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 (not yet published) 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.

Change Log: 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.

22 Nov 2023: 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.

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.

Marine satellite imagery (Sentinel 2 and Landsat 8) (AIMS), https://eatlas.org.au/data/uuid/5d67aa4d-a983-45d0-8cc1-187596fa9c0c - World_AIMS_Marine-satellite-imagery

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.

AbstractCoastline for Antarctica created from various mapping and remote sensing sources, consisting of the following coast types: ice coastline, rock coastline, grounding line, ice shelf and front, ice rumple, and rock against ice shelf. Covering all land and ice shelves south of 60°S. Suitable for topographic mapping and analysis. This dataset has been generalised from the high resolution vector polyline. Medium resolution versions of ADD data are suitable for scales smaller than 1:1,000,000, although certain regions will appear more detailed than others due to variable data availability and coastline characteristics.Changes in v7.10 include updates to the coastline of Alexander Island and surrounding islands, and the ice shelf fronts of the Wilkins and Brunt ice shelves.Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.Further information and useful linksMap projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap.The currency of this dataset is November 2024 and will be reviewed every 6 months. This feature layer will always reflect the most recent version.For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue.A related high resolution dataset is also published via Living Atlas, as well medium and high resolution polygon datasets.For background information on the ADD project, please see the British Antarctic Survey ADD project page.LineageDataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each line has attributes detailing the source which can give the user further indications of its suitability for specific uses. Attributes also give information including 'surface' (e.g. grounding line, ice coastline, ice shelf front) and revision date. Compiled from sources ranging in time from 1990s-2024 - individual lines contain exact source dates. This medium resolution version has been generalised from the high resolution version. All areas <0.1km² not intersecting anything else were deleted and the ‘simplify’ tool was used in ArcGIS with the ‘retain critical points’ algorithm and a smoothing tolerance of 50 m.CitationGerrish, L., Ireland, L., Fretwell, P., & Cooper, P. (2024). Medium resolution vector polylines of the Antarctic coastline (Version 7.10) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/bc81931c-4e8e-439a-b3c9-d3d1fdb109dfIf using for a graphic or if short on space, please cite as 'data from the SCAR Antarctic Digital Database, 2024'

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

Coastline for Antarctica created from various mapping and remote sensing sources, consisting of the following coast types: ice coastline, rock coastline, grounding line, ice shelf and front, ice rumple, and rock against ice shelf. Covering all land and ice shelves south of 60°S. Suitable for topographic mapping and analysis. High resolution versions of ADD data are suitable for scales larger than 1:1,000,000. The largest suitable scale is changeable and dependent on the region.

Major changes in v7.5 include updates to ice shelf fronts in the following regions: Seal Nunataks and Scar Inlet region, the Ronne-Filchner Ice Shelf, between the Brunt Ice Shelf and Riiser-Larsen Peninsula, the Shackleton and Conger ice shelves, and Crosson, Thwaites and Pine Island. Small areas of grounding line and ice coastlines were also updated in some of these regions as needed.

Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.

Further information and useful links

Map projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap.

The currency of this dataset is May 2022 and will be reviewed every 6 months. This feature layer will always reflect the most recent version.

For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue.

A related medium resolution dataset is also published via Living Atlas, as well medium and high resolution polygon datasets.

For background information on the ADD project, please see the British Antarctic Survey ADD project page.

Lineage

Dataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each line has attributes detailing the source which can give the user further indications of its suitability for specific uses. Attributes also give information including 'surface' (e.g. grounding line, ice coastline, ice shelf front) and revision date. Compiled from sources ranging in time from 1990s-2022 - individual lines contain exact source dates.

The Science Case in the Caribbean region presents records on landslides, precipitation, maps used as inputs of hazard models and drone imagery over the region of interest.
For the Carribean study-case, an analysis of open and proprietary satellite based dataset was used to facilitate the setup and evaluation of physically-based multi-hazard models. These allow for qualification and quantification of spatio-temporal multi-hazard patterns. These form a crucial input into the general hazard and risk assessment workflow.

Presented here are the datasets employed for Case Study 4 in Deliverable D3.1 with a short description, produced and saved within the following folders:

Dominica_landslide: the landslides datasets mapped by ITC using high-resolution satellite imagery. It is intended to calibrate and validate the flood and landslide modelling. The folder contains four shapefiles:

· Landslide_Part.shp - Shapefile containing landslide extent, flash flood extents, and their attributes.

· Cloud.shp – Shapefile represents the cloud-filled areas in the satellite imagery where no mapping was possible.

· The other two shapefiles are self-explanatory.

GPM_Maria: NASA Global Precipitation Mission (GPM) precipitation maps processed for model input in LISEM. GPM is a hybrid fusion with satellite datasets for precipitation estimates. Mean as input data to represent precipitation in the landslide and flood modelling.

Maps_Models_Input : Soil and land use and channels, lots of custom work, SOILGRIDS, and SPOT image classification; all the datasets are ready for model input for OpenLISEM and LISEM Hazard or FastFlood. The dataset is meant to calibrate and validate the flood and landslide modelling.

The raster files are either in Geotiff format or PCraster map format. Both can be opened by GIS systems such as GDAL or QGIS. The projection of each file is in UTM20N.

