QGIS Quantum GIS azad kros platform Coğrafi informasiya sistemi Sistem üzərində iş 2002 ci ildə başlamışdır Sistemin yar

This dataset contains binary geotiff masks/classifications of six Arctic deltas for channels, lakes, land, and other small water bodies (see methods). Tiff files can be opened with any image viewer, but use of georeferencing data attached to the imagery will require a GIS platform (e.g., QGIS). Dataset includes individually classified scene masks for Colville (2014), Kolyma (2014), Lena (2016), Mackenzie (2014), Yenisei (2013), and Yukon (2014). We also provide .mat files for each delta that include a 2D array of the mosaicked images that is cropped to include only the area used in our analyses (see Piliouras and Rowland, 2020, Journal of Geophysical Research - Earth Surface), as well as the X (easting) and Y (northing) arrays for georeferencing, with coordinates in UTMs.

QGIS önceleri Quantum GIS olarak bilinir veri görüntüleme düzenleme ve çözümleme yetenekleri sağlayan çoklu platform des

The geographical mapping software market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by several key factors: the rising adoption of cloud-based solutions offering enhanced accessibility and scalability, the burgeoning need for precise spatial data analysis in urban planning and water resource management, and the escalating use of GIS technology in geological exploration for resource discovery and environmental monitoring. The market's compound annual growth rate (CAGR) is estimated at 8% between 2025 and 2033, projecting significant market expansion. This growth is further supported by the increasing availability of high-resolution satellite imagery and improved data processing capabilities, leading to more accurate and detailed maps for various applications. While the market shows strong potential, certain restraints, including high software licensing costs and the complexity of some GIS software, may impede growth to some extent. However, the overall trend leans towards increased adoption driven by the significant benefits of enhanced spatial analysis across industries. Market segmentation reveals a strong demand for cloud-based solutions due to their flexibility and cost-effectiveness compared to web-based or on-premise software. Geographically, North America and Europe currently hold significant market shares, reflecting established GIS infrastructure and technological advancement. However, Asia-Pacific is expected to witness substantial growth in the coming years driven by rapid urbanization, infrastructure development, and increased government investment in mapping initiatives. This region's expanding market will be fueled by countries like China and India, with significant potential for market penetration. The key players in this competitive landscape continually innovate, releasing new features and functionalities to maintain their market positions. The focus is increasingly on user-friendliness, integration with other software platforms, and advanced analytical capabilities.

QGreenland is a free, open source mapping and data-viewing tool for Greenland. QGreenland works on the QGIS platform and full information is at www.QGreenland.org.

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.

The Digital Geologic-GIS Map of Yosemite Valley Glacial and Postglacial Deposits, 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 (yova_glacial_and_surficial_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 (yova_glacial_and_surficial_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 (yova_glacial_and_surficial_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.) A GIS readme file (yose_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (yova_glacial_and_surficial_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 (yova_glacial_and_surficial_geology_metadata_faq.pdf). Please read the yose_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 (yova_glacial_and_surficial_geology_metadata.txt or yova_glacial_and_surficial_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).

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise figures for market size and CAGR aren't provided, considering the technological advancements and applications in agriculture (precision farming, crop monitoring), water conservancy (flood management, irrigation optimization), forest management (deforestation monitoring, resource assessment), and the public sector (urban planning, disaster response), a conservative estimate places the 2025 market size at approximately $2 billion. This figure reflects the substantial investments in satellite imagery acquisition and analysis capabilities worldwide. The market is further fueled by the rising adoption of cloud-based solutions, enhancing accessibility and scalability of software platforms. Trends such as the integration of AI and machine learning for automated image processing, the proliferation of high-resolution satellite imagery, and the increasing availability of open-source software are accelerating market expansion. However, factors such as the high cost of specialized software licenses and the need for skilled professionals to operate the sophisticated systems act as restraints. The market is segmented by application (agriculture, water conservancy, forest management, public sector, others) and software type (open-source, non-open-source). The North American and European markets currently hold significant shares, but the Asia-Pacific region is witnessing rapid growth due to increasing infrastructure development and government initiatives promoting geospatial technologies. This dynamic market landscape presents lucrative opportunities for both established players and emerging companies in the years to come. The forecast period (2025-2033) anticipates continued growth, with a projected CAGR of approximately 12%, driven by the aforementioned technological advancements and broadening applications across various industry verticals. The competitive landscape is comprised of both major players like ESRI, Trimble, and PCI Geomatica, offering comprehensive suites of software, and smaller, specialized companies focusing on niche applications or open-source solutions. The market is characterized by both proprietary and open-source software options. Open-source solutions like QGIS and GRASS GIS offer cost-effective alternatives, particularly for research and smaller organizations, while commercial solutions provide advanced functionalities and support. The increasing availability of cloud-based solutions is blurring the lines between these segments, with hybrid models emerging that combine the benefits of both. Future growth will be significantly influenced by collaborations between software providers and satellite imagery providers, fostering a more integrated ecosystem and streamlining the data acquisition and processing workflow. The market will continue to benefit from advancements in satellite technology, producing higher-resolution, more frequent, and more affordable imagery.

