Artificial Intelligence
In this course, you’ll learn how to apply object detection in QGIS using modern AI and deep learning techniques. You’ll work with the latest QGIS plugins to automatically detect objects such as buildings, infrastructure, or vegetation in satellite and aerial imagery. The focus is on preparing data, running detection models, and interpreting the results. Upon completion, you’ll be able to effectively use object detection for analysis, monitoring, and reporting in research and policy.
Would you like to learn how to automatically recognize and analyze objects in spatial data? In the QGIS Object Detection course, you’ll learn how to use QGIS to detect objects in satellite imagery and aerial photos using modern AI and deep learning techniques—without having to write any code yourself. You’ll discover how object detection is used for analysis, monitoring, and supporting policy and research.
QGIS is a powerful open-source GIS platform for analyzing and visualizing geographic data. In this course, you’ll build on your basic QGIS knowledge and learn to work with current object detection workflows and plugins. You’ll analyze raster data such as orthophotos and satellite imagery and convert detection results into usable vector layers for further GIS analysis.
Object detection in QGIS goes beyond manual digitization. You’ll learn how AI models automatically recognize objects such as buildings, infrastructure, trees, or other spatial features. You’ll also learn how to prepare data, evaluate results, identify errors, and interpret uncertainties to ensure that the outcomes are reliable and reproducible.
In the QGIS Object Detection course, you’ll learn how to apply object detection results in practical workflows—for example, for monitoring, inventories, reporting, or policy analysis. The course demonstrates how to use open-source tools to integrate advanced object detection into your existing GIS environment.
Please note! Knowledge of QGIS is required for this course. If you do not have this knowledge, we recommend that you take the QGIS Basic Course first.
In this course, you’ll learn step by step how object detection works within QGIS. You’ll start with the basics: what object detection is, what types exist, and how AI-based detection differs from traditional GIS analysis.
Next, you’ll get hands-on experience with:
You’ll learn not only how to detect objects, but also how to use the results responsibly in analysis and decision-making.
This course is unique because it approaches object detection entirely from a practical GIS perspective. No abstract AI theory—just immediately applicable workflows within QGIS. You’ll learn how to use object detection in a reproducible and verifiable way with open-source tools.
Among other things, you’ll learn:
The course is hands-on, open-source, and focused on real-world GIS applications.
This course is intended for GIS users who want to automatically recognize and analyze objects in spatial data. Do you work in land use planning, the environment, ecology, infrastructure, agriculture, research, cartography, or policy? Then this course offers immediate value.
You’ll need a basic knowledge of QGIS, but no experience with AI, deep learning, or programming. Do you want to stop manually digitizing objects and instead detect them in a smart and reproducible way? Then QGIS Object Detection is the logical next step.
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Contact usOn the first day, you’ll be introduced to object detection in QGIS and the role of AI and deep learning in spatial analysis. You’ll learn what object detection is, what types of detection exist (object detection vs. segmentation), and for which applications these techniques are suitable.
You’ll gain hands-on experience with the most commonly used QGIS plugins for object detection, including Deepness and GeoAI. You’ll also learn how to prepare raster data such as satellite images and orthophotos, load AI models, and perform object detection without programming. The emphasis is on understanding the workflow, correctly interpreting detection results, and recognizing errors and uncertainties.
Topics for Day 1:
On the second day, you’ll delve deeper into the application and validation of object detection results. You’ll learn how to convert detections into usable vector layers and how to integrate them into existing GIS analyses. The course also covers quality control, reproducibility, and practical applications.
You’ll work with additional plugins and tools, such as SAM/segmentation-based plugins (e.g., GeoOSAM-style workflows) for refining and correcting objects. You will also learn how to apply object detection in specific use cases, such as monitoring, inventories, and policy analyses, and how to report results clearly.
Topics for Day 2:
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
Reliability depends on the quality of the input data, the model used, and the settings. In this course, you will learn how to identify errors, noise, and uncertainties, how to validate results, and when manual correction or additional analysis is necessary.
In traditional GIS analysis, objects are often digitized manually or derived from existing vector layers. Object detection uses AI models to automatically identify objects in raster data such as satellite imagery or aerial photographs, making analyses faster, more consistent, and more reproducible.
No, the course focuses on applying object detection using QGIS plugins with graphical interfaces. You will learn to work with existing models and workflows without having to write code yourself, while still gaining an understanding of the underlying principles.
Yes, to keep up with the program, it’s important to have a solid grasp of the basics of QGIS. If you don’t, we recommend the QGIS basics training.
