Detection and Animation of Damage Using Very High-Resolution Satellite Data Following the 2003 Bam, Iran, Earthquake Tuong Thuy Vu, a… M.EERI, Masashi Matsuoka, a… M.EERI, and Fumio Yamazaki, b… M.EERI The focus of this study was to thoroughly exploit the capability of very high-resolution VHR satellite imagery such as Ikonos and QuickBird for disaster mitigation. An efficient automated methodology that detects damage was implemented to derive the rich information available from VHR satellite imagery. Consequently, the detected results and the VHR satellite imagery are attractively presented through a fly-over animation and visualization. The aim is to assist the field-based damage estimation and to strengthen public awareness. The available Ikonos and QuickBird data captured after the Bam, Iran, earthquake in December 2003 was employed to demonstrate the competence of the automated detection algorithm and fly-over animation/ visualization. These results are consistent with the field-based damage results. DOI: 10.1193/1.2101127 INTRODUCTION For decades, remote sensing techniques have been important in grasping damage in- formation caused by earthquakes. Medium resolution satellite data like SPOT, Landsat Eguchi et al. 2003; Estrada et al. 2001 or ERS Matsuoka and Yamazaki 2004 is mainly used to identify the extent of the damage. Damaged buildings can be detected using aerial photographs Mitomi et al. 2000. Recently, very high-resolution VHR im- agery from commercial satellites such as Ikonos and QuickBird, which can be rapidly acquired, is becoming more powerful and is providing information on natural and/or man-made disasters in the early stages of their unfolding. Both visual interpretation and automated analysis are currently used to detect damaged buildings, but the latter has yet to be reliably implemented. The conventional method for detecting damage caused by an earthquake is to compare pre- and post-event images. This approach has also been de- veloped forVHR data. For example, a new overlay method between pre- and post-event images was based on artificial neural networks Kosugi et al. 2000. However, it is un- realistic to obtain images of the stricken areas before a disaster, and archived data with clear and suitable images is limited. Therefore, this paper addresses an automated de- tection method that uses only post-event images so that scientists and researchers can take advantage of the ability of helicopters and airplanes to fly over the damage soon a Earthquake Disaster Mitigation Research Center, 1-5-2, Kaigandori, Wakinohama, Kobe, 651-0073, Japan b Department of Urban Environment Systems, Chiba University, 1-33Yayoi-cho, Inage-ku, Chiba 263-8522, Japan S319 Earthquake Spectra, Volume 21, No. S1, pages S319–S327, December 2005; © 2005, Earthquake Engineering Research Institute