This is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

QGIS is a Free and Open Source Geographic Information System. This dataset contains all the information to get you started.

Download high-quality, up-to-date field_685e8fb67dd91 shapefile boundaries (SHP, projection system SRID 4326). Our field_685e8fb67dd91 Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Sampling design used in the production of the global maps of grassland dynamics 2000–2022 at 30 m spatial resolution in the scope of the Global Pasture Wath initiative. The sampling desing was based in Feature Space Coverage Sampling and resulted in 10,000 sample tiles (1x1 km) distributed across the World, which were visual interpreted in Very-High Resolution imagery thorugh the QGIS plugin QGIS Fast Grid Inspection.

FSCS steps include:

• Short vegetation mask that includes all pixels mapped as mosaic, shrubland, grassland, and sparse vegetation in at least one year from 1993 to 2021 according to ESA/CCI global land cover (gpw_short.veg.mask_esacci.lc_p_1km_s_19920101_20201231_go_epsg.3857_v1.tif),
• 87 input raster layers (including vegetation indices, terrain, land temperature, climate and water variable),
• Principal Components Analysis (PCA) using all input layers,
• Selection of the 10 first components (explaining 75% of variance),
• K-Means with 10,000 clusters (targeted number of samples -
  gpw_grassland_fscs.kmeans.cluster_c_1km_20000101_20221231_go_epsg.3857_v1.tif)
• Calculation of euclidean distance (in the principal component space) of all 1-km pixels to the centre of each cluster,
• Selection of the pixel with the shortest distance for each cluster,
• Conversion of the selected pixels into sample tiles ()

The file gpw_grassland_fscs_tile.samples_1km_20000101_20221231_go_epsg.3857_v1.gpkg provides the sample tiles and include the follow collumns:

• X: Latitude in Web Mercator projection (EPSG:3857),
• Y: Longitude in Web Mercator projection (EPSG:3857),
• cluster_id: K-Means output ranging from 0—9999,
• cluster_distance: Distance from the selected sample to the centre of the cluster,
• cluster_size: Number o 1-km pixels inside the K-Means cluster, estimated using Web Mercator projection (EPSG:3857)
• cluster_size_equal_area: Number o 1-km pixels inside the K-Means cluster, estimated using Goode Homolosine Land projection (ESRI:54052)
• cluster_size_corr: Correction factor to adjust the area distortion due to Web Mercator projection, estimated by the difference in normalized propotional values of cluster_size and cluster_size_equal_area.
• rf_n_pred: Number of pixels predicted by a RF model trained to estimate probability to select the pixel closer to the centre of the KMeans cluster. The RF models were trained individually per each cluster using the 10 first components derived by PCA (gpw_comps_fscs.pca_m_1km_20000101_20221231_go_epsg.3857_v1.tar.gz).
• rf_samp_prob: Sampling probability based on RF model (rf_n_pred / cluster_size)
• rf_samp_wei: Sampling weight estimated in Web Mercator projection.
• rf_samp_wei_coor: Corrected sampling weight estimated in Goode Homolosine Land projection.

Related resources

• Maps of dominant grassland:
  2000-2002 2003-2005 2006-2008 2009-2011 2012-2014 2015-2017 2018-2020 2021-2022
• Probability maps of cultivated grassland:
  2000-2022 (All URLs)
• Probability maps of natural/semi-natural grassland:
  2000-2022 (All URLs)
• Grassland reference samples based on VHR imagery (2000–2022):
  GeoPackage files
• Global machine learning models (Random Forest):
  Parquet and joblib python files
• Reference sampling design derived by FSCV:
  GeoPackage and raster files
• Harmonized reference samples based on existing LULC dataset:
  GeoPackage and raster files
• Source code for reproducibility:
  GitHub release
  
• Mapping feedback tool:
  GeoWiki
• Data catalogues:
  OpenLandMap STAC Google Earth Engine

Support

For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in https://github.com/wri/global-pasture-watch

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

• Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
• Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

Dataset Qgis

## Overview

Dataset Qgis is a dataset for instance segmentation tasks - it contains Rice Field annotations for 401 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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OpenStreetMap (OSM) is a free, editable map & spatial database of the whole world. This dataset is an extract of OpenStreetMap data for French Polynesia in a GIS-friendly format.

