ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 393-400, 2015
https://doi.org/10.5194/isprsannals-II-3-W5-393-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
20 Aug 2015
BUILDING CHANGE DETECTION FROM LIDAR POINT CLOUD DATA BASED ON CONNECTED COMPONENT ANALYSIS
M. Awrangjeb1, C. S. Fraser2, and G. Lu1 1School of Engineering and Information Technology, Federation University, Churchill Vic 3842, Australia
2CRC for Spatial Information, Dept. of Infrastructure Engineering, University of Melbourne, Parkville Vic 3010, Australia
Keywords: Building detection, change detection, map update, automation, LIDAR, point cloud data Abstract. 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.
Conference paper (PDF, 3788 KB)


Citation: Awrangjeb, M., Fraser, C. S., and Lu, G.: BUILDING CHANGE DETECTION FROM LIDAR POINT CLOUD DATA BASED ON CONNECTED COMPONENT ANALYSIS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 393-400, https://doi.org/10.5194/isprsannals-II-3-W5-393-2015, 2015.

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