The European Settlement Map data is a spatial raster dataset that is mapping human settlements in Europe based on Copernicus Very High Resolution optical coverage for reference year 2015 (VHR_IMAGE_2015). It follows-up on the previous ESM_2012 derived from 2.5 m resolution SPOT-5/6 images acquired in the context of the pan-European GMES/Copernicus (Core_003) dataset for the reference year 2012.

• ESM_BUILT_VHR2015_EUROPE_R2019: classifies the built-up areas at a spatial resolution of 2 meters (EPSG:3035) • ESM_BUILT_VHR2015CLASS_EUROPE_R2019: classifies the built-up areas into residential and non-residential at a spatial resolution of 10 meters (EPSG:3035)

The data covers two independent accuracy assessmets, based on 2 sets of randomly selected points, for three urban-oriented land cover products: Global Urban Footprint - GUF, European Settlement Map - ESM and Open Street Map - OSM) to detect rural settlements in the Carpathian ecoregion. The study area covers the Carpathians located in 5 countries: Czechia, Hungary, Poland, Romania and Slovakia. The accuracy assessment was conducted with two independent approaches regarding the selection of verification points. In the first approach, a set of verification points was defined through the stratified random selection of 500 points per country (2500 points in total) within the area of any of the three analysed settlement datasets combined, hereafter called the ‘total settlement mask’. In the second approach, a set of verification points was defined through the stratified random selection of 500 independent points per country (2500 points in total) within the ‘total settlement mask’ buffered by 100 m, hereafter called the ‘buffer mask’. The dataset is accompanied with Python code.

The European Settlement Map is a spatial raster dataset that is mapping human settlements in Europe based on Copernicus Very High Resolution optical coverage for reference year 2015 (VHR_IMAGE_2015). It follows-up on the previous ESM_2012 derived from 2.5 m resolution SPOT-5/6 images acquired in the context of the pan-European GMES/Copernicus (Core_003) dataset for the reference year 2012. The ESM_2015 product exploits the Copernicus VHR_IMAGE_2015 dataset made of satellite images Pleiades, Deimos-02, WorldView-2, WorldView-3, GeoEye-01 and Spot 6/7 ranging from 2014 to 2016.

STL and Urban Atlas based data subset, where every Urban Atlas element with CODE 31000 as well as all STL elements were extracted as tree elements with the next combined information:

gid integer area numeric perimeter numeric geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint hillshade_green_fraction real

This data is an input for local effects calculation.

The European Settlement Map is a spatial raster dataset that is mapping human settlements in Europe, based on Copernicus Very High Resolution optical coverage for reference year 2018. ESM 2018 exploits the Copernicus VHR_IMAGE_2018 dataset made of satellite images Pleiades 1A & 1B, SuperView-1, Kompsat-3/3A and PlanetScope with backup missions of Spot-6/7, TripleSat and Deimos-2 ranging from 2017 to 2019. It classifies the residential and non-residential buildings at a spatial resolution of 2 meters.

This lesson looks at Indigenous and European settlement populations in 1630, 1740 and 1823.

The Global Human Settlement Layer (GHSL) project is supported by European Commission, Joint Research Center and Directorate-General for Regional and Urban Policy. The GHSL produces new global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet.

The GHSL relies on the design and implementation of new spatial data mining technologies allowing to process automatically and extract analytics and knowledge from large amount of heterogeneous data including: global, fine-scale satellite image data streams, census data, and crowd sources or volunteering geographic information sources. Spatial data reporting objectively and systematically about the presence of population and built-up infrastructures are necessary for any evidence-based modelling or assessing of i) human and physical exposure to threats as environmental contamination and degradation, natural disasters and conflicts, ii) impact of human activities on ecosystems, and iii) access to resources.