The OSM data has been split into separate layers based on themes (buildings, roads, points of interest, etc), and it comes bundled with a QGIS project and styles, to help you get started with using the data in your maps. This OSM product will be updated weekly.

The goal is to increase awareness among Pacific GIS users of the richness of OpenStreetMap data in Pacific countries, as well as the gaps, so that they can take advantage of this free resource, become interested in contributing to OSM, and perhaps join the global OSM community.

OpenStreetMap data is open data, with a very permissive licence. You can download it and use it for any purpose you like, as long as you credit OpenStreetMap and its contributors. You don't have to pay anyone, or ask anyone's permission. When you download and use the data, you're granted permission to do that under the Open Database Licence (ODbL). The only conditions are that you Attribute, Share-Alike, and Keep open.

The required credit is “© OpenStreetMap contributors”. If you make a map, you should display this credit somewhere. If you provide the data to someone else, you should make sure the license accompanies the data

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In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. The plugin ‘Swiss Geo Downloader’, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model ‘swissALTI3D’ with the help of the ‘Swiss Geo Downloader’ and how to use it for digital planning of a cable line with the plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link to Seilaplan website: https://seilaplan.wsl.ch

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Das Plugin Swiss Geo Downloader, welches für das Open Source Geoinformationssystem QGIS zur Verfügung steht, ermöglicht frei verfügbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgenössischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue Höhenmodell swissALTI3D herunterladen und für die Seillinienplanung mit dem Plugin Seilaplan verwenden kann.

Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch

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The QGIS map available for download contains all layers visualized in the thesis.

PROMICE-2022 Ice Mask QGIS Bundle This dataset contains the PROMICE-2022 Ice Mask packaged as a ready-to-use QGIS project. The zipped bundle includes the QGIS project file and all associated vector/raster layers and styles, so you can open the project directly and view the pre-styled layers in a GIS environment. Contents QGIS project file (.qgz) with preconfigured layouts Raster and vector layers of the PROMICE-2022 ice mask WMS layer for the topographic map of Greenland WMS layer for SPOT 6/7 imagery WMS layers for Sentinel-2 imagery (2022) Layer style files README with usage notes Brief description The PROMICE-2022 Ice Mask provides an updated delineation of ice/non-ice areas of the Greenland Ice Sheet from August 2022. It is intended for visualization, mapping, and as an input layer for further geospatial analysis in QGIS or other GIS software. For detailed methodology, processing steps, validation, and full technical documentation, please consult the primary repository (link below). Citation Please cite this dataset as: Luetzenburg, Gregor; Korsgaard, Niels J.; Deichmann, Anna K.; Socher, Tobias; Gleie, Karin; Scharffenberger, Thomas; Fahrner, Dominik; Nielsen, Eva B.; How, Penelope; Bjørk, Anders A.; Kjeldsen, Kristian K.; Ahlstrøm, Andreas P.; Fausto, Robert S., 2025, PROMICE-2022 Ice Mask, GEUS Dataverse. DOI: https://doi.org/10.22008/FK2/O8CLRE How to use Download and unzip the package. Open the included .qgz QGIS project in QGIS (recommended QGIS 3.## or newer). Related Datasets Google Earth Engine The PROMICE-2022 Ice Mask (file 06) is available as an asset in the Google Earth Engine Feature Collection Access it here: projects/promice-data-ee/assets/ice_masks/PROMICE_2022_ICE_MASK PROMICE-2022 Ice Mask (Master Dataset) This is the dataset containing the updated PROMICE-2022 ice mask. The QGIS bundle provided here includes this dataset but users seeking the raw data files, metadata, and versioning should refer to the master record. Access it here: https://doi.org/10.22008/FK2/O8CLRE PROMICE-2022 Ice Mask Sentinel-2 RGB Mosaic (August 2022) This dataset provides the Greenland-wide Sentinel-2 RGB mosaic used as the primary visual reference during ice mask delineation. In the QGIS project, the Sentinel-2 images from 2022 are included as a WMS layer, but users who need the underlying mosaic files should reference the dedicated dataset. Access it here: https://doi.org/10.22008/FK2/OUKHBW

