This map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The basemap includes bathymetry, marine water body names, undersea feature names, and derived depth values in meters. Land features include administrative boundaries, cities, inland waters, roads, overlaid on land cover and shaded relief imagery.

The map was compiled from a variety of best available sources from several data providers, including General Bathymetric Chart of the Oceans GEBCO_08 Grid version 20100927 and IHO-IOC GEBCO Gazetteer of Undersea Feature Names August 2010 version (https://www.gebco.net), National Oceanic and Atmospheric Administration (NOAA) and National Geographic for the oceans; and DeLorme, HERE, and Esri for topographic content. The basemap was designed and developed by Esri.

The Ocean Basemap currently provides coverage for the world down to a scale of ~1:577k; coverage down to ~1:72k in United States coastal areas and various other areas; and coverage down to ~1:9k in limited regional areas. You can contribute your bathymetric data to this service and have it served by Esri for the benefit of the Ocean GIS community. For details, see the Community Maps Program.

Tip: Here are some famous oceanic locations as they appear in this map. Each URL below launches this map at a particular location via parameters specified in the URL: Challenger Deep, Galapagos Islands, Hawaiian Islands, Maldive Islands, Mariana Trench, Tahiti, Queen Charlotte Sound, Notre Dame Bay, Labrador Trough, New York Bight, Massachusetts Bay, Mississippi Sound

Updated quarterly, the Landgate Basemap is comprised of simplified cadastre, topographic and road centreline information, and is the perfect backdrop to provide context for projects that require commonly used underlying WA centric location information. The Landgate Basemap provides a stylized (familiar ‘StreetSmart’ style ) layout, current, geo-referenced and view only map base. This is a view only service (i.e no data download capability) and can be viewed in combination with Landgate’s other subscription datasets, SLIP public datasets and other geo-referenced data. Designed for use within GIS and online mapping applications, the tile cached Basemap service introduces faster panning and redrawing of location information commonly used across many sectors. Key information • WA centric basemap comprising commonly used Landgate location information • cached map tiles • ESRI cache map service and WMTS (web map tile service) - publishes in WGS84 only • Update cycle: quarterly • Coverage: whole of state (includes Christmas and Cocos Keeling Islands) • QGIS 2.18 minimum required for WMTS usage. © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions. For more information and access to Subscription Services contact Landgate's Business Sales and Service team. Email: BusinessSolutions@landgate.wa.gov.au Services Note, the following services require 3rd party software that supports OGC Standards and Esri services.

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.


Method:
The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (01-data/World_AIMS_Marine-satellite-imagery in the data download) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.


Single merged composite GeoTiff:
The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.


Source datasets:
Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302
Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT
This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose.
CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp
CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp
CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland
This is the high resolution imagery used to create the map of Mer.

World_AIMS_Marine-satellite-imagery
The base image composites used in this dataset were based on an early version of Lawrey, E., Hammerton, M. (2024). Marine satellite imagery test collections (AIMS) [Data set]. eAtlas. https://doi.org/10.26274/zq26-a956. A snapshot of the code at the time this dataset was developed is made available in the 01-data/World_AIMS_Marine-satellite-imagery folder of the download of this dataset.


Data Location:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.


Change Log:
2025-05-12: Eric Lawrey
Added Torres-Strait-Region-Map-Masig-Ugar-Erub-45k-A0 and Torres-Strait-Eastern-Region-Map-Landscape-A0. These maps have a brighten satellite imagery to allow easier reading of writing on the maps. They also include markers for geo-referencing the maps for digitisation.

2025-02-04: Eric Lawrey
Fixed up the reference to the World_AIMS_Marine-satellite-imagery dataset, clarifying where the source that was used in this dataset. Added ORCID and RORs to the record.