This spatial raster dataset depicts the distribution and density of residential population, expressed as the number of people per cell. Resident population from censuses for year 2011 provided by Eurostat were disaggregated from source zones to grid cells, informed by land use and land cover from Corine Land Cover Refined 2006 and by the distribution and density of built-up as mapped in the European Settlement Map 2016 layer.

ESM data subset, generated by extracting band 50 as buildings with the next information:

gid integer geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint height real

This data is an input for local effects calculation.

The LUISA Base Map 2012 is a high-resolution land use/land cover map developed and produced by the Joint Research Centre of the European Commission. It corresponds to a modified and improved version of the CORINE Land Cover 2012 map. Compared to CORINE, the LUISA Base Map delivers a higher overall spatial detail and finer thematic breakdown of artificial land use/cover categories (17 categories instead of 11 in CORINE). The LUISA Base Map can be used for multiple purposes and it is more suitable than CORINE for applications requiring fine spatial and/or thematic detail of land use/land cover consistently across Europe, such as land use/cover accounting and modelling. Coverage: EU27, Albania, Bosnia and Herzegovina, Iceland, Kosovo, Liechtenstein, Montenegro, North Macedonia, Norway, Serbia, Switzerland, Turkey, United Kingdom. RESOLUTION: MMU = 1ha (artificial surfaces); MMU = 5ha (non artificial surfaces); pixel resolution = 50m, 100m LINEAGE: The 2012 edition of the LUISA Base Map is constructed by refining the original thematic and spatial detail of the CORINE Land Cover (CLC) 2012. The methodology consists of a structured, automated and reproducible geospatial data fusion approach that integrates disparate but highly detailed land use information from a series of trusted, off-the-shelf datasets onto the CLC 2012 map, relying on data from the reporting year 2012 whenever possible. The main sources include the CLC Change Maps, the Copernicus High Resolution Layers (forest, water, wetlands, and imperviousness layers), the Copernicus Urban Atlas and Coastal Zones, the European Settlement Map 2012 from the Joint Research Centre, as well as the TomTom Multinet and OpenStreeMap. The use of various European-wide remotely sensed imagery as input and a uniform and automated methodology yields high comparability of the map across countries. The LUISA Base Maps 2012 and 2018 were produced using the same method and data sources. However, input data from 2012 and 2018 may not be always comparable. This is especially the case of the Copernicus High Resolution Layers whose sensors and algorithms changed between 2012 and 2018. For this reason, the LUISA Base Maps are not suitable for change detection. For what concerns the accounting of changes in urban fabric for larger geographical units (e.g. NUTS), the effect of differences in input data is limited because the LUISA Base Map uses the Copernicus Imperviousness change layers to detect meaningful changes of urban fabric backwards, using 2018 as the reference period. COMPLETENESS: 100%

Shows the vegetation of Australia in the mid-1980s. Areas over 30,000 hectares are shown, plus small areas of significant vegetation such as rainforests and croplands. Attribute information includes: growth form of tallest and lower stratum, foliage cover of tallest stratum and dominant floristic types.
Data are captured from 1:5 million source material, suitable for GIS applications. The source map is also available for purchase. Product Specifications: Coverage: Australia Currency: Compiled mid-1980s Coordinates: Geographical Datum: AGD66 Projection: Simple Conic on two standard parallels 18S and 36S (printed map only) Format: ArcInfo Export, ArcView Shapefile and MapInfo mid/mif (data only) Medium: Printed map - Paper (flat and folded); Free online and CD-ROM (fee applies) Forward Program: Under review.

Urban areas have a manifold and far-reaching impact on our environment, and the three-dimensional structure is a key aspect for characterizing the urban environment.

This dataset features a map of building height predictions for entire Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. We utilized machine learning regression to extrapolate building height reference information to the entire country. The reference data were obtained from several freely and openly available 3D Building Models originating from official data sources (building footprint: cadaster, building height: airborne laser scanning), and represent the average building height within a radius of 50m relative to each pixel. Building height was only estimated for built-up areas (European Settlement Mask), and building height predictions <2m were set to 0m.

