Unlock the full potential of geospatial data with GDAL—the Geospatial Data Abstraction Library. This hands-on course is designed to equip you with the practical skills to process, transform, and analyze both raster and vector data efficiently. Whether you’re a GIS professional, developer, or researcher, mastering GDAL will streamline your workflows and elevate your data-handling capabilities.
GDAL
ETLETL
Geodata and GDAL
In today’s data-driven world, geospatial information plays a critical role in decision-making across industries—from urban planning and environmental monitoring to logistics and disaster response. But working with geodata effectively requires more than just maps; it demands tools that are reliable, flexible, and efficient. GDAL is one of those tools.
As an open-source library supported by the Open Source Geospatial Foundation, GDAL enables professionals to read, convert, and transform geospatial data across a wide range of formats. Its strength lies in its versatility: GDAL handles both raster and vector data, supports powerful command-line utilities, and integrates easily with Python for automation. These features make it a go-to solution for geospatial data processing around the world.
As organizations increasingly rely on spatial data for real-time decision-making, the ability to process and manage this data efficiently is becoming a highly sought-after skill. This course will give you a strong foundation in GDAL, helping you work more confidently and effectively with the data that powers modern spatial analysis.
What you will learn
Throughout this course, you’ll get practical experience using GDAL to handle geospatial data workflows. You’ll start by learning how to read and write different data formats, understanding how raster and vector datasets behave in various project contexts. You’ll also explore data transformation techniques such as reprojection, clipping, and format conversion—ensuring your data is ready for whatever tools or analyses you need to run.
Once you’ve covered the fundamentals, you’ll move on to automation using GDAL’s scripting capabilities. You’ll learn how to create scripts that batch-process files, reducing time spent on repetitive tasks and increasing your accuracy. Whether you’re converting hundreds of files, generating mosaics, or automating spatial calculations, you’ll gain the skills to make GDAL work for you.
By the end of the course, you’ll be able to transform geospatial data with confidence, apply it across multiple systems, and automate your workflows to save time and reduce errors.
Why choose this course
If you’re serious about building your skills in geospatial data processing, this course offers a focused, hands-on pathway to mastering GDAL. It’s designed for professionals and students who need to get more out of their data—and their time.
- Hands-on learning from day one – Work directly with real-world datasets and exercises that reflect the challenges GIS professionals face daily.
- Expert instruction – Learn from instructors who use GDAL in active GIS, CAD, and spatial data projects.
- Focus on practical applications – Build techniques that transfer immediately to your job, research, or project needs.
- Future-ready skills – Gain experience with one of the most widely used tools in the industry, helping you stand out in the job market.
Course structure
Day 1
The course begins with an overview of geospatial data formats, covering the differences between raster and vector types. You’ll set up your working environment, install GDAL with Python bindings, and learn to navigate its core tools. Early exercises include inspecting datasets with gdalinfo and ogrinfo, converting between raster formats like GeoTIFF and PNG, and working with projections and coordinate systems. You’ll also practice tasks like georeferencing, mosaicking raster files, resampling, and calculating raster band indices such as NDVI.
Day 2
The second day dives deeper into vector data processing. You’ll explore how to read, convert, and write formats like Shapefile and GeoJSON, and apply transformations such as reprojection, clipping, buffering, and merging using ogr2ogr. The course then shifts toward automation. You’ll be introduced to scripting geospatial processes using Python and GDAL, learning how to batch-process datasets and chain together commands into reusable workflows. The day wraps up with a practical project: automating the mosaicking and reprojection of multiple raster files, followed by a group recap and discussion of real-world applications.
Learning Outcomes
- Gain a solid understanding of geospatial data formats and GDAL’s core functionalities
- Develop skills to manipulate and transform raster data, including format conversion, reprojection, mosaicking, and georeferencing
- Acquire proficiency in handling vector data operations such as format conversion, reprojection, and geometric transformations
- Learn to automate geospatial workflows using GDAL scripts, improving efficiency and accuracy in data processing tasks
More Information?
Do you have questions about the course content? Not sure if the course aligns with your learning objectives? Or would you prefer a private session or in-company training? We’re happy to assist—feel free to get in touch.
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Frequently Asked Questions about GDAL
The GDAL (Geospatial Data Abstraction Library) course is designed to teach participants how to efficiently work with geospatial data using GDAL, a widely used open-source software library for handling raster and vector geospatial data formats. GDAL is essential for professionals in GIS (Geographic Information Systems), remote sensing, and geospatial data analysis.
The GDAL Course is intended for:
- GIS Professionals – Individuals working in Geographic Information Systems who need to process, analyze, and transform geospatial data.
- Remote Sensing Specialists – Professionals handling satellite or aerial imagery who require tools for raster data manipulation and analysis.
- Data Scientists and Analysts – Those who work with geospatial data in fields like urban planning, environmental science, or transportation and need to convert or manipulate data in various formats.
- Developers – Software developers or engineers who build or maintain applications that use geospatial data and want to automate geospatial workflows using GDAL libraries.
- Surveyors and Cartographers – Experts involved in land surveying or map creation who require precise geospatial data transformations.
- Researchers and Academics – Those in academia or research institutions working on projects involving geospatial data and its analysis.
- Urban Planners and Environmental Analysts – Professionals in sectors like urban planning, natural resource management, or environmental protection, using geospatial data to inform decision-making.
This course is ideal for anyone who works with geospatial data and seeks to enhance their ability to process, analyze, and transform such data efficiently using GDAL.
By the end of the course, you will be able to efficiently manipulate, transform, and analyze geospatial data, automate repetitive tasks, and integrate GDAL into broader geospatial workflows.
GDAL can help in your daily work by providing powerful tools to efficiently manage, process, and analyze geospatial data. Here’s how it can support you:
- Data Conversion: GDAL allows you to easily convert geospatial data between different formats, making it easier to integrate data from multiple sources or share data with colleagues using different software.
- Data Transformation: You can transform raster and vector data, including reprojecting datasets into different coordinate systems, aligning datasets for spatial analysis, and applying georeferencing.
- Automation of Repetitive Tasks: By scripting geospatial operations with GDAL (using Python or the command line), you can automate tasks like batch conversion, reprojection, or processing large datasets, saving time and reducing human error.
- Spatial Analysis: GDAL supports analysis tasks such as calculating distances, performing overlays, or working with raster calculations (e.g., NDVI for vegetation analysis), enabling more detailed insights from your data.
- Integration with Other Tools: GDAL works well with other GIS software, such as QGIS, PostGIS, and even web mapping tools, helping you streamline your workflow across different platforms.
- Efficient Large Dataset Handling: GDAL is optimized for handling large geospatial datasets, which makes it invaluable when working on big projects or when processing complex geospatial data efficiently.
Overall, GDAL helps you automate processes, save time, and ensure accurate data handling in your daily geospatial workflows.