This data publication provides a European assessment of building exposure, organized country-by-country. The dataset provides information about the number of buildings; the number of occupants; structural information and structural costs of buildings per geographical area. The main purpose of this data collection is risk assessment for natural hazards, however it can be used by anyone in need of a building exposure dataset.
The data holds information about single buildings, with global estimates of built-up area on 10m x 10m pixels and exposure information per district. All OpenStreetMap (OSM) buildings existing in an OSM excerpt from 1 July 2023, 00:00 UTC (OpenStreetMap contributors, 2023), all buildings from the Global ML Building Footprint (GMLBF, Microsoft, 2023) dataset have been processed and for each building the occupancy type and number of stories have been identified based on data in OSM, such as land use and points of interest. The Global Human Settlement Built-up Characteristics 2022A Layer has been used as initial distribution of built area (Pesaresi, 2022). Aggregated exposure information, including the structural information and the number of occupants, stems the ESRM20 (Crowley et al., 2020).
The resulting dataset is distributed per country as an SQLite/SpatiaLite database. Each database contains three tables and one view. The database is organized around three key concepts, that each have their own table. An Entity is a geographical unit that contains exposure. In this dataset, the entities are tiles in a multi-resolution grid, according to the Quad tree structure (Finkel & Bentley, 1974), with the tiles projected using the Web Mercator projection (EPSG:3857). The zoom-level of the Quadkeys inside the grid varies from level-15 to level-18, depending on the number of buildings inside each tile to preserve privacy-sensitive information. Practically, the size of the tiles varies between around 100m x 100m and 1km x 1km. Each entity consists of one or more Assets, defining the number of buildings of a particular structural type and their population and structural value. The structural type is described using a taxonomy string, describing for example structural properties, occupancy type and the expected number of stories. The exact definition of a taxonomy that is used in this dataset is described in the GEM Building Taxonomy v2.0 (Brzev et al., 2013). On top of the tables, one key view has been defined too. A view is essentially a query on the table that give some insights into the data. The `key_values_per_tile` provides the total number of buildings, total number of occupants at night and total structural costs summed over all assets in one tile entity.
This work has received funding from the European Union thought the Geo-INQUIRE project (GA 101058518), within the Research Infrastructures Programme of Horizon Europe.
This dataset provides risk estimates from the long-term (5000-year) simulations of the process-based Regional Flood Model chain (RFM) developed for Germany (Falter et al. 2015). The 5000-year simulation is run as an ensemble of 50 100-year simulations. Each of those 100-year simulations is referred to as a scenario. The risk estimates are derived in Euros adjusted to prices as of 2018 for all major catchments in Germany – Elbe, Danube, Rhine, Weser and Ems. The dataset consists of the risk estimates for every simulated event at the catchment-level classified according to the sector – private sector (ps), commercial (com) and agriculture (agr). Losses to buildings and contents are estimated for private and commercial sectors. Crop losses are estimated for the agriculture sector. The full description of the RFM along with the derivation of the risk estimates and uncertainty measurement is provided in Sairam et al. (2021).