Temporal extent The acquisition dates of the different data sources vary to some degree: - Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017. - Dependent variables: the 3D building models are from 2012-2020 depending on data provider. - Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016. Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015.

Data format The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems. Building height values are in meters, scaled by 10, i.e. a pixel value of 69 = 6.9m.

Further information For further information, please see the publication or contact David Frantz (david.frantz@geo.hu-berlin.de). A web-visualization of this dataset is available here.

Publication Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: https://doi.org/10.1016/j.rse.2020.112128

Acknowledgements The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission. The European Settlement Mask was obtained from the European Commission. 3D building models were obtained from Berlin Partner für Wirtschaft und Technologie GmbH, Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung, Landeshauptstadt Potsdam, Bezirksregierung Köln / Geobasis NRW, and Kompetenzzentrum Geodateninfrastruktur Thüringen. This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Funding This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This dataset presents the refined version of the degree of urbanisation of European countries. The degree of urbanisation relies on a population grid to classify local units. Originally the classification system was developed for the European Statistical System to classify local units into three classes (level 1): cities, towns & suburbs, and rural areas. In this version the classification was further refined (level 2) to also identify smaller individual settlements; distinguishing towns from suburbs and identifying villages, dispersed areas and mostly uninhabited areas in former rural areas class. The final classes of the refined degree of urbanisation dataset are six, namely 1) cities, 2) towns, 3) suburbs, 4) villages, 5) dispersed rural areas and 6) mostly uninhabited areas. The temporal reference is set between 2011 and 2012 because of the main inputs, the GEOSTAT population grid 2011 and the European Settlement Map 2012 from Copernicus. IMPORTANT NOTE: This metadata has been created using draft documentation provided by the European Commission, DG REGIO. This dataset has been created by the European Commission, DG Regional and Urban Policy (REGIO) in cooperation with the Joint Research Centre (JRC). Re-distribution or re-use of this dataset is allowed provided that the source is acknowledged.

Shows a reconstruction of Australian vegetation in the 1780s. Areas over 30,000 hectares are shown, plus small areas of significant vegetation such as rainforest. Attribute information includes: growth form of tallest and lower stratum, foliage cover of tallest stratum and dominant floristic types. Data are captured from 1:5 million source material. Data are suitable for GIS applications, via free download. The source map is also available for purchase. Product Specifications: Coverage: Australia Currency: Compiled mid-1980s Coordinates: Geographical Datum: AGD66 Projection: Simple Conic on two standard parallels 18S and 36S (printed map only) Format: ArcInfo Export, ArcView Shapefile and MapInfo mid/mif (data only) Medium: Printed map - Paper (flat and folded); Free online and CD-ROM (fee applies) Forward Program: Under review.

The Global Human Settlement Layer (GHSL) project is supported by European Commission, Joint Research Center and Directorate-General for Regional and Urban Policy. The GHSL produces new global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet.

The GHSL relies on the design and implementation of new spatial data mining technologies allowing to process automatically and extract analytics and knowledge from large amount of heterogeneous data including: global, fine-scale satellite image data streams, census data, and crowd sources or volunteering geographic information sources. Spatial data reporting objectively and systematically about the presence of population and built-up infrastructures are necessary for any evidence-based modelling or assessing of i) human and physical exposure to threats as environmental contamination and degradation, natural disasters and conflicts, ii) impact of human activities on ecosystems, and iii) access to resources.

This spatial raster dataset depicts the distribution and density of residential population, expressed as the number of people per cell. Resident population from censuses for year 2011 provided by Eurostat were disaggregated from source zones to grid cells, informed by land use and land cover from Corine Land Cover Refined 2006 and by the distribution and density of built-up as mapped in the European Settlement Map 2016 layer.

