Electronic maps (E-maps) provide people with convenience in real-world space. Although web map services can display maps on screens, a more important function is their ability to access geographical features. An E-map that is based on raster tiles is inferior to vector tiles in terms of interactive ability because vector maps provide a convenient and effective method to access and manipulate web map features. However, the critical issue regarding rendering tiled vector maps is that geographical features that are rendered in the form of map symbols via vector tiles may cause visual discontinuities, such as graphic conflicts and losses of data around the borders of tiles, which likely represent the main obstacles to exploring vector map tiles on the web. This paper proposes a tiled vector data model for geographical features in symbolized maps that considers the relationships among geographical features, symbol representations and map renderings. This model presents a method to tailor geographical features in terms of map symbols and ‘addition’ (join) operations on the following two levels: geographical features and map features. Thus, these maps can resolve the visual discontinuity problem based on the proposed model without weakening the interactivity of vector maps. The proposed model is validated by two map data sets, and the results demonstrate that the rendered (symbolized) web maps present smooth visual continuity.

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 30 meter cell size. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format.

The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data.

The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria.

The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

This dataset consists of the vector version of the Land Cover Map 2015 (LCM2015) for Great Britain. The vector data set is the core LCM data set from which the full range of other LCM2015 products is derived. It provides a number of attributes including land cover at the target class level (given as an integer value and also as text), the number of pixels within the polygon classified as each land cover type and a probability value provided by the classification algorithm (for full details see the LCM2015 Dataset Documentation). The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. LCM2015 is a land cover map of the UK which was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. It is the latest in a series of land cover maps, which began with Land Cover Map of Great Britain (LCMGB) in 1990 (LCMGB is now often referred to as LCM1990), and was followed by Land Cover Maps for 2000 and 2007 (LCM2000 and LCM2007 respectively), both of which covered the entire UK. LCM2015 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the CEH web site and the LCM2015 Dataset documentation) to select the product most suited to their needs.

This dataset consists of the 25m raster version of the Land Cover Map 1990 (LCM1990) for Great Britain. The 25m raster product consists of three bands: Band 1 - raster representation of the majority (dominant) class per polygon for 21 target classes; Band 2 - mean per polygon probability as reported by the Random Forest classifier (see supporting information); Band 3 - percentage of the polygon covered by the majority class. The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. The 25m raster is the most detailed of the LCM1990 raster products both thematically and spatially, and it is used to derive the 1km products. LCM1990 is a land cover map of the UK which was produced at the UK Centre for Ecology & Hydrology by classifying satellite images (mainly from 1989 and 1990) into 21 Broad Habitat-based classes. It is the first in a series of land cover maps for the UK, which also includes maps for 2000, 2007, 2015, 2017, 2018 and 2019. LCM1990 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the UKCEH web site and the LCM1990 Dataset documentation) to select the product most suited to their needs. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks) For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub. To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

This dataset is the product of a geospatial interpolation using groundwater-level data obtained from a U.S. Geological Survey (USGS) synoptic survey of 129 groundwater wells in Fauquier County, VA from October 29 through November 2, 2018 and selected points from the National Hydrography Dataset (NHD). Methodology is detailed in USGS SIR 2022-5014 "Groundwater-level contour map of Fauquier County, VA, October - November 2018." Files include a continuous raster surface of groundwater-level altitudes at a horizontal resolution of 30 meters and vector lines of discrete groundwater-level altitude contours.

