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Taiwan is located in a seismically active zone between the Eurasian Plate and the Philippine Sea Plate. Taiwan has a great geological diversity, but widespread alluvium deposits create the potential for soil liquefaction, which has considerable impact on society and the economy. Taiwan residential earthquake insurance was established after the Chi-Chi earthquake occurred in 1999, which had a death toll of more than 2400 and a ranking as the 12th costliest earthquake worldwide, with US$10 billion in property losses. The damage and impact of soil liquefaction were observed at the first time after the Chi-Chi earthquake. Nevertheless, the earthquake insurance claim criteria were based on the evaluation of structural components. Such criteria created difficulties for earthquake insurance inspectors in assessing the building damage caused by soil liquefaction during the 0206 Meinong earthquake in 2016 because such damage is often appeared as building rigid body tilt and settlement. As a result, geotechnical-related structural damage assessments were all performed by registered professional engineers. This study aims to develop and suggest earthquake insurance claim criteria for soil liquefaction-induced building damage, with the dual indices of settlement and tilt. The suggested criteria meet the requirements of current evaluation standards, reduce the necessity for further assessments performed by registered professional engineers, and would expedite the process of earthquake insurance claims for the ultimate goal of emergency relief.

Skin region detection is crucial for face recognition, hand tracking, and motion detection. In the detection process, a skin color model is usually required to confine the distribution of skin colors. However, skin color models are sensitive to lighting conditions. Skin segmentation under varying lighting conditions produces poor results. This article presents a skin detection procedure for human–computer interaction sessions under varying lighting conditions. The proposed method requests a skin sample from the user to estimate the color temperature of the light source. Then, the color temperature is used to correct the skin sample. At the subsequent step, the mean of the corrected skin sample is utilized to adapt the skin color model. Finally, the adapted skin color model is employed to segment skin regions in the video stream. Tests using the proposed method and some adaptive skin detection algorithms have been conducted. Statistical data show that the proposed method is superior to color constancy methods and the Gaussian mixture model in skin region segmentation. The proposed method improves the true positive rate by more than 13% in segmenting skin regions of a database. Its true positive rate is 20% better if real-life images are used as test data.