Abstract. The aim of this study is to assess and map the seismic risk for Germany, restricted to the expected losses of damage to residential buildings. There are several earthquake prone regions in the country which have produced M w magnitudes above 6 and up to 6.7 corresponding to observed ground shaking intensity up to VIII-IX (EMS-98). Combined with the fact that some of the earthquake prone areas are densely populated and highly industrialized and where therefore the hazard coincides with high concentration of exposed assets, the damaging implications from earthquakes must be taken seriously. In this study a methodology is presented and pursued to calculate the seismic risk from (1) intensity based probabilistic seismic hazard, (2) vulnerability composition models, which are based on the distribution of residential buildings of various structural types in representative communities and (3) the distribution of assets in terms of replacement costs for residential buildings. The estimates of the risk are treated as primary economic losses due to structural damage to residential buildings. The obtained results are presented as maps of the damage and risk distributions. For a probability level of 90% non-exceedence in 50 years (corresponding to a mean return period of 475 years) the mean damage ratio is up to 20% and the risk up to hundreds of millions of euro in the most endangered communities. The developed models have been calibrated with observed data from several damaging earthquakes in Germany and the nearby area in the past 30 years.

This paper quantitatively evaluates the suitability of multi-sensor remote sensing to assess the seismic vulnerability of buildings for the example city of Padang, Indonesia. Features are derived from remote sensing data to characterize the urban environment and are subsequently combined with in situ observations. Machine learning approaches are deployed in a sequential way to identify meaningful sets of features that are suitable to predict seismic vulnerability levels of buildings. When assessing the vulnerability level according to a scoring method, the overall mean absolute percentage error is 10.6%, if using a supervised support vector regression approach. When predicting EMS-98 classes, the results show an overall accuracy of 65.4% and a kappa statistic of 0.36, if using a naive Bayes learning scheme. This study shows potential for a rapid screening assessment of large areas that should be explored further in the future.

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