QGIS Segmentacja 2

## Overview

QGIS Segmentacja 2 is a dataset for semantic segmentation tasks - it contains Trawa Kostka 29XU annotations for 200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset part of the Geology and Planetary Mapping Winter School 2022 featuring Beagle Rupes as a study area.
Beagle Rupes is lobate scarp at Mercurys surface with a length of more than 600km cross-cutting an oval shaped crater.
We compiled a beginners – intermediate level training package for the area. The package includes several basemaps such as Map Projected Basemap Reduced Data Record (BDR) (Hash 2013a), High-incidence East-illumination Basemap (HIE), Map-projected High-incidence West-illumination (HIW) (Hash 2015a), Map Projected Low-Incidence Angle Basemap Reduced Data Record (LOI) (Hash 2013b), Map Projected Multispectral Reduced Data Record (MDR) Hash 2015b) and digital terrain model (DTM) (Becker et al., 2016). The data is cut to the area of interest and a training project is set up for QGIS.

The training package is designed as a group exercise with four adjacent tiles covering the Beagle Rupes area.

Download high-quality, up-to-date shapefile boundaries (SHP, projection system SRID 4326). Our Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

The Los Angeles County Storm Drain System is a geometric network model representing the storm drain infrastructure within Los Angeles County. The long term goal of this network is to seamlessly integrate the countywide drainage infrastructure, regardless of ownership or jurisdiction. Current uses by the Department of Public Works (DPW) include asset inventory, operational maintenance, and compliance with environmental regulations.GIS DATA DOWNLOADS: (More information is in the table below)File geodatabase: A limited set of feature classes comprise the majority of this geometric network. These nine feature classes are available in one file geodatabase (.gdb). ArcMap versions compatible with the .gdb are 10.1 and later. Read-only access is provided by the open-source software QGIS. Instructions on opening a .gdb file are available here, and a QGIS plugin can be downloaded here.Acronyms and Definitions (pdf) are provided to better understand terms used. ONLINE VIEWING: Use your PC’s browser to search for drains by street address or drain name and download engineering drawings. The Web Viewer link is: https://pw.lacounty.gov/mpm/gis/fcd/More About these Downloads All data added or updated by Public Works is contained in nine feature classes, with definitions listed below. The file geodatabase (.gdb) download contains these eleven feature classes without network connectivity. Feature classes include attributes with unabbreviated field names and domains.ArcMap versions compatible with the .gdb are 10.1 and later. Feature ClassDownloadDescriptionCatchBasinIn .gdbCatch basins collect urban runoff from guttersCulvertIn .gdbA relatively short conduit that conveys storm water runoff underneath a road or embankment. Typical materials include reinforced concrete pipe (RCP) and corrugated metal pipe (CMP). Typical shapes are circular, rectangular, elliptical, or arched.ForceMainIn .gdbForce mains carry stormwater uphill from pump stations into gravity mains and open channels.GravityMainIn .gdbUnderground pipes and channels.LateralLineIn .gdbLaterals connect catch basins to underground gravity mains or open channels.MaintenanceHoleIn .gdbThe top opening to an underground gravity main used for inspection and maintenance.NaturalDrainageIn .gdbStreams and rivers that flow through natural creek bedsOpenChannelIn .gdbConcrete lined stormwater channels.PumpStationIn .gdbWhere terrain causes accumulation, lift stations are used to pump stormwater to where it can once again flow towards the oceanData Field DescriptionsMost of the feature classes in this storm drain geometric network share the same GIS table schema. Only the most critical attributes are listed here per LACFCD operations. AttributeDescriptionASBDATEThe date the design plans were approved “as-built” or accepted as “final records”.CROSS_SECTIN_SHAPEThe cross-sectional shape of the pipe or channel. Examples include round, square, trapezoidal, arch, etc.DIAMETER_HEIGHTThe diameter of a round pipe or the height of an underground box or open channel.DWGNODrain Plan Drawing Number per LACFCD NomenclatureEQNUMAsset No. assigned by the Department of Public Works’ (in Maximo Database).MAINTAINED_BYIdentifies, to the best of LAFCD’s knowledge, the agency responsible for maintaining the structure.MOD_DATEDate the GIS features were last modified.NAMEName of the individual drainage infrastructure.OWNERAgency that owns the drainage infrastructure in question.Q_DESIGNThe peak storm water runoff used for the design of the drainage infrastructure.SOFT_BOTTOMFor open channels, indicates whether the channel invert is in its natural state (not lined).SUBTYPEMost feature classes in this drainage geometric nature contain multiple subtypes.UPDATED_BYThe person who last updated the GIS feature.WIDTHWidth of a channel in feet.Contact email: mapping@dpw.lacounty.gov

