   |   Download PDF [8123 KB, uploaded 27 October 2015]   |  

Abstract

Periodic building change detection is important for many applications, including disaster management. Building map databases need to be updated based on detected changes so as to ensure their currency and usefulness. This paper first presents a graphical user interface (GUI) developed to support the creation of a building database from building footprints automatically extracted from LiDAR (light detection and ranging) point cloud data. An automatic building change detection technique by which buildings are automatically extracted from newly-available LiDAR point cloud data and compared to those within an existing building database is then presented. Buildings identified as totally new or demolished are directly added to the change detection output. However, for part-building demolition or extension, a connected component analysis algorithm is applied, and for each connected building component, the area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building-part. Using the developed GUI, a user can quickly examine each suggested change and indicate his/her decision to update the database, with a minimum number of mouse clicks. In experimental tests, the proposed change detection technique was found to produce almost no omission errors, and when compared to the number of reference building corners, it reduced the human interaction to 14% for initial building map generation and to 3% for map updating. Thus, the proposed approach can be exploited for enhanced automated building information updating within a topographic database. View Full-Text
Keywords: building detection; change detection; map update; automation; LiDAR; point cloud data building detection; change detection; map update; automation; LiDAR; point cloud data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Awrangjeb, M. Effective Generation and Update of a Building Map Database Through Automatic Building Change Detection from LiDAR Point Cloud Data. Remote Sens. 2015, 7, 14119-14150.

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