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This data publication provides the OpenBuildingMap dataset, organized per zoom-level 6 Quadkey. The dataset provides information about the occupancy and height of individual buildings. 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. Buildings play a critical role in understanding human settlement patterns and are essential for applications such as crisis management, urban planning, energy efficiency, and multi-hazard risk assessment. To address the need for comprehensive and accessible global building data, we introduce a dataset containing 2.7 billion building footprints, enriched with structured attributes such as occupancy type and height information classified using the GEM Building Taxonomy. This dataset is derived from the integration of the AI-derived Open Buildings and the Global ML Building Footprints datasets, and the crowdsourced OpenStreetMap, hence creating the most detailed and extensive building dataset to date. The quality of the dataset has been researched using intrinsic quality checks and external reference datasets, including cadaster data and the Global Human Settlement Layer. It is provided as a GeoPackage, to ensure it is easily accessible. This work has received funding from the European Union thought the Geo-INQUIRE project (GA 101058518), within the Research Infrastructures Programme of Horizon Europe.
We construct a precomputed lookup table to predict flood loss to private households based on predictor variables from a Bayesian Network model (BN-FLEMO∆). BN-FLEMO∆ is a probabilistic model that provides multinomial probability distributions of relative building loss (i.e. absolute building loss/building value) in discrete classes. More information on the development of BN-FLEMO∆ can be found in Rafiezadeh Shahi et al. (2025). The zip folder contains the precomputed lookup table, where all possible combinations of predictor and response values are stored. The lookup table contains an ID for each unique combination of possible predictor and response (i.e., relative loss) values. The file name is coded as “2023-002_Rafiezadeh Shahi-et-al_lookup.csv”.
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 data repository for the Southern Caribbean and NW South America contains a 3D thermal model computed down to 75 km depth, the modelled hypocentral temperatures and geothermal gradients at the locations of crustal earthquakes, and the crustal seismogenic depths calculated from earthquake statistics, as well as the associated modelled temperatures. We used the uppermost 75 km of the gravity-constrained structural and density model of Gómez-García et al. (2020, 2021) to derive the 3D thermal configuration of the study area (5°-15° N, 63°-82° W). A steady-state approach was followed, in which upper and lower boundary conditions were set to run the thermal calculations using the software GOLEM (Cacace & Jacquey, 2017; Jacquey & Cacace, 2017). A catalogue of earthquakes occurred within the study area and surroundings was compiled from public sources. In the database archived here, we provide data of the best located crustal earthquakes within the boundaries of this area, from January 1980 to June 2021. Earthquakes below the magnitude of completeness, or with poorly determined depths, were disregarded. Earthquakes were deemed crustal if their hypocentres were located between the topo-bathymetry from the GEBCO relief (Weatherall et al., 2015) and the Moho depth from the GEMMA model (Reguzzoni & Sampietro, 2015). We computed the crustal seismogenic depth as the 90th and 95th percentiles (D90 and D95), respectively, of the crustal hypocentral depths. These percentiles were mapped on a latitude-longitude grid, using for each grid node at least the 20 closest earthquakes as sample. The hypocentral temperatures, the geothermal gradient at the earthquake locations, and the temperatures at the D90 and D95 surfaces were calculated from the lithospheric-scale thermal model. For more details about the modelling approach and interpretation of the results, we kindly ask the reader to refer to the main publication: Gomez-Garcia et al. (2023).