This data publication contains a shapefile of points created from 1930s maps of the first land grants within the Monongahela National Forest (MNF) proclamation boundary. Corner or witness trees are those trees listed in a land survey to describe the survey corner for future re-establishment of the corner or property line. Witness trees listed in the deeds were added as attributes to the digital point locations. Deed dates range from 1752 to 1899. If no trees were listed in the deed to witness the corner, the corner was created in the point file, but no species was assigned. The deeds and surveys were created under the metes and bounds method of land survey common in the colonial era. The entire area was not surveyed in any systematic method as is found in the Western United States so there are areas of the MNF with no witness trees. Also included are the scanned images of the maps used to create the database of corner points. Each map covers a portion of the Monongahela National Forest, WV and includes latitude and longitude reference lines. On each map are the individual parcels of land drawn by draftsmen in the 1930s from the original deeds or grants. With each tract is the name of the grantee, the data of the deed or grant, the size of the tract of land (in acres), and a unique identification number that references the deed/grant from which the sketch was made. This data publication also includes two location maps (north and south) showing the location and area covered by the individual map sheets. The base map is a 1936 map of the Monongahela National Forest, WV produced by the USDA Forest Service.This database was developed to help characterize the forest at the time of European settlement.Original metadata date was 10/09/2014. Scanned images of the maps used to create the database of corner points were added on 09/15/2016 along with a few minor metadata updates.

Minor metadata updates on 12/13/2016 and 09/16/2024 (which included URL updates for related articles).

Two vegetation maps (sold separately) - Natural Vegetation (1788) and Post-European Vegetation (1988) reconstruct Australia`s vegetation in the 1780s and the mid-1980s. Areas over 30,000 hectares are shown, plus small areas of significant vegetation such as rainforest. Attribute information includes: growth form of tallest and lower stratum, foliage cover of tallest stratum and dominant floristic types. Data was captured from 1:5 million source material. These maps are also available as free vector GIS data. Product Specifications Coverage: Australia Currency: Compiled mid-1980s Coordinates: Geographical Datum: AGD66 Projection: Simple Conic on two standard parallels 18S and 36S Medium: Printed map (flat or folded) or free data download Forward Program: Under review

The pre-European vegetation mapping of Western Australia dataset is an output of a joint project. It maps original natural vegetation presumed to have existed prior to European settlement in Western Australia. Descriptions of each of the vegetation types can be found in the accompanying memoir. The major sources of data in this database are the published and unpublished mapping of J.S. Beard at 1:250,000 scale. There are c 30,000 polygons covering 160 1:250,000 map sheets. Data on the original vegetation of all of Western Australia, with the exception of three map sheets in the south-west corner, were captured from J S Beard’s original working drawings, where these were available, or from published maps, all at the scale of 1:250,000. For the three map sheets in the south-west corner, a new data set was compiled in a form consistent with Beard’s approach, from existing data (A.J.M. Hopkins, unpublished). Also published as Beard, J. S., Beeston, G.R., Harvey, J.M., Hopkins, A. J. M. and Shepherd, D. P. 2013. The vegetation of Western Australia at the 1:3,000,000 scale. Explanatory memoir. Second edition. Conservation Science Western Australia 9: 1-152.

This map package includes the settlement patterns of the Aboriginal Peoples and Europeans in Canada from 1631-1823. In addition, explorer routes are included from 1631-1894.

Minnesota's original public land survey plat maps were created between 1848 and 1907 during the first government land survey of the state by the U.S. Surveyor General's Office. This collection of more than 3,600 maps includes later General Land Office (GLO) and Bureau of Land Management maps up through 2001. Scanned images of the maps are available in several digital formats and most have been georeferenced.

The survey plat maps, and the accompanying survey field notes, serve as the fundamental legal records for real estate in Minnesota; all property titles and descriptions stem from them. They also are an essential resource for surveyors and provide a record of the state's physical geography prior to European settlement. Finally, they testify to many years of hard work by the surveying community, often under very challenging conditions.

