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. Each tutorial video is also accompanied by a written script, providing a step-by-step reference that users can follow alongside the video or consult afterwards.

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

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.

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.

This dataset has been created specifically for the intensive course 'Introduction to (Q)GIS for Archaeologists,' which is held at the Università degli Studi di Pavia in the year 2022 and 2023. The course is a part of the Master's Degree program on 'The Ancient Mediterranean World: History, Archaeology, and Art.' The dataset has been carefully developed to support the learning goals of the course, which aims to provide students with a comprehensive introduction to (Q)GIS tools and techniques that are relevant to archaeology. By using this dataset, students will be able to apply their newly acquired knowledge to real-world scenarios, preparing them for future work in the field.

Students learn about the importance of good data management and begin to explore QGIS and RStudio for spatial analysis purposes. Students will explore National Land Cover Database raster data and made-up vector point data on both platforms.

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.

Instructions for students to use aerial photos, Google Earth and QGIS to explore their fieldwork area prior to their field trip. This material was designed for first-year undergraduate Earth Sciences students, in preparation to a fieldwork in the French Alps. The fieldwork and this guide focuses on understanding the geology and geomorphology.The accompanying dataset.zip contains required gis-data, which are a DEM (SRTM) and Satellite images (Landsat). This dataset is without a topographic map (SCAN25 from IGN) due to licence constraint. For academic use, request your own licence from IGN (ign.fr) directly.

This is a full-day training, developed by UNEP CMB, to introduce participants to the basics of GIS, how to import points from Excel to a GIS, and how to make maps with QGIS, MapX and Tableau. It prioritizes the use of free and open software.

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.

Aram Chaos is a more than 250 km large crater characterized by the presence of Chaotic Terrains forming mesas and knobs, associated with the outflow channel of Ares Vallis. The Chaotic Terrains are unconformably embayed and locally superposed by some layered hydrate minerals-bearing deposits.

The training package includes a complete HRSC coverage (including images and DEMs) (Neukum et al., 2004; Jaumann et al., 2007) and selected CTX (Malin et al., 2007), HiRISE (McEwen et al., 2007), and CRISM (Murchie et al., 2007) data. The area has been divided in 8 tiles each of one ‘stand.alone’ in terms of geology but at the same time ready for collaborative mapping purposes.

Dataset

This dataset was created by Cường Phạm Quốc

Contents

This dataset is part of the QGIS beginner tutorial: https://youtu.be/wu42hyshx7Q

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.

Map specimen data points using QGIS, connect them to form a polygon using the Concave Hull plugin, and calculate the range of a species to examine how it changes over time.

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

Soil Texture Classes (USDA) by Depth, 250m Resolution

A Refined Global Mapping for 1950-2017

By [source]

About this dataset

This open-source dataset offers researchers a granular and comprehensive view of the world's soils, providing soil texture classification from 0 to 200 cm depths with a 250m resolution and utilizing the soiltexture package in R developed by OpenLandMap.org. Using columns such as code, name, value, and color, this dataset brings precision to our understanding of global soils allowing a new level of research accuracy. Internally compressed using COMPRESS=DEFLATE creation option in GDAL for improved accessibly for external users - don't miss out on an unprecedented opportunity to explore the underlying characterstics and properties that make every landscape unique! Explore this valuable open source resource today!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Steps on How To Use This Dataset:

• Understand the data columns - As discussed earlier, you will find four columns in this dataset namely – code (numeric), name (string), value (integer) and color (string). As mentioned before each row contains information regarding a certain type of soil texture associated with their respective codes, names, values and colors which can later be represented in global mapping solutions.
• Clean up data if required - Before you start your analysis it is best practice to clean up your data if required - this includes all irregularities like missing values due to any reasons/circumstances or incorrect labels assigned accidentally against particular entries in columns etcetera.
• Generate customized maps - After making sure that your dataset is complete without any issues now it’s time for visualizing using geographical mapping applications like R or QGIS etcetera based upon your own necessity(say Soil colourful maps depicting occurrences of any particular soil class family all over the world). Future use|interpretations concerning the content within this database are vast depending upon one’s initiative towards exemplifying correlations amongst other variables along with soils accumulation at different depths across vast tracts globally spanning from 1950-2017 eras through highly reliable 250 meters spatiotemporal resolutions as provided herewith!

Research Ideas

• Developing a soil health indicator to track changes in soil texture, fertility, and other physical characteristics over time on a global scale.
• Designing site-specific crop management plans to optimize water uptake and soil nutrient retention.
• Creating predictive models that forecast land suitability for different crops based on specific soil texture requirements

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: sol_texture.class_usda.tt_m_250m_b_1950..2017_v0.1.tif.csv | Column name | Description | |:--------------|:------------------------------------------------------------------------------------------------------------------------------------------------| | Code | A numerical code that represents the soil texture class. (Integer) | | Name | The name of the soil texture class. (String) | | Value | The numerical value corresponding to each code indicating a specific type of soil texture within its corresponding category or range. (Integer) | | Color | The color associated with each individual class for easier visualization on maps or charts. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Abstract In this work, we report the mapping of electrical equipotential lines (1D) and equipotential surfaces (3D) using the free Quantum Geographic information system (QGIS) software. For this purpose, experiments taking into account, four different electrical configurations were performed on physics classes of undergraduate students, using two conductors of opposite electrical charges for each experiment. For the first experiment two copper parallel linear conductors; for the second, a copper parallel linear conductor with a small circular ring acting as a point charge; for the third, two concentric circular ring and for the fourth one a semicircular ring with a small circular ring acting as point charge. The experimental data were treated and interpolated in the, open source, QGIS software, used in geoprocessing, to map the electrical equipotential planes and surfaces.

Workshop Materials Directory Overview

This directory contains a collection of workshop materials and resources for training sessions focused on planetary photogrammetric techniques. It includes step-by-step guides, exercises, installation instructions, presentations, and supplementary data files to support participants in utilizing software for photogrammetry.

Contents:

• Workshop Materials
  Includes resources on NASA’s Ames Stereo Pipeline (ASP), structure-from-motion (SfM) techniques, photogrammetry basics, and HiRISE DTM analysis in QGIS. Key files:

  • ASP: Installation notes, introductory slides, support files, command guide, Docker setup, and stereopipeline documentation.
  • SfM: Resources and guides on GCPs, surveying, exercise materials, and installation/setup instructions.
  • Additional Materials: Workshop agenda, QGIS HiRISE DTM analysis guide, and introduction presentations.

• Elysium Planitia Lava Preprocessing Data
  A zip file containing preprocessing data for analyzing the Elysium Planitia region on Mars, useful for DTM and geospatial applications.

Each sub-directory provides targeted resources designed to aid participants in learning digital elevation modeling for planetary surfaces. This structured, multi-session dataset supports both beginners and advanced users in photogrammetry, geospatial analysis, and terrain modeling applications, with a focus on Martian data.

QGIS is a user-friendly Open Source Geographic Information System (GIS) with license under the GNU General Public License. QGIS is an unofficial project from Open Source Geospatial Foundation (OSGeo). QGIS can run on Linux, Unix, Mac OSX, Windows and Android, as well as supports many vector, raster and database data formats and functionality.