Xverum’s Global GIS & Geospatial Data is a high-precision dataset featuring 230M+ verified points of interest across 249 countries. With rich metadata, structured geographic attributes, and continuous updates, our dataset empowers businesses, researchers, and governments to extract location intelligence and conduct advanced geospatial analysis.

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The Digital Geologic-GIS Map of Fort Larned National Historic Site and Vicinity, Kansas 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 (fols_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 (fols_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 (fols_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.) A GIS readme file (fols_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (fols_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 (fols_geology_metadata_faq.pdf). Please read the fols_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: Kansas 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 (fols_geology_metadata.txt or fols_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 1:50,000 and United States National Map Accuracy Standards features are within (horizontally) 25.4 meters or 83.3 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).

Use a GIS platform (arcgis or QGIS) to access the files

These map layers can be viewed in a variety of open source platforms, such as QGIS and R.

The dataset is on a global scale with a resolution of 1 km grid and encompasses a timespan from 2020 to 2100. These data are projected in the world-Mercator projection coordinate system and are provided in single-band GeoTIFF format, which can be easily utilized by various mainstream GIS and RS platforms such as ArcGIS, QGIS, ENVI, as well as programming languages such as Python and MATLAB. The simulated data files follow a standardized naming convention “sspx_pp_yyyy.tif”, where x represents the simulated SSP scenario (1 to 5), pp represents the simulated RCP scenario; and yyyy represents the simulated year. For example, the data file named “ssp1_26_2030.tif” corresponds to the LULC simulation data for the year 2030 under the SSP1-2.6 scenario. Each GeoTIFF data file includes integer raster attribute values ranging from 1 to 6, which represent the following land use types: cropland, forest, grassland, urban, barren, and water.

A continuous dataset of Land Surface Temperature (LST) is vital for climatological and environmental studies. LST can be regarded as a combination of seasonal mean temperature (climatology) and daily anomaly, which is attributed mainly to the synoptic-scale atmospheric circulation (weather). To reproduce LST in cloudy pixels, time series (2002-2019) of cloud-free 1km MODIS Aqua LST images were generated and the pixel-based seasonality (climatology) was calculated using temporal Fourier analysis. To add the anomaly, we used the NCEP Climate Forecast System Version 2 (CFSv2) model, which provides air surface temperature under both cloudy and clear sky conditions. The combination of the two sources of data enables the estimation of LST in cloudy pixels.

Data structure

The dataset consists of geo-located continuous LST (Day, Night and Daily) which calculates LST values of cloudy pixels. The spatial domain of the data is the Eastern Mediterranean, at the resolution of the MYD11A1 product (~1 Km). Data are stored in GeoTIFF format as signed 16-bit integers using a scale factor of 0.02, with one file per day, each defined by 4 dimensions (Night LST Cont., Day LST Cont., Daily Average LST Cont., QA). The QA band stores information about the presence of cloud in the original pixel. If in both original files, Day LST and Night LST there was NoData due to clouds, then the QA value is 0. QA value of 1 indicates NoData at original Day LST, 2 indicates NoData at Night LST and 3 indicates valid data at both, day and night. File names follow this naming convention: LST_  .tif, where  represents the year, represents the month and represents the day. Files of each year (2002-2019) are compressed in a ZIP file. The same data is also provided in NetCDF format, each file represents a whole year and is consist of 4 bands (Night LST Cont., Day LST Cont., Daily Average LST Cont., QA) for each day.

The file LSTcont_validation.tif contains the validation dataset in which the MAE, RMSE, and Pearson (r) of the validation with true LST are provided. Data are stored in GeoTIFF format as signed 32-bit floats, with the same spatial extent and resolution as the LSTcont dataset. These data are stored with one file containing three bands (MAE, RMSE, and Perarson_r). The same data with the same structure is also provided in NetCDF format.

How to use

The data can be read in various of program languages such as Python, IDL, Matlab etc.and can be visualize in a GIS program such as ArcGis or Qgis. A short animation demonstrates how to visualize the data using the Qgis open source program is available in the project Github code reposetory.

Web application

The LSTcont web application (https://shilosh.users.earthengine.app/view/continuous-lst) is an Earth Engine app. The interface includes a map and a date picker. The user can select a date (July 2002 – present) and visualize LSTcont for that day anywhere on the globe. The web app calculate LSTcont on the fly based on ready-made global climatological files. The LSTcont can be downloaded as a GeoTiff with 5 bands in that order: Mean daily LSTcont, Night original LST, Night LSTcont, Day original LST, Day LSTcont.

Code availability

Datasets for other regions can be easily produced by the GEE platform with the code provided project Github code reposetory.