Some key files are:

• dem.map -elevation model, the height of the landscape in meters above sea level.
• lai.map - leaf area index, estimated using empirical relationships based on NDVI (Normalized Difference Vegetation Index)). The source data to calculate NDVI is Sentinel-2.
• KSat.map - Saturated hydraulic conductivity of the soil, estimated based on a combination of SOILGRIDS soil texture, Saxton et al. (2006) Pedotransfer functions, and a national soil map for Dominica.
• clay.map - Clay texture fraction, SoilGrids resampling
• silt.map - Silt texture fraction, SoilGrids resampling
• sand.map - Sand texture fraction, SoilGrids resampling
• cover.map - Vegetation cover as a fraction, estimated using linear correlation with NDVI.
• lu_new.map - Spot satellite image classification at 10 meters resolution for predominant land use types.
• n.map - Mannings surface roughness coefficient, specific value based on the land use type.
• ndvi.map - Normalized Differential Vegetation Index, based on Sentinel-2 images in summer.
• ldd.map - Drainage network map for the island, which can be used for flow accumulation and streamflow detection
• catchments.map - Catchment ID's based on the ldd.map drainage network.
• Channelldd.map - Channel-only drainage network map, calibrated manually to have all channels on the island represented correctly.
• Soildepth - Soil depth in meters, based on a physically-based soil depth model in meters and observational data obtained from landslide-sites during fieldwork in 2018.
• Slope.map - Slope map in gradient of the elevation model (m/m) in the steepest direction

StakeholderQuestionnaire_Survey_ITC: The stakeholder questionnaires particularly relating to the tools developed partly by this project on rapid hazard modelling. Stakeholder Engagement survey and Stakeholder Survey Results prepared and implemented by Sruthie Rajendran as part of her MSc Thesis Twin Framework For Decision Support In Flood Risk Management supervised by Dr. M.N. Koeva (Mila) and Dr. B. van den Bout (Bastian) submitted in July 2024.

·Drone_Images_ 2024: Images captured using a DJI drone of part of the Study area in February 2024. The file comprises three different regions: Coulibistrie, Pichelin and Point Michel. The 3D models for Coulibistrie were generated from the nadir drone images using photogrammetric techniques employed by the software Pix4D. The image Coordinate System is WGS 84 (EGM 96 Geoid0), but the Output Coordinate System of the 3D model is WGS 84 / UTM zone 20N (EGM 96 Geoid). The other two folders contain only the drone images captured for that particular region's Pichelin and Point Michel. The dataset is used with other datasets to prepare and create the digital twin framework tailored for flood risk management in the study area.

AbstractCoastline for Antarctica created from various mapping and remote sensing sources, provided as polygons with ‘land’, ‘ice shelf’, ‘ice tongue’ or ‘rumple’ attribute. Covering all land and ice shelves south of 60°S. Suitable for topographic mapping and analysis. This dataset has been generalised from the high resolution vector polygons. Medium resolution versions of ADD data are suitable for scales smaller than 1:1,000,000, although certain regions will appear more detailed than others due to variable data availability and coastline characteristics.Changes in v7.10 include updates to the coastline of Alexander Island and surrounding islands, and the ice shelf fronts of the Wilkins and Brunt ice shelves.Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.Further information and useful linksMap projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap.The currency of this dataset is November 2024 and will be reviewed every 6 months. This feature layer will always reflect the most recent version.For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue.A related high resolution dataset is also published via Living Atlas, as well medium and high resolution line datasets.For background information on the ADD project, please see the British Antarctic Survey ADD project page.LineageDataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each polygon contains a ‘surface’ attribute with either ‘land’, ‘ice shelf’, ‘ice tongue’ or ‘rumple’. Details of when and how each line was created can be found in the attributes of the high or medium resolution polyline coastline dataset. Data sources range in time from 1990s-2024 - individual lines contain exact source dates. This medium resolution version has been generalised from the high resolution version. All polygons <0.1km² not intersecting anything else were deleted and the ‘simplify’ tool was used in ArcGIS with the ‘retain critical points’ algorithm and a smoothing tolerance of 50 m.CitationGerrish, L., Ireland, L., Fretwell, P., & Cooper, P. (2024). Medium resolution vector polygons of the Antarctic coastline (Version 7.10) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/93ac35af-9ec7-4594-9aaa-0760a2b289d5If using for a graphic or if short on space, please cite as 'data from the SCAR Antarctic Digital Database, 2024'

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

The Heard Island and McDonald Island management maps and polygon datasets stored in a Quantum Geographical Information System (QGIS) GeoPackage format. The four maps were developed to update the management zones figures in accordance with the Heard Island and McDonald Island Marine Research Management Plan, 2014-2024.

The unaltered datasets used included the ANARE historical Sites, countours_terrasar_100m_draft, flyingbird_pt_heard_pt_0304, sealer historical sites and WATERCOURSE_LN_heard.

Several of the older datasets were updated or edited these include; the Antarctic prion nests and HI_South Georgian diving petrel 2003-4, which were adapted from the FLYING_BIRD_PY_heard dataset. The Current buildings on HI_2001 and HI_Refuge_operational datasets were adapted from the heard_infrastructure dataset. The ShagIsland_Sail_DruryRocks was adapted from the 2009 DEM. Alert Island dataset was digitised from a panchromatic Digital Globe Worldview-1 satellite imagery acquired on 23 March 2008.