As one of the plain wetland systems in northern China, Baiyangdian Wetland plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of Baiyangdian Wetland ecosystem is also of great significance for the construction of the New Area and future scientific planning. Based on the 10-meter spatial resolution sentinel-2B image provided by ESA in September 2017, combined with Google Earth high resolution satellite image (resolution 0.23m), the wetland ecosystem network distribution map and river network distribution map of in Baiyangdian basin in 2017 were drawn by artificial visual interpretation and machine automatic classification, which can provide reference for the wetland connectivity (including hydrological connectivity and landscape connectivity) in Baiyangdian basin. The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (.shp); Baiyangdian basin river channel data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of Baiyangdian basin and the river 3 km buffer) (.tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif). The boundary of Baiyangdian basin in this dataset comes from the basic geographic information map of Baiyangdian basin provided by Zhou Wei and others. The DEM is the GDEM digital elevation data with 30m resolution. The original image data of wetland remote sensing classification comes from the sentinel-2B remote sensing image on September 20, 2017 provided by ESA. This data set uses the second, third, fourth and eighth bands of the 10m resolution in the image. The preprocessing operations such as radiometric calibration, mosaic and mosaic are carried out in SNAP and ArcGIS 10.2 software, and the supervised classification is carried out in ENVI software. The data used for river channel extraction is based on Google Earth high resolution satellite images. The research and development steps of this dataset include: preprocessing sentinel-2B image, establishing wetland classification system and selecting samples, drawing the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; based on Google Earth high-resolution satellite image (resolution 0.23m), this paper uses LocaSpaceViewer software to identify and extract river channels by manual visual interpretation. For the river channels with embankment, identify and draw along the embankment; for the river channels without embankment, distinguish according to the spectral difference between the river channels and the surrounding land use types and empirical knowledge, mark the uncertain areas, and conduct field investigation in the later stage, which can ensure that the identified river channels have been extracted. The identified river channels include the main river channel, each classified river channel, abandoned river channel, etc., and all rivers are continuous. It can effectively identify the channel and ensure the accuracy of extraction. According to the river network map of Baiyangdian basin obtained by manual visual interpretation, the total length of the river in Baiyangdian basin is about 2440 km, and the total area is 514 km2. Among them, there are 177 km2 river channels in mountainous area, with a length of 866 km, distributed in northeast-southwest direction, mostly at the junction of forest land and cultivated land; there are 337 km2 river channels in plain area, with a length of 1574 km. The Baiyangdian basin is divided into eight land use / cover types: river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land. The remote sensing monitoring results show that the wetland area of Baiyangdian basin accounted for 13.90% in 2017. Among all the wetland types, the area of marsh is the largest, followed by the area of flood plain, ditch accounts for about 1%, and the proportion of lake and river is less than 0.5%. Combined with the land use / cover classification map and the distribution of slope and elevation, it can be seen that nearly 60% of the area of forest land is distributed in 10 ° to 30 ° mountain area, and the rest of the land use / cover types are mainly distributed in 0 ° to 2 ° area. The elevation statistics show that nearly 80% of the lakes and large reservoirs are distributed in the height of 100 m to 300 m, the distribution of marsh is relatively uniform, mainly in the higher altitude area of 20 m to 300 m, the types of construction land, flood area and cultivated land are mainly concentrated in the area of 20 m to 100 m, and rivers and ditches are mainly concentrated in the area of 0 m to 100 m. Based on the classification results of land use / cover within the river, it can be found that the main land use type is wetland. Specifically, the types of marsh, flood area and lake are the most, while the types of ditch and river are less. With the increase of the buffer area, the proportion of non-wetland type gradually increased, while the proportion of wetland type gradually decreased. The main wetland types in 1-3km buffer zone on both sides of the river are marsh and flood zone. It is worth noting that nearly one third of the River belongs to cultivated land, that is, the river occupation is serious. In terms of area, about 1 / 3 rivers and 3 / 4 lakes are distributed in the river course. Most of the water bodies in the river course are controlled by human beings, but the marsh area in the river course only accounts for about 3% of the marsh area in the whole river course. In this study, 8 types of land features including river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land were selected. The total number of samples was 5199, of which 67% was used for supervised classification and 33% for accuracy verification of confusion matrix. The overall accuracy of support vector machine (SVM) classification results in Baiyangdian basin is 84.25%, and kappa coefficient is 0.82. River occupation will not only directly reduce the connectivity of wetlands in the basin, but also cause some environmental and economic problems such as water pollution. However, if the connectivity of wetlands is reduced, the ecological and environmental functions of wetlands will be destroyed, which will pose a great threat to the water security of the basin. Taking Baiyangdian basin as a whole, improving the connectivity of wetlands and enhancing the ecological and environmental functions of wetlands in the basin will help to improve the water ecological and environmental security of Xiong'an New Area and Baiyangdian basin.

