A series of maps at 1:10 000 scale showing base mapping for Northern Ireland. These raster maps can be used with other maps or information to enhance the mapping. Midscale Raster for Northern Ireland can be used as a general background to give context at local and regional level and as a base to overlay data. Includes water bodies, rivers, main roads, town names and townlands.Please Note for Open Data NI Users: Esri Rest API is not Broken, it will not open on its own in a Web Browser but can be copied and used in Desktop and Webmaps

This dataset holds the unpublished map “Carte physionomique des faciès savaniens de Lamto" drawn by de la Souchère; P. and Badarello, L. in 1969. We georeferenced the scanned paper map using ground control points derived from Google Maps. The dataset contains the scanned map, the ground control points and the raster layer of the georeferenced map.

The SESMAR dataset offers readily available maps and raster images tailored for scientists and decision-makers. It is derived from a wealth of remote sensing data covering the period from 2001 to 2023. Operating at a spatial resolution of 500m, this dataset evaluates soil loss susceptibility in the North African region. The application of the Revised Universal Soil Loss Equation (RUSLE) model, originally formulated by Wischmeier and Smith in 1978, was used to enhance the credibility of the dataset's computational methodology.

The dataset lies on the integration of diverse open-source datasets, namely MOD13A2.061 Terra Vegetation Indices for calculating the Cover Management factor, MCD12Q1.006 MODIS Land Cover Type Yearly Global, CHIRPS dataset for precipitation, Shuttle Radar Topography Mission (SRTM) dataset for topography, and Open Land Map Soil Texture Class (USDA System). This multi-source integration enhances the dataset's reliability and applicability for various environmental and agricultural studies.

The SESMAR dataset provides consistent susceptibility maps for major North African basins and offers readily classified raster images, enhancing its usability for researchers and practitioners. The basins are extracted from HydroSHEDS/v1/Basins/hybas dataset based on HYBAS_ID, also provided to ensure the identification of the specific basin for further analysis. The reliance on HydroSHEDS, a robust mapping product by Lehner and Grill (2013), ensures comprehensive hydrographic information across different scales, ranging from coarse to detailed. For the convenience of prospective users, it's noteworthy that the resultant raster datasets cover extensive basins, which can be further partitioned into smaller or medium-sized sub-basins as necessary.

The dataset is splitted into 22 rasters in a compressed format, consisting of a single band each one. It characterizes soil loss susceptibility, categorizing each raster cell into six distinct classes. The classification is based on the estimated annual soil loss rates per hectare, with associated values as follows:

• 0: No Data This category designates areas where soil loss susceptibility information is unavailable, serving as a placeholder for missing or inaccessible data.

• 1: Very Low (< 5 t/ha/year) Raster cells in this class represent areas with very low susceptibility to soil loss, indicating an annual rate of less than 5 tons per hectare.

• 2: Low (5 to 15 t/ha/year) This class characterizes areas with low susceptibility, where the annual soil loss rate falls within the range of 5 to 15 tons per hectare.

• 3: Medium (15 to 50 t/ha/year) Raster cells categorized as medium susceptibility denote moderate levels of soil loss, with an annual rate ranging from 15 to 50 tons per hectare.

• 4: High (50 to 80 t/ha/year) This class identifies areas with high susceptibility to soil loss, where the annual rate ranges from 50 to 80 tons per hectare.

• 5: Very High (> 80 t/ha/year) Raster cells in this category indicate the highest susceptibility to soil loss, with an annual rate exceeding 80 tons per hectare.

This comprehensive classification system is integral to the raster dataset, facilitating a nuanced understanding of soil loss susceptibility across different geographical locations. The dataset serves for environmental and agricultural planning, enabling stakeholders to identify and prioritize areas for targeted soil conservation measures. Continuous efforts to maintain data accuracy through updates and validation processes will ensure the dataset's reliability and relevance over time.