Additional datasets were created for this project including – HI glaciers 2014; HI_Coastline_2014; HI_Lagoons2014; and the HI_Vegetation Zone 2014; all of which were digitised from a pansharpened image derived from multispectral and panchromatic Digital Globe GeoEye-1 satellite imagery acquired on 6 February 2014. The HI glaciers 2014 dataset is an estimate of glacier coverage of the island in 2014 (some semi-permeant snow areas may be included, therefore should not be used to calculate total glacier area of the island). The HI_Vegetation Zone 2014 was created using the False Colour (FC) images created by Digital Globe from the pansharpened image derived from multispectral and panchromatic Digital Globe GeoEye-1 satellite imagery, which was used to estimate the vegetation coverage of the island. The vegetation zone dataset should not be used to calculate total vegetation coverage of the island.

HI_LongBeach_Macaroni_Colony_2012-2016 was digitised from three pansharpened images derived from multispectral and panchromatic two GeoEye-1 satellite imageries acquired on 2 February 2012 and 6 February 2014 and Worldview-2 imagery acquired on 21 February 2016.

The HI_Heritage_Zone_2021; HI_MainUseZone_2021; HI_RestrictedZone_2021; and HI-VisitorAccessZone_2021 were all digitised based on the areas as defined in the 2014-224 Management Plan and altered to fit the new 2014 coastline. HI_WildernessZone_2014 is a duplicate of the HI_Coastline_2014 with the symbology altered to reflect the Wilderness Zone as defined in the Management Plan 2014-2024.

The McDonald Island coastline for 1980 (McD_1980_coastline) was digitised from the georeferenced 1980 aerial photo casa9491 image; the McD_2003_coastline was digitised from a pansharpened image derived from multispectral and panchromatic Digital Globe Quickbird satellite imagery acquired on 9 April 2003; the McD_coastline_2012 was digitised from a pansharpened image derived from multispectral and panchromatic Digital Globe GeoEye-1 satellite imagery acquired on 19 May 2012 and the McD_RestrictedZone_2022 was digitised from a pansharpened image derived from multispectral and panchromatic Maxar Worldview-3 satellite imagery acquired on 25 June 2020. As it encompasses the entire island it is also the 2020 coastline for McDonald Island.

This project contains the following unaltered files:
ANARE historical Sites
countours_terrasar_100m_draft
flyingbird_pt_heard_pt_0304
sealer historical sites
WATERCOURSE_LN_heard

This project contains the following altered/edited files:
Antarctic prion nests
HI_South Georgian diving petrel 2003-4
Current buildings on HI_2001
HI_Refuge_operational
ShagIsland_Sail_DruryRocks
Alert Island 2008

This project contains the following new files:
HI glaciers 2014
HI_Coastline_2014
HI_Lagoons2014
HI_Vegetation Zone 2014
HI_LongBeach_Macaroni_Colony_2012-2016
HI_Heritage_Zone_2021
HI_MainUseZone_2021
HI_RestrictedZone_2021
HI-VisitorAccessZone_2021
HI_WildernessZone_2014

McD_1980_coastline
McD_2003_coastline
McD_coastline_2012
McD_RestrictedZone_2022 (same as McD_coastline_2020)

AbstractCoastline for Antarctica created from various mapping and remote sensing sources, provided as polygons with 'land', 'ice shelf', 'ice tongue' or 'rumple' attribute. Covering all land and ice shelves south of 60°S. Suitable for topographic mapping and analysis. High resolution versions of ADD data are suitable for scales larger than 1:1,000,000. The largest suitable scale is changeable and dependent on the region.Changes in v7.10 include updates to the coastline of Alexander Island and surrounding islands, and the ice shelf fronts of the Wilkins and Brunt ice shelves.Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.Further information and useful linksMap projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap.The currency of this dataset is November 2024 and will be reviewed every 6 months. This feature layer will always reflect the most recent version.For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue.A related medium resolution dataset is also published via Living Atlas, as well medium and high resolution line datasets.For background information on the ADD project, please see the British Antarctic Survey ADD project page.LineageDataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each polygon contains a 'surface' attribute with either 'land', 'ice shelf', 'ice tongue' or 'rumple'. Details of when and how each line was created can be found in the attributes of the high resolution polyline coastline dataset. Data sources range in time from 1990s-2024 - individual lines contain exact source dates.CitationGerrish, L., Ireland, L., Fretwell, P., & Cooper, P. (2024). High resolution vector polygons of the Antarctic coastline (Version 7.10) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/4ecd795d-e038-412f-b430-251b33fc880eIf using for a graphic or if short on space, please cite as 'data from the SCAR Antarctic Digital Database, 2024'

This dataset consists of unprocessed images and orthomosaic imagery of a barley field in Bozeman, Montana, collected throughout the growing season from emergence to maturity. The orthomosaics were used to develop an open-source workflow for extracting quantitative values from individual plots for downstream analysis of plant traits. This field exemplifies a challenge for plot extraction, as plots were planted with no border rows or alleys. Methods

UAV Imagery Collection:

Data was collected using a Mavic 2 Pro drone with the integrated Hasselblad L1D-20C RGB camera at an altitude of 90 feet (27.4 m). Flights were conducted over a barley field located west of Bozeman Montana (45.676415, -111.149092). DJI GS Pro software was used on an iPad mini to create an automated flight path for imagery capture.  Images were collected while hovering to minimize blurring and captured with 70% overlap along the flight path and 70% overlap between flight passes. Weather permitting, flights were timed as close to 10:00 am or 2:00 pm as possible.