Land cover maps are the basic data layer required for understanding and modeling ecological patterns and processes. The Circumpolar Arctic Vegetation Map (CAVM), produced in 2003, has been widely used as a base map for studies in the arctic tundra biome. However, the relatively coarse resolution and vector format of the map were not compatible with many other data sets. We present a new version of the CAVM, building on the strengths of the original map, while providing a finer spatial resolution, raster format, and improved mapping. The Raster CAVM uses the legend, extent and projection of the original CAVM. The legend has 16 vegetation types, glacier, saline water, freshwater, and non-arctic land. The Raster CAVM divides the original rock-water-vegetation complex map unit that mapped the Canadian Shield into two map units, one with lichen-dominated vegetation and one with shrub-dominated vegetation. In contrast to the original hand-drawn CAVM, the raster map is based on unsupervised classifications of seventeen geographic/floristic sub-sections of the Arctic, using AVHRR and MODIS data (reflectance data and NDVI) and elevation data. The units resulting from the classification were modeled to the CAVM types using a wide variety of ancillary data. The map was reviewed by experts familiar with their particular region, including of the original authors of the CAVM from the U.S., Canada, Greenland (Denmark), Iceland, Norway (including Svalbard) and Russia.

Detailed information about the methods can be found in the publication to which this dataset is a supplement.

Citation

In order to use these data, you must cite this data set with the following citation:

Raynolds, Martha; Walker, Donald (2019), “Raster Circumpolar Arctic Vegetation Map”, Mendeley Data, v1 https://dx.doi.org/10.17632/c4xj5rv6kv.1

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).

The Bright Earth eAtlas Basemap dataset collection is a satellite-derived global map of the world at a 1:1M scale for most of the world and 1:200k scale for Australia. This map was inspired by Natural Earth II (NEII) and NASA's Blue Marble Next Generation (BMNG) imagery.

Its aim was to provide a basemap similar to NEII but with a higher resolution (~10x).

This basemap is derived from the following datasets: Blue Marble Next Generation 2004-04 (NASA), VMap0 coastline, Coast100k 2004 Australian coastline (GeoScience Australia), SRTM30 Plus v8.0 (UCSD) hillshading, Natural Earth Vector 10m bathymetry and coastline v2.0 (NE), gbr100 hillshading (JCU).

This dataset (World_Bright-Earth-e-Atlas-basemap) contains all the files required to setup the Bright Earth eAtlas basemap in a GeoServer. All the data files are stored in GeoTiffs or shapefiles and so can also be loaded into ArcMap, however no styling has been included for this purpose.

This basemap is small enough (~900 MB) that can be readily used locally or deployed to a GeoServer.

Base map aesthetics (added 28 Jan 2025)

The Bright Earth e-Atlas Basemap is a high-resolution representation of the Earth's surface, designed to depict global geography with clarity, natural aesthetics with bright and soft color tones that enhance data overlays without overwhelming the viewer. The land areas are based on NASA's Blue Marble imagery, with modifications to lighten the tone and apply noise reduction filtering to soften the overall coloring. The original Blue Marble imagery was based on composite satellite imagery resulting in a visually appealing and clean map that highlights natural features while maintaining clarity and readability. Hillshading has been applied across the landmasses to enhance detail and texture, bringing out the relief of mountainous regions, plateaus, and other landforms.

The oceans feature three distinct depth bands to illustrate shallow continental areas, deeper open ocean zones, and the very deep trenches and basins. The colors transition from light blue in shallow areas to darker shades in deeper regions, giving a clear sense of bathymetric variation. Hillshading has also been applied to the oceans to highlight finer structures on the seafloor, such as ridges, trenches, and other geological features, adding depth and dimensionality to the depiction of underwater topography.

At higher zoom levels prominent cities are shown and the large scale roads are shown for Australia.

Rendered Raster Version (added 28 Jan 2025)

A low resolution version of the dataset is available as a raster file (PNG, JPG and GeoTiff) at ~2 km and 4 km resolutions. These rasters are useful for applications where GeoServer is not available to render the data dynamically. While the rasters are large they represent a small fraction of the full detail of the dataset. The rastered version was produced using the layout manager in QGIS to render maps of the whole world, pulling the imagery from the eAtlas GeoServer. This imagery from converted to the various formats using GDAL. More detail is provided in 'Rendered-bright-earth-processing.txt' in the download and browse section.

