How can you uncover deeper insights from geospatial data using AI? In this one-on-one course, you’ll learn how to apply deep learning techniques in QGIS for object detection, image recognition, and advanced spatial analysis. Through clear online modules and hands-on assignments, you’ll build practical skills you can apply right away. By the end, you’ll be able to run GIS analyses more quickly—and with greater accuracy—using deep learning.
Deep learning, a branch of artificial intelligence (AI), can identify patterns in vast amounts of geospatial data. When combined with QGIS—a powerful, open-source GIS platform—it becomes a valuable tool for analyzing aerial imagery, satellite photos, and other spatial datasets.
By training neural networks, you can use deep learning in QGIS for a range of tasks. For example, it can automatically detect buildings, roads, or other features in imagery. It also helps analyze land use changes, such as urban expansion or shifts in natural landscapes. Another key use is image classification, which allows you to categorize satellite images quickly and accurately.
When integrated into QGIS workflows, deep learning allows GIS professionals and data analysts to analyze spatial data faster and with higher precision. This opens the door to more advanced modeling, monitoring, and data-driven decision-making.
This course shows you how to use deep learning in QGIS to streamline and enhance geospatial analysis. You’ll learn how to use AI to automate tasks like analyzing aerial photos, classifying images, and identifying patterns in spatial datasets.
You’ll start by learning the fundamentals of deep learning and how neural networks work. Then, step by step, you’ll explore how to collect, prepare, and optimize training data, train models, and apply them in real GIS scenarios.
Along the way, you’ll practice techniques like object detection, image recognition, and pattern analysis. You’ll see how AI can automatically identify features such as buildings, roads, and vegetation. You’ll also learn how to track land use changes and fine-tune datasets for smarter, data-driven insights.
By the end of the course, you’ll be able to apply deep learning in your own GIS projects—performing complex analyses with more speed, accuracy, and confidence.
Blended learning gives you the best of both worlds—live interaction and flexible, self-paced study—so you can build real, job-ready GIS skills. In this course, you’ll get hands-on with open-source tools and learn how to apply AI to real-world geospatial challenges.
We kick off with a live session where you’ll start working with real aerial and satellite imagery. With help from deep learning experts, you’ll prepare training data, apply neural networks, and build models that detect features and patterns in your data.
Next, our self-paced modules let you go deeper into key topics at your own speed. You’ll explore how to integrate AI into QGIS, work with plugins, and automate analysis tasks. You’ll also learn how to improve model performance and translate outputs into clear, usable insights.
Then, in a second live session, you’ll apply your skills to a full deep learning workflow. You’ll train a model, assess its accuracy, fix common issues, and get expert feedback to fine-tune your results.
A highlight of this course is its real-world focus. You’ll work on exercises that reflect actual GIS challenges—like identifying urban features, mapping vegetation, or monitoring land use. The outputs you create will be directly useful for your own projects.
By combining expert guidance with flexible learning, this course helps you move beyond theory. By the end, you’ll know how to build, train, and apply AI models in QGIS—and make better, faster decisions with your spatial data.
After this course, you’ll be able to:
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.