This dataset holds the map “Carte du recouvrement ligneux de la réserve de Lamto" published by Gautier, L. in 1990. We georeferenced the scanned paper map using ground control points derived from Google Maps. The dataset contains the scanned map, the ground control points and the raster layer of the georeferenced map.

This raster dataset was developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using This raster dataset was developed for the Sea Grant South Coast MPA Baseline Program as part of the project “Nearshore Substrate Mapping and Change Analysis using Historical and Concurrent Multispectral Imagery” (#R/MPA 30 10-049). The study region is the South Coast Region (SCR). Imagery was acquired on October 14, 2012 at a spatial resolution of 0.3 meters using a Microsoft UltraCam-X digital camera acquiring in the red, green, blue and near-infrared bands. Information on the UltraCam-X camera system and wavelengths for each ban can be found in the file "The Microsoft Vexcel UltraCam X.pdf" included in the Support folder on the image data delivery media and on the OceanSpaces.org server. This image mosaic product is a result of the resampling of the 0.3 meter data to 1 meter GSD. Details on this system and the data processing are below in the Lineage section of this document. Individual UCX image tiles were mosaicked into sections based on the islands covered and local coastal regions as well as the SCR MPA zones in order to generate this multispectral image product. These imagery were subsequently used to generate habitat classification thematic maps of the SCR's intertidal region and kelp beds from Point Conception to Imperial Beach, CA. The imagery files deliverd are in GeoTIFF format. This raster dataset contains a habitat classification of either offshore giant kelp beds and/or the intertidal zone along the California South Coast Region (SCR) from from Point Conception, CA down to Imperial beach, CA. This specific raster classification includes the Scorpion SMR. This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) and the Knowledge Network for Biocomplexity (KNB, https://knb.ecoinformatics.org/data) in 2013 as part of the South Coast baseline monitoring program. In 2022 this dataset was moved to the California Ocean Protection Council Data Repository (https://opc.dataone.org/) by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). At that time the GIS analysis products were added to the dataset. The long-term California MPA boundary and project info tables can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W.

Official derived mapping of Spain at scale 1:200,000 per province. The Provincial Map includes information on orography, hydrography, communications, constructions and singular elements, land uses, administrative boundary lines and toponymy.

Soil acidity is a natural process that can be exacerbated in farming systems. Current knowledge and data on the extent and severity of acidic soils in south-western Victoria is limited. This makes inferences on the impacts to production across the region difficult. Furthermore, improved mapping is required in order to define the opportunities to address soil acidity in southern Victoria and increase production potential. The availability of soil site data managed in the Victorian Soil Information System (VSIS) and spatially exhaustive ancillary datasets (i.e. environmental covariate map data such as elevation, rainfall and gamma radiometrics) support the application of predictive modelling techniques to produce soil pH maps at finer scales and qualities previously unattainable.

The digital soil maps of soil pH for the South West region of Victoria have been produced by modelling the spatial relationships between points (soil sites) of measured or estimated soil pH and their environment (defined by a comprehensive set of covariates). A 10-fold cross validation procedure was used to produce average predictions for the upper, lower and mean values. The mapping provides predictions of soil pH at 50 m pixel resolution for six set depths from the surface down to two metres. The six set depths have been chosen to align to the Global Soil Map specifications, www.globalsoilmap.net.

In total, data from 3,668 sites were identified for application in spatial models across south-western Victoria. This data has been sourced from land studies dating back to the 1950s and the 670 samples collected by this project are now accessible as part of this larger dataset. Spatial covariate datasets using in modelling includes climate (e.g. annual rainfall, evaporation, Prescott index), landscape (e.g. clay mineral maps), organisms (e.g. MODIS time series, LANDSAT scenes), relief (e.g. elevation, slope, topographic wetness index) and parent material (e.g. terrain weathering index). In total, 71 covariate raster datasets have been used in generating soil pH maps.