Change Log 2025-01-28: Added two rendered raster versions of the dataset at 21600x10800 and 10400x5400 pixels in size in PNG, JPG and GeoTiff format. Added

Context

Map data from Natural Earth.

This data set contains the cultural and physical vector data sets only. It does not contain the raster format data. Cultural data contains map data on countries, states, boundaries, roads, railways, airports, ports, urban areas, etc.

Data are organized by scale, see here for details: - 110m: 1:110,000,000, suitable for schematic maps of the world on a postcard or as a small locator globe. - 50m: 1:50,000,000, suitable for making zoomed-out maps of countries and regions. Show the world on a tabloid size page. - 10m: 1:10,000,000, the most detailed. Suitable for making zoomed-in maps of countries and regions. Show the world on a large wall poster.

Acknowledgements

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset is a complete state-wide digital land use map of Queensland. The dataset is a product of the Queensland Land Use Mapping Program (QLUMP) and was produced by the Queensland Government. It presents the most current mapping of land use features for Queensland, including the land use mapping products from 1999, 2006 and 2009, in a single feature layer. This dataset was last updated July 2012. See additional information also.

Purpose

Indicates the current primary use or management objective of the land.

Dataset History

Source DataQueensland Government - Land use mapping (1999); Landsat TM and ETM imagery; Spot5 imagery; High resolution ortho photography through the Spatial Imagery Subscription Plan (SISP); Queensland Digital Cadastral Database (DCDB) (2009), Queensland Valuation and Sales Database (QVAS) (2009); Queensland Nature Refuges (2009); Queensland Estates (2009); Queensland Herbarium's Regional Ecosystem, Water Body and Wetlands datasets (2009); Statewide Landcover & Trees Study (SLATS) Queensland Dams and Waterbodies (2009) and land cover change data; scanned aerial photography (1999-2009).Additional verbal & written information on land uses & their locations was obtained from regional Queensland Government officers, Local Government Authorities, land owners & managers, private industry as well as from field observations & checking.Data captureA range of existing digital datasets containing land use information was collated from the Queensland Government spatial data inventory and prepared for use in a GIS using ArcGIS and ERDAS Imagine software.Processing steps To compile the 1999 baseline mapping, datasets containing baseline land cover (supplied by SLATS), Protected Areas, State Forest and Timber Reserves, plantations, coastal wetlands, reserves (from DCDB) and logged forests were interpreted in a spatial model to produce a preliminary land use raster image.The model incorporated a decision matrix which assigned each pixel a specific land use class according to a set of pre-determined rules.Individual catchments were clipped from the model output and enhanced with additional land use information interpreted primarily from Landsat TM and ETM imagery as well as scanned and hardcopy aerial photography (where available). The DCDB and other datasets containing land use information were used to help identify property and land use type boundaries. This process produced a draft land use raster.Verification of the draft land use dataset, particularly those with significant areas of intensive land uses, was undertaken by comparing mapped land use classes with observed land use classes in the field where possible. The final raster image was converted to a vector coverage in ARC/Info and GIS editing performed.The existing 1999 baseline (or later where available) land use dataset (vector) formed the basis for the 2006 and 2009 land use mapping. The 2006 & 2009 datasets were then updated primarily by interpretation of SPOT5 imagery, high-res orthophotography, scanned aerial photography and inclusion of expert local knowledge. This was performed in an ESRI ArcSDE geodatabase replication infrastructure, across some nine regional offices. The DCDB, QVAS, Estates, Queensland Herbarium wetlands and SLATS land cover change and waterbody datasets were used to assist in identification and delineation of property and land use type boundaries. Digitised areas of uniform land use type were assigned to land use classes according to ALUMC Version 7 (May 2010).This "current" land use mapping product presents a complete state-wide land use map of Queensland, after collating the most current land use datasets within a single mapping layer.An independent validation was undertaken to assess thematic (attribute) accuracy under the ALUM classification. Please refer to the orignal source data for the validation results.

