Bathymetric datasets are an extraction of surveys belonging to the Shom public database. For depth up to 50m, the vertical precision of soundings varies from 30cm to 1m and the horizontal precision varies from 1 to 20m. In deep ocean, the vertical precision is mainly around 1 or 2% of the bottom depth. It is sometimes more, it depends on the technology used. The data are referenced to ZH which is assimilated to LAT. Data are corrected for sound velocity variations.

This metadata refers to a dataset that shows the percentage of cities' administrative area (core city based on the Urban Morphological Zones dataset) inundated by the sea level rise of 2 metres, without any coastal flooding defences present for a series of individual coastal European cities (included in Urban Audit). The dataset has been computed using the CReSIS (Centre for Remote Sensing of Ice Sheets) dataset for 2018.

The bathymetric DEM of the atlantic facade with a resolution of 0.001° (~ 100 m) was prepared as part of the HOMONIM project. It encompasses part of the North Sea, the Channel and the Bay of Biscay. Offshore, this DTM extends beyond the slope to approximately 4800 m depth. The DEM is designed to be used in hydrodynamic models in order to produce high-precision forecasts for coastal water levels and sea conditions and therefore improve the pertinence of the Waves-Submersion monitoring programme. This product is available with the Lowest Astronomic Tide (LAT) or the Mean Sea Level (MSL) as a vertical datum.

Digital Surface Models (DSMs) of the study area surveyed by drone between June 2024 and March 2025. Each pixel contains the elevation value corresponding to the terrain at the time of the mission. These DSMs were generated from around 1,300 images (depending of the survey) acquired using the DJI Mavic 3E RTK drone, with a resolution of 3.2 cm/pixel. The surveyed area covers around 2.1 km². The campaign was conducted on the highest tides of each month. The drone acquisitions were carried out by Nicolas Lachaussée from the LIENSs UMR Drone Platform. Photogrammetric processing was performed by Natacha Volto (LIENSs) using Agisoft Metashape software. The vertical coordinate system is NGF IGN69. The vertical accuracy (RMSD ~ 3 cm), calculated using around 100 control points, randomly distributed across the study area. It is derived from the vertical difference for each control point between the value measured with the DGPS SP80 and the value extracted from the photogrammetry-generated DSM. These drone surveys were carried out as part of the thesis of Loës Le Goff Le Gourrierec supervised by Xavier Bertin.

This metadata corresponds to the EUNIS Coastal habitat types, predicted distribution of habitat suitability dataset. Coastal habitats are those above spring high tide limit (or above mean water level in non-tidal waters) occupying coastal features and characterised by their proximity to the sea, including coastal dunes and wooded coastal dunes, beaches and cliffs. Includes free-draining supralittoral habitats adjacent to marine habitats which are normally only very rarely subject to any type of salt water, in as much as they may be inhabited predominantly by terrestrial species, strandlines characterised by terrestrial invertebrates and moist and wet coastal dune slacks and dune-slack pools. Supralittoral sands and wracks may be found also in marine habitats (M). Excludes supralittoral rock pools and habitats, the splash zone immediately above the the mean water line, as well the spray zone and zone subject to sporadic inundation with salt water in as much as it may be inhabited predominantly by marine species, which are included in marine (M). The modelled suitability for EUNIS coastal habitat types is an indication of where conditions are favourable for the habitat type based on sample plot data (Braun-Blanquet database) and the Maxent software package. The modelled suitability map may be used as a proxy for the geographical distribution of the habitat type. Note however that it is not representing the actual distribution of the habitat type. As predictors for the suitability modelling not only climate and soil parameters have been taken into account, but also so-called RS-EVB's, Remote Sensing-enabled Essential Biodiversity Variables, like land use, vegetation height, phenology, and LAI (Leaf Area Index). Because the EBV's are restricted by the extent of the remote sensing data (EEA38 countries and the United Kingdom) the modelling result does also not go beyond this boundary. The dataset is provided both in Geodatabase and Geopackage formats.