2023-11-22: Eric Lawrey
Added the data and maps for close up of Mer.
- 01-data/TS_DNRM_Mer-aerial-imagery/
- preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg
- exports/Torres-Strait-Mer-Map-Landscape-A0.pdf
Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

2023-03-02: Eric Lawrey
Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

Spatial dataset derived from many different open data repositories and cropped on the Omo-Turkana Basin boundary, used to create a base-map describing the components of Water-Energy-Food nexus in the case study. omo-turkana.gpkg: vector dataset including the following layers, together with the related map style for QGIS Desktop used in the DAFNE Geoportal basemap: basin: Hydrological basin of the Omo-Turkana river (case study boundary) subbasin: Basins of the main tributaries waterbodies: Natural lakes and reservoirs boundaries rivers: River network dams: Existing dams protected_areas: Protected areas aei_pct_cells: Area equipped for irrigation, expressed as percentage of total area. roads: Main roads network cities: Main cities in the riparian countries countries: Administrative borders of riparian countries markers: DAFNE model components location, with existing and planned dams and power plants, irrigation schemes, environmental target areas. zambezi_raster.zip: raster dataset including the following layers: srtm_90m: Digital Elevation Model Global Surface Water: change: Occurrence Change Intensity map provides information on where surface water occurrence increased, decreased or remained the same between 1984-1999 and 2000-2015 extent: Maximum Water Extent shows all the locations ever detected as water over period 1984-2015 occurence: Occurrence shows where surface water occurred between 1984 and 2015 and provides information concerning overall water dynamics. recurrence: Recurrence provides information concerning the inter-annual behaviour of water surfaces and captures the frequency with which water returns from year to year. seasonality: Seasonality map provides information concerning the intra-annual behaviour of water surfaces for a single year (2015) and shows permanent and seasonal water and the number of months water was present. transitions: Transitions map provides information on the change in surface water seasonality between the first and last years (between 1984 and 2015) and captures changes between the three classes of not water, seasonal water and permanent water. Original data sources include: AQUASTAT, the FAO global information system on water resources and agricultural water management; Natural Earth, a public domain map dataset available at different scales; Protected Planet, the most up to date and complete source of data on protected areas and other effective area-based conservation measures, maintained by UNEP-WCMC and IUCN; OpenStreetMap, a collaborative project to create a free editable map of the world; NASA's Shuttle Radar Topography Mission (SRTM) Digital Elevation Database; Global Water Surface, a virtual time machine that maps the location and temporal distribution of water surfaces at the global scale.

This vector tile layer presents the Nova style (World Edition) and provides a detailed basemap for the world, featuring a dark background with glowing blue symbology. The Nova map uses color scheme, with a grid pattern across the ocean and stripes or square stippled patterns for land use features visible at larger scales. The colors are reminiscent of science-fiction shows, where one is looking at a map of the world on a 'head's up' device or a map that would be projected from a transparent glass wall. Additional graphics in the oceans presents a futuristic user interface. The futuristic and less terrestrial feel theme continues with the geometric patterns, starburst city dot symbols, and cool color scheme. The fonts displayed are clean and squarish (san serif) with a futuristic, science-fiction, or high technology appearance. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.This layer is used in the Nova Map web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

The Canada Basemap Transportation (CBMT) is a raster tile service that provides spatial reference context with an emphasis on transportation networks across Canada. It is designed especially for use as a background layer in a web mapping application or geographic information system (GIS). Access: Access is free of charge under the terms of the Open Government Licence - Canada. Data Sources: Data for the CBMT is sourced from the following datasets: - Topographic data of Canada – CanVec Series - Official names from the Canadian Geographical Names Database (CGNDB). Projections: - Data is provided in the EPSG:3978 (NAD83 Canada Atlas Lambert) projected coordinate system. - Data is provided in the EPSG:3857 (WGS84 Pseudo-Mercator) projected coordinate system. Geographic Coverage: - The CBMT in the EPSG:3857 has complete coverage of the world, with full datasets in Canada and only partial data in other parts of the world including boundaries, Country Names, and major cities. - The CBMT in the EPSG:3978 covers the entire geographic area of Canada and some major transportation routes and cities in the northern States of the USA. Additional Versions: - The CBMT is available as a dynamic service (WMS) or a tiled service (ESRI REST and WMTS). - A geometry-only version (CBMT GEOM) and a text-only version (CBMT TXT) are available. - French versions of the basemap are accessible via the Carte de base du Canada - Transport (CBCT).

The Queensland basemap web services are designed for use as a background reference layers in applications where the user’s data will take preference.

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

drone Matteo

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This data contains an Index identifying Ontario Base Map map tiles.

Eastern and Southern Ontario is covered at a scale of 1:10,000. Northern Ontario is covered at a scale of 1:20,000.

We are no longer updating this data. It is best suited for historical research and analysis.