Dataset Citation

Queensland Department of Science, Information Technology, Innovation and the Arts (2013) Bioregional_Assessment_Programme_Land use mapping - Queensland current. Bioregional Assessment Source Dataset. Viewed 21 December 2017, http://data.bioregionalassessments.gov.au/dataset/740d257f-b622-49c2-9745-be283239add3.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below:

corine/*

• CORINE Land Cover (CLC) database
• Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/
• Extract from Terms of Use:

Access to data is based on a principle of full, open and free access as established by the Copernicus data and information policy Regulation (EU) No 1159/2013 of 12 July 2013. This regulation establishes registration and licensing conditions for GMES/Copernicus users and can be found here. Free, full and open access to this data set is made on the conditions that:

• When distributing or communicating Copernicus dedicated data and Copernicus service information to the public, users shall inform the public of the source of that data and information.

• Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the Union.

• Where that data or information has been adapted or modified, the user shall clearly state this.

• The data remain the sole property of the European Union. Any information and data produced in the framework of the action shall be the sole property of the European Union. Any communication and publication by the beneficiary shall acknowledge that the data were produced “with funding by the European Union”.

• https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

eez/*

• World exclusive economic zones (EEZ)
• Source: http://www.marineregions.org/sources.php#unioneezcountry
• Extract from Terms of Use:

Marine Regions’ products are licensed under CC-BY-NC-SA. Please contact us for other uses of the Licensed Material beyond license terms. We kindly request our users not to make our products available for download elsewhere and to always refer to marineregions.org for the most up-to-date products and services.

• http://www.marineregions.org/disclaimer.php

natura/*

• Natura 2000 natural protection areas
• Source: https://www.eea.europa.eu/data-and-maps/data/natura-10
• Extract from Terms of Use:

EEA standard re-use policy: unless otherwise indicated, re-use of content on the EEA website for commercial or non-commercial purposes is permitted free of charge, provided that the source is acknowledged (https://www.eea.europa.eu/legal/copyright). Copyright holder: Directorate-General for Environment (DG ENV).

• https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata

naturalearth/*

• World country shapes
• Source: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/
• Extract from Terms of Use:

All versions of Natural Earth raster + vector map data found on this website are in the public domain. You may use the maps in any manner, including modifying the content and design, electronic dissemination, and offset printing. The primary authors, Tom Patterson and Nathaniel Vaughn Kelso, and all other contributors renounce all financial claim to the maps and invites you to use them for personal, educational, and commercial purposes.

No permission is needed to use Natural Earth. Crediting the authors is unnecessary.

• http://www.naturalearthdata.com/about/terms-of-use/

NUTS_2013_60M_SH/*

• Europe NUTS3 regions
• Source: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• Extract from Terms of Use:

In addition to the general copyright and licence policy applicable to the whole Eurostat website, the following specific provisions apply to the datasets you are downloading. The download and usage of these data is subject to the acceptance of the following clauses:

1. The Commission agrees to grant the non-exclusive and not transferable right to use and process the Eurostat/GISCO geographical data downloaded from this page (the "data").

2. The permission to use the data is granted on condition that: the data will not be used for commercial purposes; the source will be acknowledged. A copyright notice, as specified below, will have to be visible on any printed or electronic publication using the data downloaded from this page.

• https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• https://ec.europa.eu/eurostat/about/policies/copyright

gebco/GEBCO_2014_2D.nc

• GEBCO bathymetric dataset
• Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/
• Extract from Terms of Use:

The GEBCO Grid is placed in the public domain and may be used free of charge. Use of the GEBCO Grid indicates that the user accepts the conditions of use and disclaimer information given below.

Users are free to:

• Copy, publish, distribute and transmit The GEBCO Grid

• Adapt The GEBCO Grid

• Commercially exploit The GEBCO Grid, by, for example, combining it with other information, or by including it in their own product or application

Users must:

• Acknowledge the source of The GEBCO Grid. A suitable form of attribution is given in the documentation that accompanies The GEBCO Grid.

• Not use The GEBCO Grid in a way that suggests any official status or that GEBCO, or the IHO or IOC, endorses any particular application of The GEBCO Grid.

• Not mislead others or misrepresent The GEBCO Grid or its source.

• https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf

je-e-21.03.02.xls

• Population and GDP data for Swiss Cantons
• Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html
• Extract from Terms of Use:

Information on the websites of the Federal Authorities is accessible to the public. Downloading, copying or integrating content (texts, tables, graphics, maps, photos or any other data) does not entail any transfer of rights to the content.

