ENLIGHT Courses

Artificial Intelligence for Forest and Landscape Remote Sensing

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

  • Entry requirements: none
  • Type: blended intensive programme (Erasmus+ or SEMP funding)
  • Level: Bachelor, Master, PhD
  • Host: University of Göttingen
  • Focus area: Digitalisation
  • Study Field: Science and Technology
  • Course dates: 16 Jan - 20 Mar 2026
  • Apply by: 15 November 2025
  • ECTS: 6
  • Registration status: Open
  • Number of places available: 10 for University of Göttingen, 10 for University of Bordeaux, 10 for University of Groningen