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Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessaryfor many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updatedto ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many buildingchange detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in orderto 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 extendedbuilding parts, a connected component analysis algorithm is applied and for each connected component its area, width and height areestimated 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 buildingdetection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentationerrors as compared to its original counterpart. The proposed change detection technique produces no omission errors andthus it can be exploited for enhanced automated building information updating within a topographic database. Using the developedGUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks

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