Here you will find an assembly of the open street map in metropolitan france. The geopackage version also contains data from neighbouring countries (border regions except espagne). The the.qgz project allows the geopackage data to be opened with the busy style and hacking depending on the zoom level. video presenting this data gpkg and QGIS: https://www.youtube.com/watch?v=R6O9cMqVVvM&t=6s The version.sql is characterised by an additional attribute for each geometric entity: The INSEE code This data will be updated on a monthly basis.

INSTRUCTIONS FOR DECLARING GPKG DATA: Download all files and rename as follows:

OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_001.zip — > OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.001 OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_002.zip — > OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.002 OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_003.zip — > OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.003 OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_004.zip — > OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.004

or if you know the batch back to create a.bat file containing this (or you rename the renowned file. txt as rename.bat):

pushd “% ~ DP0” REN OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_001.zip OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.001 REN OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_002.zip OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.002 REN OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_003.zip OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.003 REN OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_004.zip OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.004

and launch.bat by double clicking on it (the batch must be in the same place as the zip files)

Then right-click on the OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_001.zip file and have it extracted to “OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_001\” with your pressure relief software. There is no need to click on 002, 003, 004. Opening file.001 opens all other parts of the archive

For version.sql, the procedure is the same: rename OSM_SQL_FXX_PRDG_D000_ED214_001.zip to OSM_SQL_FXX_PRDG_D000_ED214.zip.001 OSM_SQL_FXX_PRDG_D000_ED214_002.zip to OSM_SQL_FXX_PRDG_D000_ED214.zip.002 OSM_SQL_FXX_PRDG_D000_ED214_003.zip to OSM_SQL_FXX_PRDG_D000_ED214.zip.003 Then carry out pressure relief

In order to digitally plan a cable line using the QGIS plugin ‘Seilaplan’, maps with various background information are helpful. In this tutorial we show you how to obtain maps that are helpful for cable line planning, for example a national map of Switzerland at different scales, the NFI vegetation height model or the NFI forest mix rate. For this we explain what WMS datasets are and how to integrate them into QGIS. No download of large data is needed for this, only a good internet connection. Please note that the tutorial language is German!

Link for the integration of WMS data: https://wms.geo.admin.ch/

Link to the description on the Swisstopo website: https://www.geo.admin.ch/en/geo-services/geo-services/portrayal-services-web-mapping/web-map-services-wms.html

Link to the Seilaplan website: https://seilaplan.wsl.ch

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung sind verschiedene Hintergrundkarten hilfreich. In diesem Tutorialvideo zeigen wir, was WMS Daten sind und wie man diese in QGIS einbinden kann. Dafür müssen die Daten nicht heruntergeladen werden. Es braucht lediglich eine gute Internetverbindung. Für die Seillinienplanung hilfreiche Karten sind bspw. die Landeskarte der Schweiz in verschiedenen Massstäben, das Vegetationshöhenmodell LFI oder der Waldmischungsgrad LFI.

Link zur Einbindung der WMS Daten: https://wms.geo.admin.ch/

Link zur Beschreibung auf der Swisstopo Webseite: https://www.geo.admin.ch/de/geo-dienstleistungen/geodienste/darstellungsdienste-webmapping-webgis-anwendungen/web-map-services-wms.html

