Building-level exposure asset value modelling for Germany: an Ahrweiler case study

Verified, harmonised, open-access, object-level exposure data are essential for next-generation risk assessments, risk management, and impact-based forecasting. However, this object-level information is often proprietary, protected by regulation, poorly documented, and fragmented because data on building usage, structural type, or replacement costs are not readily available or not compiled in one dataset. To address this gap, we present an evaluation of exposure model workflows employing various disaggregation approaches and source data from cadastre-derived, crowd-sourced, national accounts, and fit-for-purpose datasets. Using information collected from one flood-affected region in Germany and a weighted scoring model, we evaluate the ability of each workflow to assign a building's economic sector and asset value against our hand-labelled benchmark dataset. Ultimately, we find an exposure model workflow disaggregating national accounts onto cadastre-derived building footprints slightly outperforms the other workflows owing mainly to its transparency and adaptability. However, we conclude that all but the land-use-derived workflow are defensible for object-level exposure modelling – when validated. While these findings are limited to one specific region, the workflows developed here provide the basis for broader evaluation across regions with different building stocks, economic structures, and data quality. Workflows like these enable the transparent, reproducible, and maintainable multi-sector object-level exposure modelling necessary for the next generation of risk analysis and impact forecasting.