Unlock the power of deep learning in your geospatial workflows. In this hands-on, project-based course, you’ll learn how to use QGIS to apply neural networks for tasks like image classification, object detection, and pattern recognition. Whether you're working in environmental science, urban planning, or infrastructure analysis, this course gives you the tools to automate processes and extract insights from complex spatial data.
Deep Learning in QGIS
GeoAIGeoAI
Deep Learning and QGIS
Deep learning — a powerful branch of machine learning built on multi-layered neural networks — is changing how we analyze and understand geospatial data. By mimicking the way the human brain recognizes patterns, deep learning enables computers to detect subtle features in satellite imagery, classify land cover, and identify objects with exceptional precision.
Within GIS, this technology is unlocking new levels of efficiency. It can automate time-consuming tasks like mapping infrastructure, monitoring environmental change, and detecting objects across vast landscapes. What once took hours or days to process can now be completed in minutes — and often with greater accuracy.
This matters more than ever. As the volume of spatial data grows — from drones, satellites, and IoT sensors — so does the need for faster, smarter analysis. Urban planners use deep learning to track land use. Conservationists monitor deforestation trends. Disaster response teams map wildfire impact zones in near real-time. In each case, deep learning provides faster insights and supports better decisions.
Thanks to tools like QGIS and its robust plugin ecosystem, deep learning is no longer confined to data science labs or expensive proprietary platforms. It’s now accessible to GIS analysts, planners, researchers, and engineers working on real-world challenges. By combining spatial intelligence with the analytical power of AI, you can approach complex problems with confidence, clarity, and speed.
What will you learn
This course offers a hands-on, practical introduction to deep learning in QGIS. You’ll start by exploring the basics of neural networks — how they work, how they’re built, and why they’re so effective in spatial analysis. Then, you’ll dive into real-world projects where you collect data, train models, and perform deep learning tasks directly in QGIS.
You’ll learn how to:
- Prepare and label geospatial data for training
- Use QGIS plugins to build and fine-tune deep learning models
- Apply your models to classify imagery, detect objects, and segment land cover
- Analyze large datasets using automation
- Communicate results through clear maps and visualizations
By the end of the course, you’ll be ready to apply deep learning to support smarter, faster decision-making in fields like infrastructure planning, environmental monitoring, or disaster response.
Why choose this course
At Geo-ICT, we believe advanced technology should be accessible, practical, and directly tied to real-world impact. That’s why this course is built around applied learning — not just theory.
Here’s what makes it stand out:
- Learn by doing: Every concept is applied in exercises drawn from real scenarios
- Expert guidance: Taught by professionals with deep experience in both AI and GIS
- Open-source tools: No expensive licenses — just QGIS, plugins, and your curiosity
- Relevant and future-ready: Gain skills that are in high demand across industries
Whether you’re new to deep learning or looking to take your GIS skills to the next level, this course provides a smart, approachable path into AI-powered spatial analysis.
Course structure
Day 1
We begin with the fundamentals of deep learning and how it fits into geospatial analysis. You’ll learn how neural networks work, how to prepare training data, and how to label imagery for classification tasks. By the end of the day, you’ll build and run your first classification model using QGIS.
Day 2
On day two, you’ll move into more advanced techniques like object detection and model evaluation. You’ll explore best practices for training accuracy, and apply your model to real satellite or drone imagery. The course concludes with a capstone project that challenges you to apply everything you’ve learned to a real-world use case.
Learning Outcomes
- Understand deep learning fundamentals and neural network architecture
- Collect, prepare, and label spatial data for classification and detection
- Train and apply deep learning models in QGIS
- Analyze satellite imagery and extract features automatically
- Create visualizations that clearly communicate your results
- Use AI to automate analysis and support faster, smarter decision-making
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.
Frequently Asked Questions about Deep Learning in QGIS
In this course you will learn how to apply deep learning techniques for geospatial analyzes with ArcGIS Pro, such as image classification and object detection.
This course is ideal for entry-level and experienced geospatial specialists, as well as professionals from other sectors who want to develop their skills in geospatial analytics and deep learning.
The course lasts two days and covers both theoretical and practical aspects of deep learning in ArcGIS Pro.
Basic knowledge of ArcGIS Pro is recommended, but not required. Some prior knowledge of geospatial concepts is helpful.
Yes, upon successful completion of the course you will receive a certificate, which is valuable for your professional development.
Yes, the course includes practical exercises where you will learn how to apply deep learning models to real geospatial data.
The course covers various techniques such as neural networks, image classification and object detection, specifically aimed at geospatial applications.
Yes, there are online participation options so you can learn from anywhere.
This course focuses specifically on the application of deep learning techniques within ArcGIS Pro, which is a unique combination in the field of geospatial analyses.
We provide additional resources and recommendations to continue developing your knowledge, including access to our online community and updates on future courses.