The maps are for soil pH measured in a 1:5 soil-to-water suspension (pHw) with possible addition of a salt solution (typically Calcium chloride, CaCl2). The raster datasets (maps) include a mean, lower and upper uncertainty prediction for each depth interval.

This geodatabase includes spatial datasets that represent the Mississippian aquifer in the States of Alabama, Illinois, Indiana, Iowa, Kentucky, Maryland, Missouri, Ohio, Pennsylvania, Tennessee, Virginia and West Virginia. The aquifer is divided into three subareas, based on the data availability. In subarea 1 (SA1), which is the aquifer extent in Iowa, data exist of the aquifer top altitude and aquifer thickness. In subarea 2 (SA2), which is the aquifer extent in Missouri, data exist of the aquifer top and bottom aquifer surface altitudes. In subarea 3 (SA3), which is the aquifer area of the remaining States, no altitude or thickness data exist. Included in this geodatabase are: (1) a feature dataset "ds40MSSPPI_altitude_and_thickness_contours that includes aquifer altitude and thickness contours used to generate the surface rasters for SA1 and SA2, (2) a feature dataset "ds40MSSPPI_extents" that includes a polygon dataset that represents the subarea extents, a polygon dataset that represents the combined overall aquifer extent, and a polygon dataset of the Ft. Dodge Fault and Manson Anomaly, (3) raster datasets that represent the altitude of the top and the bottom of the aquifer in SA1 and SA2, and (4) georeferenced images of the figures that were digitized to create the aquifer top- and bottom-altitude contours or aquifer thickness contours for SA1 and SA2. The images and digitized contours are supplied for reference. The extent of the Mississippian aquifer for all subareas was produced from the digital version of the HA-730 Mississippian aquifer extent, (USGS HA-730). For the two Subareas with vertical-surface information, SA1 and SA2, data were retrieved from the sources as described below. 1. The aquifer-altitude contours for the top and the aquifer-thickness contours for the top-to-bottom thickness of SA1 were received in digital format from the Iowa Geologic Survey. The URL for the top was ftp://ftp.igsb.uiowa.edu/GIS_Library/IA_State/Hydrologic/Ground_Waters/ Mississippian_aquifer/mississippian_topography.zip. The URL for the thickness was ftp://ftp.igsb.uiowa.edu/GIS_Library/IA_State/Hydrologic/Ground_Waters/ Mississippian_aquifer/mississippian_isopach.zip Reference for the top map is Altitude and Configuration, in feet above mean sea level, of the Mississipian Aquifer modified from a scanned image of Map 1, Sheet 1, Miscellaneous Map Series 3, Mississippian Aquifer of Iowa by P.J. Horick and W.L. Steinhilber, Iowa Geological Survey, 1973; IGS MMS-3, Map 1, Sheet 1 Reference for the thickness map is Distribution and isopach thickness, in feet, of the Mississipian Aquifer, modified from a scanned image of Map 1, Sheet 2, Miscellaneous Map Series 3, Mississippian Aquifer of Iowa by P.J. Horick and W.L. Steinhilber, Iowa Geological Survey, 1973; IGS MMS-3, Map 1, Sheet 2 The altitude contours for the top and bottom of SA2 were digitized from georeferenced figures of altitude contours in U.S. Geological Survey Professional Paper 1305 (USGS PP1305), figure 6 (for the top surface) and figure 9 (for the bottom surface). The altitude contours for SA1 and SA2 were interpolated into surface rasters within a GIS using tools that create hydrologically correct surfaces from contour data, derive the altitude from the thickness (depth from the land surface), and merge the subareas into a single surface. The primary tool was an enhanced version of "Topo to Raster" used in ArcGIS, ArcMap, Esri 2014. ArcGIS Desktop: Release 10.2 Redlands, CA: Environmental Systems Research Institute. The raster surfaces were corrected in areas where the altitude of the top of the aquifer exceeded the land surface, and where the bottom of an aquifer exceeded the altitude of the corrected top of the aquifer.