Date Number of Images Time of Flight Notes

June 16 37 10.38

June 21 49 11:27 Increased number of passes for better stitching of edge plots.

June 24 49 10:45

July 01 49 10:16

July 12 59 09:51

One-the-fly flight plan due to hardware issues.

July 15 49 9:09

July 19 48 11:20

July 25 49 14:04

July 27 49 14:07

August 5 48 10:20

August 8 48 14:44

OpenDroneMap was used to stitch images together and create an orthomosaic of each flight. Parameters were default except for the following arguments:

min-num-features: 4000, max-concurrency: 6, skip-3dmodel: TRUE, fast-orthophoto: TRUE, crop: 0, texturing-outlier-removal-type: gauss_damping, orthophoto-resolution: 0.125, orthophoto-compression: NONE

The minimum number of features defines the number of tie points needed to stitch each pair of images. ‘min-num-features’ was lowered from the default 8000 to 4000 to ease processing time and memory load. ‘max-concurrency’ allocates CPU cores to the stitching project. ‘skip-3Dmodel’ and ‘fast-orthophoto’ keep the stitching procedure from creating undesired files like a 3D model and digital elevation model (DEM). ‘crop’ and ‘orthophoto-compression’ maintain the imagery quality, so nothing was cropped or down sampled. ‘texturing-outlier-removal’ defines how moving objects are processed and the option ‘gauss-damping’ was chosen because it is a less aggressive approach that prioritizes images that do not include the moving object. In this image set, there were no moving objects. ‘orthophoto- resolution’ defines the final resolution of the image. A value of 0.125 was selected for this dataset as a conservative estimate of the true resolution collected by the sensor.

Field Operations:

The field was planted on April 26th, 2022, with spring barley from the S2MET population. Aggregated by Neyhart et. al. 2019, the S2MET barley population provides a representation of high-performance barley from around the United States, selected to be grown across many environments to study genotype-by-environment interactions. Lines were planted in an augmented block design including 12 blocks and four control varieties planted across all blocks. These control varieties were selected as common high-performing barley lines in the Montana region: Odyssey, Lavina, Merit 57, and Hockett. All other lines were planted once. Planting was conducted with a 6-row planter, planting two 3-row plots simultaneously in a North-South orientation. In total, 23-24 plots were planted per block, for a total of 282 plots. After emergence alleys were cut East-West to distinguish plots more easily. 

Data Processing:

This dataset was used to develop an analysis workflow using QGIS and R. After stitching, imagery was loaded into QGIS. First, each image was georeferenced to the flight on June 16th using the 6 ground control points laid out over the extent of the field. Further, each band was calibrated relative to the June 16th flight image using the reflectance calibration pad (Micasense, panel serial number RP02-1622081-SC).

Once georeferenced and calibrated, plants were extracted from each image using the excess greenness index threshold (2 * Green) – Red – Blue). Next, plots were defined through a user-defined line grid overlay that was then translated into a polygon shapefile. This overlay was used to extract digital number statistics in each band, for every plot, on each flight date.

References:

Neyhart, J.L., Sweeney, D., Sorrells, M., Kapp, C., Kephart, K.D., Sherman, J., Stockinger, E.J., Fisk, S., Hayes, P., Daba, S., Mohammadi, M., Hughes, N., Lukens, L., Barrios, P.G., Gutiérrez, L. and Smith, K.P. (2019), Registration of the S2MET Barley Mapping Population for Multi-Environment Genomewide Selection. Journal of Plant Registrations, 13: 270-280. https://doi.org/10.3198/jpr2018.06.0037crmp