Status Completed: production of the data has been completed

Maintenance and Update Frequency Not planned: there are no plans to update the data

Contact Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

The datasets represent topographic description (cost and accessibility maps) of Khabarovsk and Primorsky Krais of the Russian Far East divided into unit areas with a 10x10 km grid in WGS84. The datasets are in MID/MIF formats to be processed in QGIS with use of self-written open source software. The datasets are used to model single or multiple socio-economic scenarios of regional spatial development and inter-regional economic cooperation.

The Digital Geologic-GIS Map of Santa Rosa Island, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (sris_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (sris_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (sris_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (sris_geology_metadata_faq.pdf). Please read the chis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (sris_geology_metadata.txt or sris_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Datasets- simple_land_cover1.tif - an example land cover dataset presented in Figures 1 and 2- simple_landform1.tif - an example landform dataset presented in Figures 1 and 2- landcover_europe.tif - a land cover dataset with nine categories for Europe - landcover_europe.qml - a QGIS color style for the landcover_europe.tif dataset- landform_europe.tif - a landform dataset with 17 categories for Europe - landform_europe.qml - a QGIS color style for the landform_europe.tif dataset- map1.gpkg - a map of LTs in Europe constructed using the INCOMA-based method- map1.qml - a QGIS color style for the map1.gpkg dataset- map2.gpkg - a map of LTs in Europe constructed using the COMA method to identify and delineate pattern types in each theme separately- map2.qml - a QGIS color style for the map2.gpkg dataset- map3.gpkg - a map of LTs in Europe constructed using the map overlay method- map3.qml - a QGIS color style for the map3.gpkg dataset

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

Vector tile card based on the Mabpbox Vector Tiles (mvt) format (EPSG:3857). Background card in variants color (standard), grey and available as QGIS style. Integration designed for Mapbox/Maplibre clients or QGIS (QGIS style).

This dataset shows the results of mapping the connectivity of key values (natural heritage, indigenous heritage, social and historic and economic) of the Great Barrier Reef with its neighbouring regions (Torres Strait, Coral Sea and Great Sandy Strait). The purpose of this mapping process was to identify values that need joint management across multiple regions. It contains a spreadsheet containing the connection information obtained from expert elicitation, all maps derived from this information and all GIS files needed to recreate these maps. This dataset contains the connection strength for 59 attributes of the values between 7 regions (GBR Far Northern, GBR Cairns-Cooktown, GBR Whitsunday-Townsville, GBR Mackay-Capricorn, Torres Strait, Coral Sea and Great Sandy Strait) based on expert opinion. Each connection is assessed based on its strength, mechanism and confidence. Where a connection was known to not exist between two regions then this was also explicitly recorded. A video tutorial on this dataset and its maps is available from https://vimeo.com/335053846.

Methods:

The information for the connectivity maps was gathered from experts (~30) during a 3-day workshop in August 2017. Experts were provided with a template containing a map of Queensland and the neighbouring seas, with an overlay of the regions of interest to assess the connectivity. These were Torres Strait, GBR:Far North Queensland, GBR:Cairns to Cooktown, GBC: Townsville to Whitsundays, GBR: Mackay to Capricorn Bunkers and Great Sandy Strait (which includes Hervey bay). A range of reference maps showing locations of the values were provided, where this information could be obtained. As well as the map the template provided 7x7 table for filling in the connectivity strength and connection type between all combinations of these regions. The experts self-organised into groups to discuss and complete the template for each attribute to be mapped. Each expert was asked to estimate the strength of connection between each region as well as the connection mechanism and their confidence in the information. Due to the limited workshop time the experts were asked to focus on initially recording the connections between the GBR and its neighbouring regions and not to worry about the internal connections in the GBR, or long-distance connections along the Queensland coast. In the second half of the workshop the experts were asked to review the maps created and expand on the connections to include those internal to the GBR. After the workshop an initial set of maps were produced and reviewed by the project team and a range of issues were identified and resolved. Additional connectivity maps for some attributes were prepared after the workshop by the subject experts within the project team. The data gathered from these templates was translated into a spreadsheet, then processing into the graphic maps using QGIS to present the connectivity information. The following are the value attributes where their connectivity was mapped: Seagrass meadows: pan-regional species (e.g. Halophila spp. and Halodule spp.) Seagrass meadows: tropical/sub-tropical (Cymodocea serrulata, Syringodium isoetifolium) Seagrass meadows: tropical (Thalassia, Cymodocea, Thalassodendron, Enhalus, Rotundata) Seagrass meadows: Zostera muelleri Mangroves & saltmarsh Hard corals Crustose coralline algae Macroalgae Crown of thorns starfish larval flow Acropora larval flow Casuarina equisetifolia & Pandanus tectorius Argusia argentia Pisonia grandis: cay vegetation Inter-reef gardens (sponges + gorgonians) (Incomplete) Halimeda Upwellings Pelagic foraging seabirds Inshore and offshore foraging seabirds Migratory shorebirds Ornate rock lobster Yellowfin tuna Black marlin Spanish mackerel Tiger shark Grey nurse shark Humpback whales Dugongs Green turtles Hawksbill turtles Loggerhead turtles Flatback turtles Longfin & Shortfin Eels Red-spot king prawn Brown tiger prawn Eastern king prawns Great White Shark Sandfish (H. scabra) Black teatfish (H. whitmaei) Location of sea country Tangible cultural resources Location of place attachment Location of historic shipwrecks Location of places of social significance Location of commercial fishing activity Location of recreational use Location of tourism destinations Australian blacktip shark (C. tilstoni) Barramundi Common black tip shark (C. limbatus) Dogtooth tuna Grey mackerel Mud crab Coral trout (Plectropomus laevis) Coral trout (Plectropomus leopardus) Red throat emperor Reef manta Saucer scallop (Ylistrum balloti) Bull shark Grey reef shark

Limitations of the data:

The connectivity information in this dataset is only rough in nature, capturing the interconnections between 7 regions. The connectivity data is based on expert elicitation and so is limited by the knowledge of the experts that were available for the workshop. In most cases the experts had sufficient knowledge to create robust maps. There were however some cases where the knowledge of the participants was limited, or the available scientific knowledge on the topic was limited (particularly for the ‘inter-reefal gardens’ attribute) or the exact meaning of the value attribute was poorly understood or could not be agreed up on (particularly for the social and indigenous heritage maps). This information was noted with the maps. These connectivity maps should be considered as an initial assessment of the connections between each of the regions and should not be used as authoritative maps without consulting with additional sources of information. Each of the connectivity links between regions was recorded with a level of confidence, however these were self-reported, and each assessment was performed relatively quickly, with little time for reflection or review of all the available evidence. It is likely that in many cases the experts tended to have a bias to mark links with strong confidence. During subsequent revisions of some maps there were substantial corrections and adjustments even for connections with a strong confidence, indicating that there could be significant errors in the maps where the experts were not available for subsequent revisions. Each of the maps were reviewed by several project team members with broad general knowledge. Not all connection combinations were captured in this process due to the limited expert time available. A focus was made on capturing the connections between the GBR and its neighbouring regions. Where additional time was available the connections within 4 regions in the GBR was also captured. The connectivity maps only show connections between immediately neighbouring regions, not far connections such as between Torres Strait and Great Sandy Strait. In some cases the connection information for longer distances was recorded from the experts but not used in the mapping process. The coastline polygon and the region boundaries in the maps are not spatially accurate. They were simplified to make the maps more diagrammatic. This was done to reduce the chance of misinterpreting the connection arrows on the map as being spatially explicit.

Format:

This dataset is made up of a spreadsheet that contains all the connectivity information recorded from the expert elicitation and all the GIS files needed to recreate the generated maps.