The Digital Raster Graphic (DRG) is a raster image of a scanned USGS topographic map including the collar information, georeferenced to the UTM grid. This version of the Digital Raster Graphic (DRG) has been clipped to remove the collar (white border of the map) and has been reprojected to geographic coordinates.

The High Plains aquifer extends from approximately 32 to 44 degrees north latitude and 96 degrees 30 minutes to 106 degrees west longitude. The aquifer underlies about 175,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This dataset consists of a raster of estimated water-level changes for the High Plains aquifer from pre-irrigation season 2017 to pre-irrigation season 2019. This digital dataset was created using water-level measurements from 7,195 wells measured in both 2017 and 2019. The map was reviewed for consistency with the relevant data at a scale of 1:1,000,000. Negative raster-cell values correspond to decline in water level and positive raster-cell values correspond to water-level rise.

The present dataset is part of the Alaiz Experiment-2017 (ALEX17). The information is divided into two groups based on their source. 1)Two raster-tpye geotif files containing the Digital Elevation and Digital Surface Models (DEM and DSM) data of the ALEX17 domain. The models were built by TRACASA ( https://tracasa.es/all-about-us/) which is a company part of the Navarra Government. The original dataset is cropped to fit the ALEX17 experimental domain with the following spatial coverage: 607700, 4720300 628010, 4738800 The datasets are generated through lidar airborne scans taken during years 2011 and 2012 and updated by photogrammetry with orthophotos of year 2014. The original lidar scans (2011-2012) have a density of 1pnt/m^2 . The raw data are then processed and converted to orthometric heights (from the original ellipsoidal heights ) and later projected into a 2x2m grid with spatial reference EPSG:25830. The conversion from ellipsoidal to orthometric height is carried out with the EGM2008_REDNAP model, generated by the Spanish Geographic National Institute available in: ftp://ftp.geodesia.ign.es/geoide/ 2)The second dataset is also a raster-type file which contains the approximate annual mean of aerodynamic roughness length in meters. The maps was created with two data sources: Visual estimation of the roughness length values & zones. The Corine Land Cover (CLC) 2006 data. 2.1) The visual estimations of roughness values w carried out with the use of both, orthophotos gathered from the National Geographic Institute of Spain (IGN) as well as site visits. These values were assigned to the Alaiz mountain region while the 2.2) CLC-derived roughness was set to the rest of the domain area. The orthophotos are obtained from the National Plan for Aerial Orthophotogrpy (PNOA) program (available at http://www.ign.es/ign/layoutIn/faimgsataerea.do ). These photos have a pixel size of 50cm and were taken in summer 2014. On the other hand, the Corine Land Cover (CLC) 2006 raster dataset have a 100 m grid size. These data are available at http://www.eea.europa.eu/data-and-maps/data/corine- land-cover-2006-raster-3 (g100_06.zip file). The roughness values were derived from the Land Cover data mostly based on the relation between CLC and the aerodynamic roughness length applied by the Finnish wind atlas (http://www.tuuliatlas.fi/modelling/mallinnus_3.html ). The final composed roughness raster map was built by interpolation (nearest-neighbor) of the two data sources onto a 10x10 meters grid . The map is also projected with the same spatial reference as the DEM/DSM data described above.

Spatial raster datasets derived from 1 m LiDAR digital elevation model describe topographic control on hydrological processes for the Catalina - Jemez CZO research areas. These data are intended for the visualization and support topographic and geo-spatial analysis.

The High Plains aquifer extends from approximately 32 to 44 degrees north latitude and 96 degrees 30 minutes to 106 degrees west longitude. The aquifer underlies about 175,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This digital dataset consists of a raster of water-level changes for the High Plains aquifer, predevelopment (about 1950) to 2019. It was created using water-level measurements from 2,741 wells measured in both the predevelopment period (about 1950) and in 2019, the latest available static water level measured in 2015 to 2018 from 71 wells in New Mexico and using other published information on water-level change in areas with few water-level measurements. The map was reviewed for consistency with the relevant data at a scale of 1:1,000,000. Negative raster-cell values correspond to decline in water level and positive raster-cell values correspond to water-level rise.

