They also tend to focus on a reduced number of variables, contain no or limited details on structural characteristics, and can only be carried out every certain number of years, given their resource intensiveness (e.g., Crowley et al. Building-by-building surveys undertaken by qualified professionals are only possible within limited spatial extents, and wide-spread building censuses, though useful, are still relatively uncommon. The creation of building exposure models for seismic risk assessment is challenging, as relevant detailed information on structural properties, size and value of all the buildings in an area of interest is seldom readily available for such a use. Through this real-world application we demonstrate the potential of VGI and open data to be used for exposure modelling for natural risk assessment, when combined with suitable knowledge on building fragility and accounting for the inherent uncertainties. The resulting exposure model and damage estimates are presented, and it is shown that the latter are broadly consistent with damage data from the 1978 Albstadt earthquake, notwithstanding the differences in the scenario. Though applicable to any other region of interest where analogous datasets are available, the workflow and approach followed are explained by focusing on the case of the German city of Cologne, for which a scenario earthquake is defined and the potential damage is calculated. The initial model stemming purely from building-level data is enriched with statistics aggregated at the neighbourhood and city level by means of a Monte Carlo simulation that enables the generation of full realisations of damage estimates when using the exposure model in the context of an earthquake scenario calculation. This paper presents the development of a building-by-building exposure model based exclusively on openly available datasets, including both VGI and census statistics, which are defined at different levels of spatial resolution and for different moments in time. Different strategies have been developed in recent years to overcome this, including the use of census data, remote sensing imagery and volunteered graphic information (VGI). Geological evolution and dynamics on regional and global scale.The creation of building exposure models for seismic risk assessment is frequently challenging due to the lack of availability of detailed information on building structures.
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