The authors visually inspected the building damage caused by the 2011 Tohoku earthquake tsunami, using the pre and post-event aerial photos. First, we prepared the mosaic of post-tsunami aerial photos acquired by Geospatial Information Authority of Japan (GSI), and conducted the visual inspection of buildings damage to classify the damage. The damage classification results are compiled with building shape files on GIS for mapping the structural vulnerability in the tsunami inundation zone. Finally, we discussed the structural vulnerability in the tsunami affected area based on mapping results of building damage.

On March 11th, 2011, the Pacific coast of Japan was hit by a tsunami generated by the largest earthquake (M9.0) in the history of the country and causing a wide range of devastating damage. Using preliminary reported data from many sources, some topics such as tsunami fatality ratio and tsunami fragility curves for structural damage are discussed and compared with other countries. This paper aims to discuss the damage characteristics of this tsunami as well as its mechanism, as observed through field surveys conducted over the 4 months following the tsunami. The field survey covers 13 areas in the Miyagi prefecture from Kesennuma city in the northernmost region to Yamamoto town in the southernmost region. The arrival time of the first tsunami along the coastal areas in the Miyagi prefecture was confirmed by stopped clocks found during the survey. The damage mechanism of coastal structures such as breakwaters, seawalls, tsunami gates, and evacuation buildings was investigated and discussed. Damage characteristics for each area, i.e., urban areas, port, coastal structures, fisheries, and agricultural areas, were also summarized. The conclusions drawn from the data analysis suggest that experience and education (soft countermeasures) are important to reduce the loss of life, as shown for example in the Sanriku area. The field surveys indicate that wood and reinforced-concrete (RC) structures should be balanced to survive both earthquake and tsunami forces, and 1250005-1 Coast. Eng. J. 2012.54. Downloaded from www.worldscientific.com by UNIVERSITY OF CALIFORNIA @ SAN DIEGO on 06/09/15. For personal use only. A. Suppasri et al.the structural design for buildings should be reconsidered after the example in Onagawa town. In addition, coastal structures for tsunami countermeasures (hard countermeasures) should be more properly designed for survival instead of becoming floating debris upon being overturned by a tsunami. The combination of both hard and soft measures is especially necessary for optimizing the outcomes following a great disaster. These recommendations should be taken into consideration in the reconstruction efforts for better tsunami countermeasures in the future.

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The Tohoku earthquake of 11 March 2011 caused very large tsunamis and widespread devastation. Various high-resolution satellites captured details of affected areas and were utilized in emergency response. In this study, high-resolution pre- and post-event TerraSAR-X intensity images were used to identify tsunami-flooded areas and damaged buildings. Since water surface generally shows very little backscatter, flooded areas could be extracted by the difference of backscattering coefficients between the pre- and post-event images. Impacted buildings were detected by calculating the difference and correlation coefficient within the outline of each building. The damage estimates were compared with visual interpretation results, which suggest that the overall accuracy of the proposed method for flooded areas was 80%, and for damaged buildings was 94%. Since the proposed half-automated method takes less processing time and is applicable to various cases, it is expected to provide quick and useful information in emergency management.

In this letter, a new approach is proposed to classify tsunami-induced building damage into multiple classes using preand post-event high-resolution radar (TerraSAR-X) data. Buildings affected by the 2011 Tohoku earthquake and tsunami were the focus in developing this method. In synthetic aperture radar (SAR) data, buildings exhibit high backscattering caused by doublebounce reflection and layover. However, if the buildings are completely washed away or structurally destroyed by the tsunami, then this high backscattering might be reduced, and the post-event SAR data will show a lower sigma nought value than the pre-event SAR data. To exploit these relationships, a rapid method for classifying tsunami-induced building damage into multiple classes was developed by analyzing the statistical relationship between the change ratios in areas with high backscattering and in areas with building damage. The method was developed for the affected city of Sendai, Japan, based on the decision tree application of a machine learning algorithm. The results provided an overall accuracy of 67.4% and a kappa statistic of 0.47. To validate its transferability, the method was applied to the town of Watari, and an overall accuracy of 58.7% and a kappa statistic of 0.38 were obtained.