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

This raster dataset corresponds to the year 1947, with data obtained from the USGS Earth Explorer, an online collection of aerial photography. This image is a mosaic of the following photo frames: 1EJA000010017, 1EJA000010019, 1EJA000010024, 1EJA000010025, 1EJA000010027, 1EJA000010066, 1EJA000010067, 1EJA000010102, 1EJA000010103, 1EJA000010106, 1EJA000020081, 1EJA000020082,Some images were clipped to fit into the Roseville City limit.

Access the Data:

Access the REST Service from https://ags.roseville.ca.us/arcgis/rest/services/PublicServices/. View the data in our Historical Imagery Collection.Add data to ArcMap or ArcPro by clicking on “View Metadata” and selecting “Open in ArcGIS Desktop”.

This raster dataset is intended to be a spatial representation of the entire zoning map catalog for the City of New York as one seamless citywide raster zoning map sans title blocks. These maps are normally maintained as 126 individual quarter sections and printed periodically for inclusion in Vol III of the City’s Zoning Resolution. This dataset encompasses recent changes to mapped zoning districts or zoning text amendments as they are adopted by the City Council as well as filed City Map changes.

This dataset is one of eight datasets produced by this study. Four of the datasets predict the probability of detecting atrazine and(or) desethyl-atrazine (a breakdown product of atrazine) in ground water in Colorado; the other four predict the probability of detecting elevated concentrations of nitrate in ground water in Colorado. The four datasets that predict the probability of atrazine and(or) desethyl-atrazine (atrazine/DEA) are differentiated by whether or not they incorporated atrazine use and whether or not they incorporated hydrogeomorphic regions. The four datasets that predict the probability of elevated concentrations of nitrate are differentiated by whether or not they incorporated fertilizer use and whether or not they incorporated hydrogeomorphic regions. Each of the eight datasets has its own unique strengths and weaknesses. The user is cautioned to read Rupert (2003, Probability of detecting atrazine/desethyl-atrazine and elevated concentrations of nitrate in ground water in Colorado: U.S. Geological Survey Water-Resources Investigations Report 02-4269, 35 p., https://water.usgs.gov/pubs/wri/wri02-4269/) to determine if he(she) is using the most appropriate dataset for his(her) particular needs. This dataset specifically predicts the probability of detecting atrazine/DEA in ground water in Colorado with hydrogeomorphic regions not included and atrazine use estimates included. The following text was extracted from Rupert (2003). Draft Federal regulations may require that each State develop a State Pesticide Management Plan for the herbicides atrazine, alachlor, metolachlor, and simazine. Maps were developed that the State of Colorado could use to predict the probability of detecting atrazine/DEA in ground water in Colorado. These maps can be incorporated into the State Pesticide Management Plan and can help provide a sound hydrogeologic basis for atrazine management in Colorado. Maps showing the probability of detecting elevated nitrite plus nitrate as nitrogen (nitrate) concentrations in ground water in Colorado also were developed because nitrate is a contaminant of concern in many areas of Colorado. Maps showing the probability of detecting atrazine/DEA at or greater than concentrations of 0.1 microgram per liter and nitrate concentrations in ground water greater than 5 milligrams per liter were developed as follows: (1) Ground-water quality data were overlaid with anthropogenic and hydrogeologic data by using a geographic information system (GIS) to produce a dataset in which each well had corresponding data on atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well construction. These data then were downloaded to a statistical software package for analysis by logistic regression. (2) Relations were observed between ground-water quality and the percentage of land-cover categories within circular regions (buffers) around wells. Several buffer sizes were evaluated; the buffer size that provided the strongest relation was selected for use in the logistic regression models. (3) Relations between concentrations of atrazine/DEA and nitrate in ground water and atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well-construction data were evaluated, and several preliminary multivariate models with various combinations of independent variables were constructed. (4) The multivariate models that best predicted the presence of atrazine/DEA and elevated concentrations of nitrate in ground water were selected. (5) The accuracy of the multivariate models was confirmed by validating the models with an independent set of ground-water quality data. (6) The multivariate models were entered into a geographic information system and the probability GRIDS were constructed.

