The impact of natural hazards on mankind has increased dramatically over the past decades. Global urbanization processes and increasing spatial concentrations of exposed elements induce natural hazard risk at a uniquely high level. To mitigate affiliated perils requires detailed knowledge about elements at risk. Considering a high spatio-temporal variability of elements at risk, detailed information is costly both in terms of time and economic resources and therefore often incomplete, aggregated, or outdated. To alleviate these restrictions, the availability of very high resolution satellite images promotes accurate and detailed analysis of exposure over various spatial scales with large-area coverage. In the past, valuable approaches were proposed, however, the design of information extraction procedures with a high level of automatisation remains challenging. In this paper, we uniquely combine remote sensing data and Volunteered Geographic Information from the OpenStreetMap project (OSM) (i.e., freely accessible geospatial information compiled by volunteers) for a highly automated estimation of crucial exposure components (i.e., number of buildings and population) with a high level of spatial detail. To this purpose, we first obtain labeled training segments from the OSM data in
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