Link zur Seilaplan-Website: https://seilaplan.wsl.ch

Data Usage and Deployment This guide intends to describe how the provided datasets can be used within the environment of ArcGIS version 10.4, although the same rules and guidelines apply for usage in the QGIS and ArcGIS Pro environment. In the ZENODO data repository (accessed at https://doi.org/10.5281/zenodo.17579289), end users can find four compressed files. Use Winrar, Winzip or the Windows embedded decompression tools to open the files and decompress the included filed. The file titled “FSim_Dataset_Greece_LYR_files_ARCGIS_QGIS_ARCGISPRO.rar” contains .lyr files that are an ArcGIS file type that is a container that stores the visualization and metadata properties for a single map layer, such as symbology, labeling, and transparency. It does not contain the actual geographic data itself but acts as a "pointer" to the source data (like a shapefile or a raster). These files can be used withing the ArcGIS environment to assign the proper colors and classes, as portrayed on the maps of the paper. Layer files are available only for raster type datasets. In addition, we provide the layer files that hold the symbology and classes at the format of .qml, for use in QGIS, and .lyrx for use in ArcGIS Pro. The file titled “Metadata.rar” contains all the individual .xml files holding information about the metadata of each dataset, created with the ISO 19139 XML format. The Geodatabase_metadata.xml contains the metadata of the File Geodatabase. The FSim_Geodatabase_Schema_Report is the schema report of the File Geodatabase, created with ArcGIS Pro 3.5 in four readable versions (Excel, JSON, PDF, and HTML). They can be directly opened with any relevant software that supports each file type, and users can navigate to the different sections of metadata information for all datasets included in the File Geodatabase. The file titled “FSim_Dataset_Greece_raw_files.rar” contains all datasets in file formats that are not part of a File Geodatabase, like ESRI shapefiles (.shp) and ERDAS IMAGINE raster filed (.img), intended for use in GIS software that are not owned by the ESRI (like the QGIS). Each dataset contained in the File Geodatabase can be found in this compressed file with the same name, as reported in the paper. Below the image shows how the data in folder appear when viewed inside ArcCatalog. The file titled “FSim_Dataset_Greece.gdb.rar” contains the File Geodatabase, created with ArcGIS version 10.4. The File Geodatabase has all the datasets, including their accompanying metadata. This File Geodatabase can be opened with any version of ArcGIS or ArcGIS Pro. To Open ESRI File (gdb) using GDAL (Geospatial Data Abstraction Library) follow these steps: Install GDAL: If you don’t have GDAL installed on your system, download and install it from the official website (https://gdal.org/download.html). Make sure to get the version that supports the OpenFileGDB driver. Open a command prompt or terminal window: Launch the command prompt (Windows) or terminal (macOS/Linux). Use ogrinfo: To list the layers available in the gdb file, use the ogrinfo command followed by the path to the gdb folder. For example: ogrinfo path/to/your/geodatabase.gdb Replace path/to/your/geodatabase.gdb with the actual path to your gdb file. This command will display information about the layers in the geodatabase. Access specific layers: To access a specific layer within the gdb, you can use the ogr2ogr command to convert the layer to another format, such as a shapefile, GeoJSON, or CSV. For example, to convert a layer named “example_layer” to a shapefile, use: ogr2ogr -f "ESRI Shapefile" output_shapefile.shp path/to/your/geodatabase.gdb example_layer Replace output_shapefile.shp with the desired name for the output shapefile, and path/to/your/geodatabase.gdb with the actual path to your gdb file. This command will convert the “example_layer” to a shapefile format. To open the File Geodatabase in QGIS, follow these steps: 1. Open QGIS: Launch your QGIS application to begin the import process. 2. Access Data Source Manager: Click on the Data Source Manager button in the toolbar. 3. Select Directory: In the Data Source Manager window, click on the Directory tab. Choose Open File GDB as the source type. 4. Browse to the .gdb Folder: Click the Browse button and navigate to the unzipped .gdb folder. Select the folder and click Add. 5. Adding Layers: After adding the folder, you will see the layers contained within the geodatabase. Click Close to finish the process. An example of how the Metadata appear when viewed through ArcCatalog is provided in the figure below: Open the datasets in ArcGIS version 10.4 and use the .lyr files First, open the ArcGIS, navigate to the decompress folder of the File Geodatabase after pressing “Add Data”, and select a dataset (in this case, the Burn Probability raster). Then, double click on the Burn_Prob layer in the Table of Contents, move to Symbology, select “Classified” since all layers have a