This data repository contains the 3D steady-state thermal field computed for the South Caribbean and NW South America down to 75 km depth, the modelled hypocentral temperatures, the depths to the upper and lower stability transitions, as well as the seismogenic thickness calculated from selected earthquakes of the ISC Bulletin (International Seismological Centre, 2022). All methodological details can be found in the main publication (see section 2). We used the uppermost 75 km of the gravity-constrained structural and density model of Gómez-García et al. (2020, 2021) to derive the 3D thermal configuration of the study area. A steady-state approach was followed, in which upper and lower boundary conditions were set to run the thermal experiments using the software GOLEM (Cacace & Jacquey, 2017; Jacquey & Cacace, 2017). We selected earthquakes from the ISC Bulletin from January 1980 to January 2021 (International Seismological Centre, 2022), considering the magnitude of completeness for different periods, removing earthquakes without depth, set as 0 km or fixed, as well as those with reported hypocentral depth errors >30 km. Of this set, we selected the crustal earthquakes, located between the topo-bathymetry from the GEBCO relief (Weatherall et al., 2015) and the Moho depth from the GEMMA model (Reguzzoni & Sampietro, 2015), interpolated to a resolution of 5 km. From this earthquake subset we computed the upper and lower stability transitions for seismogenesis, as the 10th and 90th percentiles (D10 and D90), respectively, of the hypocentral depths. These percentiles were mapped on a latitude-longitude grid, using for each grid node its 20 closest earthquakes as sample. The hypocentral temperatures and the temperatures at the D10 and D90 crustal depths were calculated from the lithospheric-scale thermal model. Lastly, the crustal seismogenic thickness was computed as the difference between D90 and D10 for each grid node. For more details about the modelling approach and interpretation of the results, we kindly ask the reader to refer to the main publication: Gomez-Garcia et al., (2022).
Data used for simulating the flood event in July 2021 along the river Ahr, Germany, and results of the simulation. The data cover the reach of the river from Altenahr to Sinzig (inflow to the Rhine). The data set contains: Model input data: - DEM with 10 m resolution (ASCII Raster) - Roughness raster (ASCII Raster) - building raster (ASCII Raster) - boundary time series (csv spreadsheet) Model outpout data: - maximum inundation depths of flood forecast and estimated real flood peak (ASCII Raster) - maximum effective flow velocities of flood forecast and estimated real flood peak (ASCII Raster) - maximum prduct of water depth and flow valocities of flood forecast and estimated real flood peak (ASCII Raster)
This dataset provides average national-level current gross replacement costs of the stock of residential assets (buildings and household contents) per m2 of useful floor space. The dataset includes annual time series (2000–2018) for 31 European countries, in nominal and real prices. It is a thorough revision and update of the dataset described in Paprotny et al. (2020), "Estimating exposure of residential assets to natural hazards in Europe using open data", Nat. Hazards Earth Syst. Sci., 20, 323–343, additionally expanded by one country (Croatia) and an aggregate for the European Union (27 countries). The dataset contains data in two formats (CSV and XLSX) and documentation (PDF).
Multi-temporal landslide inventories are important information for the understanding of landslide dynamics and related predisposing and triggering factors, and thus a crucial prerequisite for probabilistic hazard and risk assessment. Despite the great importance of these inventories, they do not exist for many landslide prone regions in the world. In this context, the recently evolving global-scale availability of high temporal and spatial resolution optical satellite imagery (RapidEye, Sentinel-2A/B, planet) has opened up new opportunities for the creation of these multi-temporal inventories.Taking up on these at the time still to be evolving opportunities, a semi-automated spatiotemporal landslide mapper was developed at the Remote Sensing Section of the GFZ Potsdam being capable of deriving post-failure landslide objects (polygons) from optical satellite time series data (Behling et al., 2014). The developed algorithm was applied to a 7500 km² study area using RapidEye time series data which were acquired in the frame of the RESA project (Project ID 424) for the time period between 2009 and 2013. A multi-temporal landslide inventory from 1986 to 2013 derived from multi-sensor optical satellite time series data is available as separate publications (Behling et al., 2016; Behling and Roessner, 2020).The resulting multi-temporal landslide inventory being subject of this data publication is supplementary to the article of Behling et al. (2014), which describes the developed spatiotemporal landslide mapper in detail. This landslide mapper detects landslide objects by analyzing temporal NDVI-based vegetation cover changes and relief-oriented parameters in a rule-based approach combining pixel- and object-based analysis. Typical landslide-related vegetation changes comprise abrupt disturbances of the vegetation cover in the result of the actual failure as well as post-failure revegetation which usually happens at a slower pace compared to vegetation growth in the surrounding undisturbed areas, since the displaced landslide masses are susceptible to subsequent erosion and reactivation processes. The resulting landslide-specific temporal surface cover dynamics in form of temporal trajectories is used as input information to detect freshly occurred landslides and to separate them from other temporal variations in the surrounding vegetation cover (e.g., seasonal vegetation changes or changes due to agricultural activities) and from permanently non-vegetated areas (e.g., urban non-vegetated areas, water bodies, rock outcrops). For a detailed description of the methodology of the spatiotemporal landslide mapper, please see Behling et al. (2014).The data are provided in vector format (polygons) in form of a standard shapefile contained in the zip-file Behling_et-al_2014_landslide_inventory_SouthernKyrgyzstan_2009_2013.zip and are described in more detail in the data description file.
