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The purpose of this research is to develop a practical system for building damage detection in dense urban areas after natural disasters such as earthquakes, typhoons, and tsunamis. A novel approach is presented that allows real-time building change detection using high resolution aerial images and existing 3D building data. The developed system contains two major processing modules: inflight processing and ground processing. Inflight processing module mainly extracts feature information from images acquired by an airborne digital camera, and compresses them in order to transfer to the ground. Ground processing module is responsible to analyze building changes by comparing the feature information to the existing 3D data. The automated change detection is performed by the following steps: firstly, edge features of all buildings in the aerial images are extracted on board of an aircraft; Then, the outlines of each building, which are extracted from the 3D building data, are projected onto the images based on the interior and exterior orientation parameters of the camera, and compared to the extracted edge features of each building in the images. The Portion Hausdorff Matching is applied to calculate the similarities between the projected outlines and the extracted edge features. Finally, collapsed buildings are detected using the calculated similarities. The maximal similarity between the edge features and the projected outlines in the direction of projection can be used to detect the change in building height. This approach is verified using an airborne digital camera UltraCamD image and corresponding 3D building data of Tokyo, Japan. The heights of some buildings in the 3D building data are manually modified in order to simulate building changes. The experimental results show that among 1320 buildings used in the test, 45 buildings are successfully detected as collapsed or removed. Among them, 9 buildings are correctly detected out of 10 collapsed buildings. Also, 2- 885 buildings are used in another test for detecting changes in building heights. 11 of the 27 buildings known to have height changes are successfully detected. The experimental results indicate that the proposed approach shows great potential for practical applications although still faces challenges from factors such as inaccurate 3D building data, occlusions due to high building density, and small changes in building height.