Copyright and any other rights relating to content available on the websites of the Federal Authorities are the exclusive property of the Federal Authorities or of any other expressly mentioned owners.

Any reproduction requires the prior written consent of the copyright holder. The source of the content (statistical results) should always be given.

This dataset consists of a 25m resolution raster version of the Land Cover Map 2007 for Great Britain. Each 25m pixel represents a 25m area of land cover target class, broadly representing Broad Habitats (see below). The dataset is part of a series of data products produced by the Centre for Ecology & Hydrology known as LCM2007. LCM2007 is a parcel-based thematic classification of satellite image data covering the entire United Kingdom. The map updates and upgrades the Land Cover Map of Great Britain (LCMGB) 1990 and LCM2000. Like the earlier 1990 and 2000 products, LCM2007 is derived from a computer classification of satellite scenes obtained mainly from Landsat, IRS and SPOT sensors and also incorporates information derived from other ancillary datasets. LCM2007 was classified using a nomenclature corresponding to the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompasses the entire range of UK habitats. In addition, it recorded further detail where possible. The series of LCM2007 products includes vector and raster formats, with a number of different versions containing varying levels of detail and at different spatial resolutions.

The gSSURGO dataset provides detailed soil survey mapping in raster format with ready-to-map attributes organized in statewide tiles for desktop GIS. gSSURGO is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO generally has the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes and are derived from properties and characteristics stored in the National Soil Information System (NASIS).

The gSSURGO data were prepared by merging the traditional vector-based SSURGO digital map data and tabular data into statewide extents, adding a statewide gridded map layer derived from the vector layer, and adding a new value-added look up table (valu) containing ready-to-map attributes. The gridded map layer is in an ArcGIS file geodatabase in raster format, thus it has the capacity to store significantly more data and greater spatial extents than the traditional SSURGO product. The raster map data have a 10-meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link the raster cells and polygons to attribute tables.

For more information, see the gSSURGO webpage: https://www.nrcs.usda.gov/resources/data-and-reports/description-of-gridded-soil-survey-geographic-gssurgo-database

This dataset consists of the 1km raster, percentage target class version of the Land Cover Map 2015 (LCM2015) for Great Britain. The 1km percentage product provides the percentage cover for each of 21 land cover classes for 1km x 1km pixels. This product contains one band per target habitat class (producing a 21 band image). The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM2015 is a land cover map of the UK which was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. LCM2015 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the CEH web site and the LCM2015 Dataset documentation) to select the product most suited to their needs. LCM2015 was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. It is one of a series of land cover maps, produced by UKCEH since 1990. They include versions in 1990, 2000, 2007, 2015, 2017, 2018 and 2019.

This is the land parcels (polygon) dataset for the UKCEH Land Cover Map of 2017 (LCM2017) representing Northern Ireland. It describes Northern Ireland's land cover in 2017 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived from the corresponding LCM2017 20m classified pixels dataset. All further LCM2017 datasets for Northern Ireland are derived from this land parcel product. A range of land parcel attributes are provided. These include the dominant UKCEH Land Cover Class given as an integer value, and a range of per-parcel pixel statistics to help to assess classification confidence and accuracy; for a full explanation please refer to the dataset documentation. LCM2017 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2017. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2017. LCM2017 was simultaneously released with LCM2018 and LCM2019. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

This dataset consists of the vector version of the Land Cover Map 1990 (LCM1990) for Great Britain. The vector data set is the core LCM data set from which the full range of other LCM1990 products are derived. It provides a number of attributes including land cover at the target class level (given as an integer value and also as text), the number of pixels within the polygon classified as each land cover type and a probability value provided by the classification algorithm (for full details see the LCM1990 Dataset Documentation). The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. LCM1990 is a land cover map of the UK which was produced at the UK Centre for Ecology & Hydrology by classifying satellite images (mainly from 1989 and 1990) into 21 Broad Habitat-based classes. It is the first in a series of land cover maps for the UK, which also includes maps for 2000, 2007, 2015, 2017, 2018 and 2019. LCM1990 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the UKCEH web site and the LCM1990 Dataset documentation) to select the product most suited to their needs. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.