Multi-temporal landslide inventories are important information for the understanding of landslide dynamics and related predisposing and triggering factors, and thus a crucial prerequisite for probabilistic hazard and risk assessment. Despite the great importance of these inventories, they do not exist for many landslide prone regions in the world. In this context, the recently evolving global-scale availability of high temporal and spatial resolution optical satellite imagery (RapidEye, Sentinel-2A/B, planet) has opened up new opportunities for the creation of these multi-temporal inventories.To derive such multi-temporal landslide inventories, a semi-automated spatiotemporal landslide mapper was developed at the Remote Sensing Section of the GFZ Potsdam being capable of deriving post-failure landslide objects (polygons) from multi-sensor optical satellite time series data (Behling et al., 2016). The developed approach represents an extension of the original methodology (Behling et al., 2014, Behling and Roessner, 2020) and facilitates the integration of optical time series data acquired by different satellite systems. The goal of combining satellite data originating from variable sensor systems has been the establishment of longest possible time series for retrospective systematic assessment of multi-temporal landslide activity at highest possible temporal and spatial resolution. We applied the developed approach to a 2500 km² study area in Southern Kyrgyzstan using an optical satellite database acquired by the Landsat TM/ETM+, SPOT 1/5, IRS1-C LISSIII, ASTER, and RapidEye sensor systems covering a time period between 1986 and 2013. A multi-temporal landslide inventory from 2009-2013 derived from RapidEye satellite time series data is available as separate publications (Behling et al., 2014; Behling and Roessner, 2020).The resulting systematic multi-temporal landslide inventory being subject of this data publication is supplementary to the article of Behling et al. (2016), which describes the extended spatiotemporal landslide mapper in detail. This multi-sensor approach prioritizes most suitable images within the available multi-sensor satellite time series using parameters, such as spatial resolution, cloud coverage, similarity of sensor characteristics and seasonality related to vegetation characteristics with the goal of establishing a robust back-bone time series for initial detection of possible landslide objects. In a second step, this initial analysis gets more refined in order to achieve the best possible approximation of the date of landslide occurrence. For a more detailed description of the methodology of the extended spatiotemporal landslide mapper, please see Behling et al. (2016).In general, this landslide mapper detects landslide objects by analyzing temporal NDVI-based vegetation cover changes and relief-oriented parameters in a rule-based approach combining pixel- and object-based analysis. Typical landslide-related vegetation changes comprise abrupt disturbances of vegetation cover in the result of the actual failure as well as post-failure revegetation which usually happens at a slower pace compared to vegetation growth in the surrounding undisturbed areas, since the displaced landslide masses are susceptible to subsequent erosion and reactivation processes. The resulting landslide-specific temporal surface cover dynamics in form of temporal trajectories is used as input information to identify freshly occurred landslides and to separate them from other temporal variations in the surrounding vegetation cover (e.g., seasonal vegetation changes or changes due to agricultural activities) and from permanently non-vegetated areas (e.g., urban non-vegetated areas, water bodies, rock outcrops).The data are provided in vector format (polygons) in form of a standard shapefile contained in the zip-file 2020-002_Behling_et-al_2016_landslide_inventory_SouthernKyrgyzstan_1986_2013.zip and are described in more detail in the associated data description.
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