Thermokarst lagoons develop in permafrost lowlands along the ice-rich Arctic coast when thermokarst lakes or basins with bottom elevations at or below sea level are breached by the sea due to erosion, sea-level rise, or connection via drainage channels. Thermokarst lagoons, as dynamic landforms at the interface of terrestrial permafrost and marine systems, play a crucial role in the transformation of permafrost carbon under rising marine influence. Here we present a comprehensive dataset consisting of the first manual thermokarst lagoon area mapping, a more precise number of thermokarst lagoons and a detailed lagoon classification for thermokarst lagoons along the pan-Arctic coast from Taymyr Peninsula in Russia to the Tuktoyaktuk Peninsula in Canada.\r \r This is an updated dataset based on the previous work of Jenrich et al. 2021 and Jenrich et al. 2023. The main improvements include (1) counting thermokarst lagoons individually within a lagoon system, as long as the distinct round form of former lake basins is visible; (2) manually calculating the area for all mapped thermokarst lagoons based on the updated Global Surface Water Dataset from 1984-2021 by Pekel et al., 2016; and (3) classifying lagoons based on connectivity to the sea into 5 connectivity classes. We identified 520 thermokarst lagoons covering an area of 3457 km2.\r \r Methods: Pan-Arctic thermokarst lagoon distribution and area were mapped using QGIS version 3.34 and Google Earth Engine. The updated Global Surface Water Dataset by Pekel et al., 2016, based on Landsat-5, -7, and -8 satellite images from 1984-2021, was used to create masks with a threshold of >75% based on water occurrence, which enabled the manual splitting of polygons from the resulting mask vector data and extraction of thermokarst lagoon areas. Mapping and area extraction also relied on Sentinel-2 imagery from 2023/07/01-2023/08/30, basemaps Google Satellite and ESRI Satellite, and the digital elevation model ArcticDEM and its hillshade HSarcticDEM (Porter et al., 2018). The thermokarst lagoon classification employed a geomorphological approach based on Sentinel-2 imagery and basemaps Google Satellite and ESRI Satellite. Connectivity classes were visually defined and attributed to thermokarst lagoons based on: 1) the size of the lagoon opening relative to its overall size, 2) whether it was directly connected or subsequent within a lagoon system, and 3) interactivity within the lagoon system. The five classes range from 5 - very high connectivity to 1 - very low connectivity:

• 5 - Lagoon, always open (Lao) - Very high connectivity - Lagoon in direct exchange with the sea.
• 4 - Lagoon, mostly open (Lmo) - High connectivity - Barrier islands or sand spits only slightly block exchange with the sea or subsequent lagoon which is very well connected to the primary lagoon.
• 3 - Lagoon, semi-open (Lso) - Medium connectivity - Exchange limited either temporally or spatially due to barrier islands and sand spits or subsequent lagoon which is well connected to the primary lagoon.
• 2 - Lagoon, limited open (Llo) - Low connectivity - Exchange very limited due to very small opening or narrow channel or subsequent lagoon which is less connected to primary lagoon due to small channel or high distance.
• 1 - Lagoon, nearly-closed (Lnc) - Very low connectivity - Exchange strongly limited due to long and narrow channel, or subsequent lagoon with very limited exchange. Temporary lake characteristics are possible.

This dataset quantifies the extent and annual rate of change in surface water area (SWA) in India's rivers and basins over a period of 30 years from 1991 to 2020. Visit Surface Water Trends - India for an interactive web interface to explore these results, and for additional data and information.

It is derived from the Global Surface Water Explorer which maps terrestrial surface water globally using historical Landsat satellite imagery. (Pekel, J. et al., Nature 540, 418-422 (2016). (doi:10.1038/nature20584)). The data files contain zipped archives of shapefiles and CSV (comma separated values) files.

Shapefiles are one for each season (dry, wet and permanent) and scale (river basin and reach) of our analysis, and contain annual trends in surface water area. To open and explore them in a GIS software (eg. QGIS), un-ZIP them and include them as vector datasets.

CSV files are one for each scale (river basin and reach (transect)) of our analysis, and contain time series of surface water areas from 1991 to 2020. To open and explore them, for analysis or to explore in a table editing software, un-ZIP them and read them in.

Refer to 00_README.txt for details on feature and table attributes in the files.

Global trends in marine turtle nesting numbers vary by region, influenced by environmental or anthropogenic factors. Our study investigates the potential role of past temperature fluctuations on these trends, particularly whether warmer beaches are linked to increased nesting due to higher female production (since sea turtles have temperature-dependent sex determination). We selected the loggerhead turtle (Caretta caretta) due to its wide distribution, strong philopatry, and vulnerability to environmental changes. We compiled nest counts per year on 35 globally significant rookeries, analysing trends at regional and individual beach levels. We compiled air (CHELSA) and land surface (MODIS) temperature datasets spanning the last four decades (1979-2023) for each location. To analyse temporal trends in nest counts and temperatures, we used generalised additive models and Mann-Kendall trend tests. Additionally, we correlated nest counts with lagged air temperature variables. We found signi..., , , # The effects of warming on loggerhead turtle nesting counts

https://doi.org/10.5061/dryad.b8gtht7n5

Description of the data and file structure

This dataset contains digitised polygons representing loggerhead turtle nesting beaches across the globe. The data were obtained by delineating polygons using Google satellite imagery within QGIS 3.32.2 (QGIS Development Team, 2020). Spatial-temporal fluctuations of nest counts across nesting grounds were analyzed using the Mann-Kendall trend analysis (Kendall, 1990) with the ‘MannKendall’ function in the R package ‘Kendall’. The dataset does not include locations of nests, and the polygons represent generalised beach boundaries** **and no precise nesting locations or coordinates have been included.

File structure

Shapefile: nestingbeaches_GCB.shp

• Code: Individual polygon code representing one beach.
• Beach Name: Name of the beach, when available.
• Country: Coun...

This dataset compiles georeferenced media - including videos (480), articles (20), and datasets (6) - specifically curated to facilitate the understanding of reef habitats across northern Australia. It was designed as a research tool for virtual fieldwork with a particular focus on identifying sources of information that allow an understanding of both inshore and offshore reef environments. This dataset provides a record of the literature and media that was reviewed as part of mapping the reef boundaries from remote sensing as part of project NESP MaC 3.17.