Provides WMS/WFS services based on OGC international standards. It can be used in Openlayers Qgis that supports OGC standard. Please refer to the attached file for details.

The GIS database used in this project serves as a centralized repository for all spatial datasets required for wildfire threat analysis and model training. It includes CAL FIRE’s Wildland Fire Threat layer, which provides pixel-based classifications of wildfire potential across California, as well as transportation infrastructure layers, including primary and secondary roads and railways.To support impact analysis, 1,000-foot buffer zones were generated around each infrastructure feature to define zones of interest for wildfire segmentation. The database is structured for integration into both machine learning workflows and GIS environments, enabling seamless overlay, visualization, and spatial querying within platforms such as ArcGIS Pro or QGIS.

The Digital Geologic-GIS Map of the Primm Springs Quadrangle, Tennessee 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 (prsp_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (prsp_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 (prsp_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). 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 (natr_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (natr_tn_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 (prsp_geology_metadata_faq.pdf). Please read the natr_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. 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: Tennessee Division of Geology. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (prsp_geology_metadata.txt or prsp_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 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).

Important note: If you get a message that .zip archive is corrupt, please try updating WinRAR or right-click the folder and select Extract All on Windows or use unzip command on Linux terminal. If the issue persists, email: kamath.harsh@utexas.edu

Abstract

We introduce GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for major cities worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse resolution urban canopy elevation data with a random forest model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had an RMSE of 9.1 meters, and mean building height within 1-km² grid cells had an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (~55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort at Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and ecological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.

Data

We are also supplying a vector file to represent the data coverage, and this file will receive updates as data for new city is added. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). These formats are compatible with the SUEWS and SOLWEIG models for the simulation of urban energy balance and thermal comfort. The vector files employ the Universal Transverse Mercator (UTM) projection. Both the vector and raster files are compatible with GIS platforms like QGIS and ArcGIS and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the BEP-BEM urban model in the urban WRF system in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These files can be directly incorporated into the WRF pre-processing system (WPS). The UT-GLOBUS UCPs are determined using a moving kernel with a size of 1 km2 and spacing of 300 meters in both the X and Y directions

Data coverage

The 'Coverage_xxxx.gpkg' files provide that geographical extents of cities that are included in our dataset.

How to find your city in the UT-GLOBUS dataset

Open the 'coverage' geopackage (.gpkg) files in QGIS or ArcGIS. Click on the city polygons and get the 'Label'/City name. Find a folder with the same 'Label'/City name. All the data for the periticular city will be in the folder.

How to run BEP-BEM model in WRF using UT-GLOBUS urban canopy parameters

This dataset contains the spatial-temporal distribution data of the Modified Soil Adjusted Vegetation Index (MSAVI) in the vegetation growth season (April October) around the Tarim Basin. The dataset is stored in a folder named "MSAVI Dataset". The folder contains seven periods of data from 1990 to 2021 in 1990, 1995, 1999, 2005, 2010, 2015 and 2021. The data naming method is "Year+MSAVI", The projection coordinate system of the dataset is WGS1984-Albers. The data types are all in GeoTIFF format, with a spatial resolution of 30 meters and a data volume of approximately 40.6 GB. It can be read, viewed, analyzed, processed, and applied in conventional geographic information system software and remote sensing platforms such as ArcGIS, QGIS, ENVI, and ERDAS IMAGINE.

This layer shows urban bike corridors for York County, Pennsylvania. They are dedicated or prioritized pathways designed to enhance cyclist safety, accessibility, and connectivity within city transportation networks. Bike corridors may include protected bike lanes, shared roadways, multi-use trails, and greenways, often integrated with public transit and urban land uses. They are typically designed with features such as traffic calming, signage, pavement markings, and physical barriers to promote safe, continuous, and comfortable cycling for all age and ability levels. The analysis incorporates spatial data layers such as road networks, digital elevation models (DEM), land use/land cover (LULC), vehicular traffic volumes, cycling crash incidents, and demographic data within GIS platforms like ArcGIS Pro and QGIS. Advanced network analysis, including least-cost path modeling and service area analysis, identifies optimal routing options based on factors such as slope, traffic density, and road hierarchy. Kernel Density Estimation and hotspot analysis help locate high-risk zones for cyclists, informing the placement and design of safer corridors. Suitability analysis is conducted through a weighted overlay of criteria—proximity to key destinations (schools, parks, employment centers), existing cycling infrastructure, equity indicators, and environmental considerations. GIS tools such as buffering, intersecting, and spatial joins are used to model catchment areas and potential demand. A Multi-Criteria Decision Analysis (MCDA) framework within GIS enables the prioritization of corridor segments based on user-defined planning goals. The output includes thematic maps, spatial prioritization models, and a comprehensive geodatabase of recommended bike corridor alignments. This GIS-based approach offers city planners and policymakers a robust, data-driven toolset for designing bike-friendly cities that promote active transportation, reduce carbon emissions, and support healthier, more equitable communities.