The deteriorating physical condition of the older maps (drawn on paper, linen, and other similar materials) and the need to provide wider public access to the maps, made handling the original records increasingly impractical. To meet this challenge, the Office of the Secretary of State (SOS), the State Archives of the Minnesota Historical Society (MHS), the Minnesota Department of Transportation (MnDOT), MnGeo (formerly the Land Management Information Center - LMIC) and the Minnesota Association of County Surveyors collaborated in a digitization project which produced high quality (800 dpi), 24-bit color images of the maps in standard TIFF, JPEG and PDF formats - nearly 1.5 terabytes of data. Funding was provided by MnDOT.

In 2010-11, most of the JPEG plat map images were georeferenced. The intent was to locate the plat images to coincide with statewide geographic data without appreciably altering (warping) the image. This increases the value of the images in mapping software where they can be used as a background layer.

Defining the pre-European range of vegetation communities can enhance our understanding of the role soil, hydrology, and climate had on climax plant communities within southwest Louisiana. Coastal prairie grasslands were in a perpetual state of succession due to two primary disturbances; grazing, primarily by bison and other ungulates, and fires ignited by lightning and Native Americans. Along its borders, prairie vegetation blended into adjacent plant communities forming biologically diverse ecotones that may have fluctuated between a prairie, marsh, or forest dominated community as a result of variable conditions including climate cycles, disturbance and soil characteristics. Since European settlement, this landscape has undergone dramatic change with less than 1% of intact coastal prairie remaining. Conservation entities across the Western Gulf Coastal Plain are taking a collaborative, strategic, landscape scale approach to pollinator conservation. This effort encourages communication and implementation of restoration and habitat enhancement actions within water sheds. We have produced a spatial dataset which considers landscape position and soil type, based on Soil Survey Geographic Database (SSURGO) data, to predict appropriate vegetation associations for plantings across southwest Louisiana based on expert elicitation, and historic references. Methods to produce this product begin with soil boundaries and identification information using Map Unit Keys (MUKEY) which were gathered from SSURGO data (Soil Survey Staff, NRCS 2017). Each mukey number was reviewed on the SOIL WEB to obtain information about components. Components include the proportion and general geomorphic features associated with soil series. Natural vegetation associations were examined and documented for each soil series individually using multiple references, including USDA Soil Series descriptions, expert elicitation, and historical spatial references. Professional reference maps contributed to this spatial dataset and include an 1863 work by Henry L. Abbot and numerous General Land Office surveyor maps and surveyor descriptions from the early 1800s drawn at the scale of a township. General vegetation categories associated with Soil Types (Mukey) were derived from reviewing the vegetation associations of the dominant components, or soil series. These general categories include: anthropogenic, prairie, transition, forest, marsh, swamp, uncertain, and water. Anthropogenic categories were generally due to significant dredging, or other industrial activities. Transitional areas included savannas and areas which may have significantly changed from prairie to forest dominated communities due to rainfall and/or fire frequency and intensity. Forest and swamp includes a range of forest types from which the distinction between these two categories primarily depend upon relative elevation and hydrology. There were a few soil series in which we are uncertain of their pre-settlement vegetation. These areas are anomalies on the landscape and include salt domes and old, disjunct river meanders which are largely comprised of Pleistocene soils and were most likely marais, yet currently much of it is heavily forested as bottomlands, and we are therefore uncertain if this result is solely due to absence of fire. Attribute data include MUKEYs within the parishes which are included in the Louisiana portion of the Gulf Coastal Plain Ecoregion. Information in the table includes symbols, common names, and components which were compiled from SSURGO dataset and Soil Web online resources (Soil Survey Staff, NRCS, accessed 2/2017). For more detailed vegetation associations for individual soil series, please refer to 'VegSoilAssoc_SWLA.pdf' or 'VegSoilAssoc_SWLA.csv'.