This dataset only focuses on media that is useful for understanding shallow reef habitats. It includes videos of snorkelling, diving, spearfishing, and aerial drone imagery. It includes websites, books and journal papers that talk about the structure of reefs and datasets that provide fine scale benthic mapping.

This dataset is likely to not comprehensive. While considerable time was put into collecting relevant media, finding all available information sources is very difficult and time consuming.

A relatively comprehensive search was conducted on: - AIMS Metadata catalogue for benthic habitat mapping with tow videos and BRUVS - A review of the eAtlas for benthic habitat mapping - YouTube searches for video media of fishing, cruises, snorkelling of many named locations. The dataset is far less comprehensive on existing literature from journals, reports and dataset.

As the NESP MaC 3.17 project progresses we will continue to expand the dataset.

Changelog:

Changes made to the dataset will be noted in the change log and indicated in the dataset via the 'Revision' date. 1st Ed. - 2024-04-10 - Initial release of the dataset

Methods:

Identifying media - YouTube videos The initial discovery of videos for a given area was achieved by searching for place names in YouTube search using terms such as diving, snorkeling or spearfishing combined with the location name.

Each potential video was reviewed to:

1. Determine if the video had any visual content that would useful for understanding the marine environment.
2. Determine if the footage could be georeferenced to a specific location, the more specific the better.

In cases where the YouTube channel was making travel videos that were of a high quality, then all the relevant videos in that channel were reviewed. A high proportion of the most useful videos were found using this technique.

The most useful videos were those that had named specific locations (typically in their title or description) and contained drone footage and underwater footage. The drone footage would often show enough of the landscape for features to be matched with satellite imagery allowing precise geolocation of the imagery.

To minimise the time required to find relevant videos, the scrubbing feature on YouTube was used to allow the timeline of the video to be quickly reviewed for relevant scenes. The scrubbing feature shows a very quick, but low resolution version of the video as the cursor is moved along the video timeline. This scrubbing was used to quickly look through the videos for any scenes that contained drone footage, for underwater footage. This was particularly useful for travel videos that contained significant footage of overland travel mixed in with boating or shoreline activities. It was also useful for fishing videos where all the fishing activities could be quickly skipped over to focus on any available drone footage or underwater footage from snorkeling or spearfishing.

Where a video lacked direct clues to the location (such as in the title), but the footage contained particularly relevant and useful footage, additional effort was made listen to the conversations and other footage in the videos for additional clues. This includes people in the video talking about the names of locations, or any marine charts in the footage, or previous and proceeding scenes, where the location could be determined, adding constraints to the location of the relevant scene. Where the footage could not be precisely determine, but the footage was still useful then it was added to a video playlist for the region.

In many remote locations there were so few videos that the bar for including the videos was quite low as these videos would at least provide some general indication of the landscape.

When on PC, Google Maps was used to look up locations and act as reference satellite imagery for locating places, QGIS was used to record the polygons of locations and YouTube in a browser was used for video review.

YouTube Playlists: The initial collection of videos were compiled into YouTube playlists corresponding to relatively large regions. Using playlists was the most convenient way to record useful videos when viewing YouTube from an iPad. This compilation was done prior to the setup of this dataset.

Localising Playlists: For YouTube playlists the region digitised was based on the region represented by the playlist name and the collection of videos. Google maps was used to help determine the locations of each region. Where a particularly useful video is found in one of the playlists and its location can be determined accurately then this video was entered into this database as an individual video with its own finer scale mapping. However this process of migrating the videos from the playlists to more highly georeferenced individual videos in the dataset is incomplete.

The playlists are really a catch-all for potentially useful videos.

Localising individual videos: Candidate videos were quickly assessed for likely usefulness by reviewing the title and quickly scrubbing through the video looking for any marine footage, in water or as drone footage. If a video had a useful section then the focus was to determine the location of that part of the footage as accurately as possible. This was done by searching for locations listed in the title, chapter markers, video description, or mentions in video. These were then looked up in Google Maps. In general we would start with any drone footage that shows a large area with distinct features that could be matched with satellite imagery. The region around named locations were scanned for matching coastline and marine features. Once a match was found then the footage would be reviewed to track the likely area that the video covers in multiple scenes.

The video region was then digitised approximately in QGIS into the AU_AIMS_NESP-3-17_Reef-map-geo-media.shp shapefile. Notes were then added about the important features seen in the footage. A link to the video, including the time code so that it would start at the relevant portion of the video. Long videos showing multiple locations were added as multiple entries, each with a separate polygon location and a different URL link with a different start time.

Articles and Datasets While this dataset primarily focuses on videos, we started adding relevant datasets, websites, articles and reports. These categories of media are not complete in this version of the dataset.