The raster dataset on the 1: 1.000.000 geological map of Italy, published by the Geological Service of Italy (APAT now ISPRA) in 2005, was produced on the analogue map published in two languages. The legend consists of 128 items.

The product represents a new design of the State Map at a scale of 1:5,000 in raster form, whose advantages are recency and colour processing. The map contains planimetry based on cadastral map, altimetry adopted from the altimetry part of ZABAGED and map lettering based on database of geographic names Geonames and abbreviations of feature type signification coming up from attributes of selected ZABAGED features. The cartographic visualisation is solved automatically without manual works of a cartographer. This new design of the SM 5 is repeatedly generated once a year on the part of the Czech territory where the vector form of cadastral map is available. Therefore, part of export units (map sheets of SM 5) has not a full coverage (price of such export unit is then proportionally reduced).

This data shows areas where merged survey bathymetry and backscatter data exists and allows you to download the data. The data was collected between 2001 and 2021.Bathymetry is the measurement of how deep is the sea. Bathymetry is the study of the shape and features of the seabed. The name comes from Greek words meaning "deep" and “measure". Bathymetry is collected on board boats working at sea and airplanes over land and coastline. The boats use special equipment called a multibeam echosounder. A multibeam echosounder is a type of sonar that is used to map the seabed. Sound waves are emitted in a fan shape beneath the boat. The amount of time it takes for the sound waves to bounce off the bottom of the sea and return to a receiver is used to determine water depth. The strength of the sound wave is used to determine how hard the bottom of the sea is. In other words, backscatter is the measure of sound that is reflected by the seafloor and received by the sonar. A strong sound wave indicates a hard surface (rocks, gravel), and a weak return signal indicates a soft surface (silt, mud).LiDAR is another way to map the seabed, using airplanes. Two laser light beams are emitted from a sensor on-board an airplane. The red beam reaches the water surface and bounces back; while the green beam penetrates the water hits the seabed and bounces back. The difference in time between the two beams returning allows the water depth to be calculated. LiDAR is only suitable for shallow waters (up to 30m depth).This data shows areas which have data available for download in Irish waters. These are areas where several surveys have been merged together.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).This data is shown as polygons. Each polygon holds information on the data type (bathymetry or backscatter), format of data available for download (GEOTIFF, ESRI GRID), its resolution, projection, last update and provides links to download the data.The data available for download are raster datasets. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns.This data was collected using a boat or plane. Data is output in xyz format. X and Y are the location and Z is the depth or backscatter value. A software package converts it into gridded data. The grid cell size varies. Most of this data is available at 10m resolution. Each grid cell size is 10 meter by 10 meter. This means that each cell (pixel) represents an area of 10 meter squared.ESRI GRID datasets contain the depth value. This means you can click on a location and get its depth.GEOTIFFS are images of the data and only record colour values. We use software to create a 3D effect of what the seabed looks like. By using vertical exaggeration, artificial sun-shading (mostly as if there is a light source in the northwest) and colouring the depths using colour maps, it is possible to highlight the subtle relief of the seabed. The darker shading represents a deeper depths and lighter shading represents shallower depths.This data shows areas that have been surveyed. There are plans to fill in the missing areas between 2020 and 2026. The deeper offshore waters were mapped as part of the Irish National Seabed Survey (INSS) between 1999 and 2005. INtegrated Mapping FOr the Sustainable Development of Ireland's MArine Resource (INFOMAR) is mapping the inshore areas. (2006 - 2026).