About the course
Forests are an important part of our ecosystems and produce a large part of the air we breathe. With drones, satellites, and cutting-edge Artificial Intelligence, we can monitor and survey forests and trees faster and more precisel. In this course, you’ll learn how Deep Learning turns these datasets into actionable insights for sustainable forest management. Combine your passion for nature with the power of AI.
Content
Satellite Earth observation has become a key technology in forest monitoring. With the European earth observation program Copernicus, a program exists that provides access to temporally high-resolution and freely available satellite images, and this worldwide. New analysis methods are needed to deal with the huge amounts of data; machine learning offers excellent opportunities here. Even with UAVs, huge amounts of data are often generated when, for example, ground resolutions of 2 cm are used. Concrete evaluation topics are e.g. the detection of game damage or the differentiation of tree species.
Topics which will be addressed:
- Introduction to Python and Tensorflow Keras
- Introduction to classical machine learning and deep Learning
- Deep Learning architectures and their application fields
- Exemplary forestry applications
- Deep Learning with Python and Keras
The skills learned in this course also transfer to many other application fields that use image or video data.
Learning outcomes
In this module, students acquire key qualifications for the use of Deep Learning algorithms for forestry applications, which is also transferable to other applications in other disciplines. They learn the principles of Deep learning as well as neural networks and their optimisation. You will develop an understanding of which problems can be solved with the methods of Deep Learning and which methods should be selected. After completing the module, the students are able to freely program Deep Learning applications in Python. They can independently implement existing neural networks and handle implement existing neural networks and deal with large amounts of data.
Programme
Virtual phase
The programme will cover the following topics and discuss them from the perspectives of Artifical Intelligence/Computer Science, Forestry, and Bioinformatics:
- Introduction to Python and Tensorflow Keras
- Introduction to Data Science, working with data, visualization, image processing basics - Introduction to Machine Learning and Deep Learning
- Deep Learning architectures, Computer Vision tasks, and Explainable AI
- Example forestry applications and datasets - Graphical representation of the results and their interpretation
On-site programme
- Sources of forest remote sensing data
- Introduction to Python with a focus on Tensorflow Keras
- Introduction to Data Science, working with data, visualization, image processing basics
- Introduction to Machine Learning and Deep Learning
- Deep Learning architectures, Computer Vision tasks, and Explainable AI
- Graphical representation of the results and their interpretation
Assessment
Assessment will be through a project to be developed during the on-site period and afterwards. There will be a presentation on the last on-site day, and a final report that will be graded and completes the course.
After successful completion of the course, students will receive the Transcript of records.
Lecturers
Dr. Nils Nölke, University of Göttingen
Prof. Dr. Jean-Christophe Taveau, University of Bordeaux
Prof. Dr. Matias Valdenegro Toro, University of Groningen
Course dates
On-site: 15th March - 20th March 2026, University of Göttingen
Online: 16th January - 27th February 2026, days and hours to be specified
How to apply?
There are approximately 30 places, divided over each of the partner universities: 10 for University of Göttingen, 10 for University of Groningen and 10 for University of Bordeaux. If not all places get filled, the vacant places will be offered to other ENLIGHT universities.
Students will be selected at each of these universities separately. Students interested in the course need to apply via their home university. They should contact their faculty or programme to verify whether it can fit in their curriculum and to ensure academic recognition of the credits obtained. The home university will select the permitted number of students, inform the students as soon as possible, and then send these names to the host institution. Unsuccessful students will be placed on a waiting list and may get a place, if other universities do not use all their allocated places.
Please select your home university below and contact your ENLIGHT coordinator for further information on the application process or consult the linked information.
- University of the Basque Country:
This email address is being protected from spambots. You need JavaScript enabled to view it. - University of Bern: Application instructions for students at the University of Bern
- University of Bordeaux:
This email address is being protected from spambots. You need JavaScript enabled to view it. - Comenius University Bratislava:
This email address is being protected from spambots. You need JavaScript enabled to view it. - University of Galway:
This email address is being protected from spambots. You need JavaScript enabled to view it. - Ghent University:
This email address is being protected from spambots. You need JavaScript enabled to view it. (see information about BIP's) - University of Groningen:
This email address is being protected from spambots. You need JavaScript enabled to view it. - University of Göttingen:
This email address is being protected from spambots. You need JavaScript enabled to view it. (for BIP’s) orThis email address is being protected from spambots. You need JavaScript enabled to view it. (other courses) - University of Tartu: Application instructions for students at the University of Tartu
- Uppsala University: Application instructions for students at Uppsala University.
Contact
Dr. Nils Nölke,