Data dictionary:

RegionName: (String, 255 characters): Name of the location, Examples: 'Oyster Stacks Snorkelling Area', 'Kurrajong Campground', 'South Lefroy Bay' State: (String, 30 characters): Abbreviation of the state that the region corresponds to. For example: 'WA', 'QLD', 'NT'. For locations far offshore link the location to the closest state or to an existing well known region name. For example: Herald Cay -> Coral Sea, Rowley shoals -> WA. MediaType: (String, 20 characters): One of the following: - Video - Video Playlist - Website - Report - EIS - Book - Journal Paper

HabitatRef: (Int): An indication that this resource shows high accuracy spatial habitat information can be used for improving the UQ habitat reference datasets. This attribute should indicate which resources should be reviewed and converted to habitat reference patches. It should be reserved for where a habitat can be located on satellite imagery with sufficient precision that it has high confidence. Media that corresponds to information that is deeper than 15 m is excluded (assigned a HabitatRef of 0) as this is too deep to be used by the UQ habitat mapping. - 1 - Use for habitat reference data. - 0 - Only provides general information about the patch. Imagery can be spatially located accurately or detail is insufficient.

Highlight: (String, 255 characters): This records the classification of reef mapping, or research question that this video is most useful for. Not all videos need this classification. In general this attribute should be reserved for those videos that have the highest level of useful information. Think of it as a shortlist of videos that someone trying to understand a particular aspect of categorising reefs from satellite imagery should review. The following are some of the questions associated with each category that the videos provide some answers. - High tidal range fringing reef: Here we want to understand the structure of fringing reefs in the Kimberleys and Northern Territory where the tides are large and the water is turbid. Is there coral on the tops of the reef flats? Won't the coral dry out if it grows on the reef flat? How will it get enough light if it grows on the reef slope? - Ancient coastline: Along many parts of WA there are shallow rocky reefs off the coast that appear to be acient coastline. What is the nature of these reefs? Does coral or macroalgae grow on them? - Seagrass: What does seagrass look like from satellite imagery - Ningaloo backreef coral: Ningaloo is a very large reef system with a large sandy back. Should the whole back reef be considered coral reef or something else? What are all the dark areas in this back reef area, macroalgae, seagrass, coral? - Macroalgae: What does macroalgae look like from satellite imagery. How can we tell it apart from coral or seagrass? - Deep shoal benthic habitat: There are many deep banks and shoals across north

This specialized location dataset delivers detailed information about marina establishments. Maritime industry professionals, coastal planners, and tourism researchers can leverage precise location insights to understand maritime infrastructure, analyze recreational boating landscapes, and develop targeted strategies.

How Do We Create Polygons? -All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct detailed indoor polygons. This meticulous process ensures higher accuracy and consistency. -We verify our polygons through multiple quality checks, focusing on accuracy, relevance, and completeness.

What's More? -Custom Polygon Creation: Our team can build polygons for any location or category based on your specific requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market analyses to identify growth opportunities. -Pinpoint the ideal location for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute targeted, location-driven marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

Why Choose LocationsXYZ? LocationsXYZ is trusted by leading brands to unlock actionable business insights with our spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI data. Request your free sample today and explore how we can help accelerate your business growth.

This extensive location dataset offers a comprehensive mapping of automotive businesses across the United States and Canada. Auto industry researchers, business developers, and market analysts can leverage precise location information to understand market distribution, identify potential opportunities, and develop strategic insights into the automotive service sector.

How Do We Create Polygons? -All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct detailed indoor polygons. This meticulous process ensures higher accuracy and consistency. -We verify our polygons through multiple quality checks, focusing on accuracy, relevance, and completeness.

What's More? -Custom Polygon Creation: Our team can build polygons for any location or category based on your specific requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market analyses to identify growth opportunities. -Pinpoint the ideal location for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute targeted, location-driven marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

Why Choose LocationsXYZ? LocationsXYZ is trusted by leading brands to unlock actionable business insights with our spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI data. Request your free sample today and explore how we can help accelerate your business growth.

The dataset contains 49103 landslides, that were manually mapped by visual inspection of pre- and post-event satellite images in an area of 8981 km2. Such images are acquired by PlanetScope satellites (https://www.planet.com/) and are provided under an academic license; 3-m resolution multiband tiles are used.

Pre-event imagery refers to the Monthly Global Basemap products provided by Planet, the April 2023 Basemap was used. Post-event images were acquired between 22 May and beginning of June 2023. The cloud-free image closer to the event was used and multi-temporal frames were checked in selected areas (e.g., due to the presence of shadows or unclear images). Images are accessed through the Planet QGIS Plugin.

This dataset supersedes version 1, since it represents its update; major changes include:

• mapping over a wider area (8981 vs 5764 km2);
• check on the landslides mapped in version 1 located on flat slopes (lower than 5°); removal of polygons associated with river erosion and not due to gravity movements

NOTES ON VERSION 1

landslides were manually mapped at a scale of 1:5.000 by a single operator in a time interval of 5 weeks following the rainfall event; the inventory (version 1.0) was completed on 28 June 2023. Please note that data did not undergo any kind of validation.

Data are provided in shapefile format (coordinate system WGS84 UTM 32N) and in kml format.

The main dataset is the “Emilia landslides” shp/kml file; the “area” shapefile refers to the investigated area; the “riverbank and agricultural fields” files include polygons that were mapped but refer either to river courses having high discharge in the post-event images, or to color changes probably due to farming activities or the evolution of agricultural fields. The “riverbank and agricultural fields” elements should not refer to slope movements, and usage of these data is not recommended, unless a validation is made.

