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

This data is comprised of U.S. Census tracts for the year 2019 with data from the American Community Survey, CDC social vulnerability index, CDC Places EPA toxic release inventory sites, PM2.5 annual average from the Atmospheric Composition Analysis Group (https://sites.wustl.edu/acag/). This dataset was created as part of the CAFE Introduction to QGIS 101!!! Session on 6/27/2024 and is for training purposes only.

This repo contains a series of datasets connected to training on geoprocessing.Within the zipped folder there are two subfolder, one containing raster data and the second one containing vector data.

Material for QGIS training provided by FAO NSAHL.

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

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.

This dataset part of the Geology and Planetary Mapping Winter School 2022 featuring Ingenii Basin as a study area. Ingenii Basin is located on the lunar farside centred at 33.7°S 163.5°E within the South Pole-Aitken basin. The floor of Ingenii Basin is filled with mare materials with the basin having a diameter of 282 km. We compiled a beginners – intermediate level training package for the area. The package includes the Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) global mosaic (Speyerer et al., 2011) as a basemap, the Lunar Orbiter Laser Altimeter (LOLA) and SELenological and Engineering Explorer (SELENE) Kaguya merged lunar digital elevation model (DEM) (Barker et al., 2016) and spectral data in the form of a clementine Ultraviolet/Visible (UVVIS) warped color ratio mosaic (Lucey et al., 2000). 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 entirety of Ingenii basin. For beginners the aim is to create a low scale map of the area where the basin rim is distinguished from the basin floor and mare unit as well as detecting smaller craters that exist in the area. These units should then be put in a stratigraphic relationship based on superposition, degradation state and embayment. For intermediate mappers this task can be extended to include the swirl features and finding potential areas for crater size frequency distribution measurement to determine absolute ages for a more detailed stratigraphy.

ArcGIS tool and tutorial to convert the shapefiles into network format. The latest version of the tool is available at http://csun.uic.edu/codes/GISF2E.htmlUpdate: we now have added QGIS and python tools. To download them and learn more, visit http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

Raczynski, K., Grala, K., & Cartwright, J. H. (2025). Enhancing Maps with Charts in QGIS. Mississippi State University: Geosystems Research Institute. [View Document]GEO Tutorial Number of Pages: 7 Publication Date: 02/2025 This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

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

This resources contains PDF files and Python notebook files that demonstrate how to create geospatial resources in HydroShare and how to use these resources through web services provided by the built-in HydroShare GeoServer instance. Geospatial resources can be consumed directly into ArcMap, ArcGIS, Story Maps, Quantum GIS (QGIS), Leaflet, and many other mapping environments. This provides HydroShare users with the ability to store data and retrieve it via services without needing to set up new data services. All tutorials cover how to add WMS and WFS connections. WCS connections are available for QGIS and are covered in the QGIS tutorial. The tutorials and examples provided here are intended to get the novice user up-to-speed with WMS and GeoServer, though we encourage users to read further on these topic using internet searches and other resources. Also included in this resource is a tutorial designed to that walk users through the process of creating a GeoServer connected resource.

The current list of available tutorials: - Creating a Resource - ArcGIS Pro - ArcMap - ArcGIS Story Maps - QGIS - IpyLeaflet - Folium

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from a workshop that was conducted on October 19-23, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

Raczynski, K., Grala, K., & Cartwright, J. H. (2025). GEO Tutorial: Dealing with Coastal Flooding series, part 2: Spatial Predicates: Preparing Residential Dataset. Mississippi State University: Geosystems Research Institute. [View Document] GEO TutorialNumber of Pages: 7Publication Date: 06/2025This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

Raczynski, K., Xavier, F., & Cartwright, J. H. (2025). GEO Tutorial: Joining Tables With Spatial Data. Mississippi State University: Geosystems Research Institute. [View Document]

GEO Tutorial Number of Pages: 6 Publication Date: 06/2025This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

This dataset has been created to train Univ. Eiffel personnels on raster data handling with QGIS.

It provides the following elements:

• Geopackage database with the following layers:
  • QGIS project
  • Extract from the SENTINEL-2 2022-06-11 B8A band
  • Extract from the SENTINEL-2 2022-06-11 B12 band
  • Extract from the SENTINEL-2 2022-07-21 B8A band
  • Extract from the SENTINEL-2 2022-07-21 B12 band
  • Reclassified delta NBR raster layer
  • Delta NBR vector layer
  • Studied area bounding box
• Intermediate results:
  • pre-event NBR raster file
  • post-event NBR raster file
  • Delta NBR raster file
  • Delta NBR raster file multiplied by 1000 (for easier reclassification)

Data sources IDs from opensearch-theia.cnes.fr-sentinel2-l2a catalogue :

• SENTINEL2B_20220721-104826-811_L2A_T31TFL_D
• SENTINEL2B_20220611-104824-395_L2A_T31TFL_D

Raczynski, K., Xavier, F., & Cartwright, J. H. (2025). GEO Tutorial: Dealing with Coastal Flooding series, part 3A: Using Unsupervised Machine Learning For Land Use Land Cover Classification. Mississippi State University: Geosystems Research Institute. [View Document] GEO TutorialNumber of Pages: 5Publication Date: 06/2025This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from workshops that were conducted on February 19-21 and October 6-7, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

Raczynski, K., Babineaux, C., & Cartwright, J. H. (2025). GEO Tutorial: Dealing with Coastal Flooding series, part 9: Creating And Animating Timeseries. Mississippi State University: Geosystems Research Institute. [View Document] GEO TutorialNumber of Pages: 5Publication Date: 06/2025This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.