original/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_Master_v2018-09-05.xlsx: ‘Values connectivity’: This sheet contains the raw connectivity codes transcribed from the templates produced prepared by the subject experts. This is the master copy of the connection information. Subsequent sheets in the spreadsheet are derived using formulas from this table. 1-Vertical-data: This is a transformation of the ‘Values connectivity’ sheet so that each source and destination connection is represented as a single row. This also has the connection mechanism codes split into individual columns to allow easier processing in the map generation. This sheet pulls in the spatial information for the arrows on the maps (‘LinkGeom’ attribute) or crosses that represent no connections (‘NoLinkGeom’) using lookup tables from the ‘Arrow-Geom-LUT’ and ‘NoConnection-Geom-LUT’ sheets. 2.Point-extract: This contains all the ‘no connection’ points from the ‘Values connectivity’ dataset. This was saved as working/ GBR_NESP-TWQ-3-3-3_Seascape-connectivity_no-con-pt.csv and used by the QGIS maps to draw all the crosses on the maps. This table is created by copy and pasting (values only) the ‘1-Vertical-data’ sheet when the ‘NoLinkGeom’ attribute is used to filter out all line features, by unchecking blank rows in the ‘NoLinkGeom’ filter. 2.Line-extract: This contains all the ‘connections’ between regions from the ‘Values connectivity’ dataset. This was saved as working/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_arrows.csv and used by the QGIS maps to draw all the arrows on the maps. This table is created by copy and pasting (values only) the ‘1-Vertical-data’ sheet when the ‘LinkGeom’ attribute is used to filter out all point features, by unchecking blank rows in the ‘LinkGeom’ filter. Map-Atlas-Settings: This contains the metadata for each of the maps generated by QGIS. This sheet was exported as working/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_map-atlas-settings.csv and used by QGIS to drive its Atlas feature to generate one map per row of this table. The AttribID is used to enable and disable the appropriate connections on the map being generated. The WKT attribute (Well Known Text) determines the bounding box of the map to be generated and the other attributes are used to display text on the map. map-image-metadata: This table contains metadata descriptions for each of the value attribute maps. This metadata was exported as a CSV and saved into the final generated JPEG maps using the eAtlas Image Metadata Editor Application

Die österreichische Basemap, wie man sie noch nie zuvor gesehen hat! Ein QGIS-Projekt, welches nur aus Onlinequellen ein optisch ansprechendes Kartenbild der Basemap, mit Höhenschichtlinien und optionaler Visualisierung von Gelände- und Oberflächendetails in verschiedenen Stilen ermöglicht. Wanderrouten können über waymarkedtrails.org eingebunden werden. Die Darstellung funktioniert auch mit QFIELD auf Mobilgeräten. Anleitung (Links zu den Anwendungen und QGIS-Projektdatei in der Info): 1.) QGIS/QFIELD Anwendung herunterladen und installieren 2.) QGIS-Projektdatei "Basemap Online.qgz" herunterladen und mit QGIS/QFIELD öffnen 3.) Gewünschten Kartenstil in der QGIS/QFIELD Layeransicht anpassen (siehe Screenshots) 4.) Bei eingeschränkter Bandbreite (z.B. Mobilgerät) die Wanderweg- sowie Orthofoto-Optionen ausschalten und in den Kartenstil-Optionen "geglättet (Landkartenmodus)" wählen. Viel Spaß damit! Durch Kombination fast aller verfügbaren Offline-Datenquellen österreichischer Geoinformationen mit OpenStreetMap entstehen die noch detaillierteren Karten für den YouTube-Kanal. Details siehe https://www.data.gv.at/katalog/application/97679bfa-9f28-44c3-9347-8df341502e0e

Soil-geomorphological mapping is a reliable tool for analyzing the patterns of soil distribution in various parts of Earth’s surface. Cryogenic and watershed areas are the most dynamic landscapes with relatively rapid transformation under the influence of climate change and river activity. The soil-geomorphological map obtained by unmanned aerial vehicle (UAV) imaging, classical soil sections, geomorphological observation, and determination of the main chemical parameters of soils are presented. Mapping of the spatial distribution was performed using QGIS 3.22, SAGA GIS 7.9.1, and ArcGIS 10.6 software. The investigation of soil cover was performed according to WRB soil classification. From the obtained data, four types of soils were identified due to their position in the relief and chemical parameters. The dominant soil type is Folic Cryosol (Siltic) (41.1%) which is formed on the periglacial landscape of wet polygons on Samoylov Island. The application of high-resolution UAV imaging to construct soil-geomorphological maps is the most relevant method for analyzing soils formed in cryogenic, watershed, and mountainous landscapes. Based on SOC distribution, it was found that the highest SOC content corresponds to Holocene terrace (Cryosol and Histosol soil types), in areas that are not subject to the flooding process. According to the analysis of the chemical composition of soils, it was found that the main elements accumulating in the soil are SiO2, Al2O3, CaO, and K2O, which have a river origin. The soil-geomorphological maps can be used to analyze the reserves and contents of organic and inorganic components with high accuracy.