Xtract.io's comprehensive automotive location data for automotive businesses delivers a detailed view of the automotive service sector. Industry researchers, business developers, and market analysts can utilize this automotive data to understand market distribution, identify potential opportunities, and develop strategic insights into car repair shop locations and automotive service landscapes.

How Do We Create Automotive Polygons? -All our automotive store polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery and street-level views to ensure precision for automotive location data. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official automotive company websites to construct detailed indoor polygons for auto service locations. This meticulous process ensures higher accuracy and consistency for automotive POI data. -We verify our car repair shop polygons through multiple quality checks, focusing on accuracy, relevance, and completeness for automotive retail locations.

What's More? -Custom Polygon Creation: Our team can build automotive polygons for any location or category based on your specific requirements. Whether it's a new automotive retail chain, auto service center, or automotive dealership, we've got you covered. -Enhanced Customization: In addition to automotive polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your automotive geospatial data. -Flexible Data Delivery Formats: We provide automotive datasets in industry-standard formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various automotive systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your automotive location data is always up-to-date for evolving business needs.

Unlock the Power of Automotive POI and Geospatial Data With our robust automotive polygon datasets and point-of-interest data, you can: -Perform detailed automotive market analyses to identify growth opportunities. -Pinpoint the ideal location for your next auto service center or business expansion. -Decode consumer behavior patterns using automotive geospatial insights. -Execute targeted, location-driven marketing campaigns for better ROI in automotive sectors. -Gain an edge over competitors by leveraging automotive geofencing and spatial intelligence.

Why Choose LocationsXYZ for Automotive Data? LocationsXYZ is trusted by leading automotive brands to unlock actionable business insights with our spatial data solutions. Join our growing network of successful clients who have scaled their automotive operations with precise polygon and automotive POI data. Request your free sample today and explore how we can help accelerate your automotive business growth.

Grasslands provide a wide range of ecosystem services within agricultural landscapes. Mapping and assessing the status and use intensity of grasslands is thus important for environmental monitoring. We here provide maps with detected mowing events, as a proxy for grassland use intensity, for grassland areas across Germany for the years 2017 to 2021.

The dataset contains maps of grassland mowing activity in Germany, which have been produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire grassland area, i.e. permanent grassland, potentially permanent grassland (e.g. fodder crops) and other extensive areas. They are derived from dense time series of Sentinel-2, Landsat 8 (and 9) data. Map production is based on the methods described in Schwieder et al. (2022). The algorithm used to derive the maps is available as a user-defined function for the FORCE environment (Frantz, D., 2019).

Each annual dataset includes seven layers: (1) the number of detected mowing events, (2) the day of year (DOY) of the first to sixth detected mowing event. Ancillary data layers are available on request. The maps include all areas that have at least once been classified as permanent grassland, cultivated grassland or fallow in the maps of agricultural land use between 2017 and 2021 that are provided by Thünen Institute. Please consider to use the respective annual agricultural land use map or any other data source to generate a mask for your purpose.

We provide this dataset "as is" without any warranty regarding the quality or completeness and exclude all liability. Please refer to Schwieder et al. (2022) for the related accuracy assessment and potential limitations and / or contact the authors directly.

The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed.

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References

Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.

Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795.

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Grassland mowing events across Germany © 2022 by Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; Erasmi, Stefan; licensed under CC BY 4.0.

Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

Xtract.io's location data for home and electronics retailers delivers a comprehensive view of the retail sector. Retail analysts, industry researchers, and business developers can utilize this dataset to understand market distribution, identify potential opportunities, and develop strategic insights into home and electronics retail landscapes.

How Do We Create Polygons? -All our polygons are manually crafted using advanced GIS tools like QGIS, ArcGIS, and similar applications. This involves leveraging aerial imagery and street-level views to ensure precision. -Beyond visual data, our expert GIS data engineers integrate venue layout/elevation plans sourced from official company websites to construct detailed indoor polygons. This meticulous process ensures higher accuracy and consistency. -We verify our polygons through multiple quality checks, focusing on accuracy, relevance, and completeness.

What's More? -Custom Polygon Creation: Our team can build polygons for any location or category based on your specific requirements. Whether it’s a new retail chain, transportation hub, or niche point of interest, we’ve got you covered. -Enhanced Customization: In addition to polygons, we capture critical details such as entry and exit points, parking areas, and adjacent pathways, adding greater context to your geospatial data. -Flexible Data Delivery Formats: We provide datasets in industry-standard formats like WKT, GeoJSON, Shapefile, and GDB, making them compatible with various systems and tools. -Regular Data Updates: Stay ahead with our customizable refresh schedules, ensuring your polygon data is always up-to-date for evolving business needs.

Unlock the Power of POI and Geospatial Data With our robust polygon datasets and point-of-interest data, you can: -Perform detailed market analyses to identify growth opportunities. -Pinpoint the ideal location for your next store or business expansion. -Decode consumer behavior patterns using geospatial insights. -Execute targeted, location-driven marketing campaigns for better ROI. -Gain an edge over competitors by leveraging geofencing and spatial intelligence.

Why Choose LocationsXYZ? LocationsXYZ is trusted by leading brands to unlock actionable business insights with our spatial data solutions. Join our growing network of successful clients who have scaled their operations with precise polygon and POI data. Request your free sample today and explore how we can help accelerate your business growth.