Forests are an important part of our ecosystems and produce a large part of the air we breathe. It is important to monitor and survey forests and trees. Using Artificial Intelligence for monitoring and surveying of forests is one way to reduce costs and interpret different kinds of data (color and hyperspectral images). Recent progress in Artificial Intelligence through Neural Networks and Deep Learning means more widespread applications are now at hand and the exploitation of large amounts of data.
About the course
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
To be announced after registration. Tentative program includes lectures on AI and Deep Learning, Data Science and Image Processing with Python, Forestry, and sessions on Intercultural Competence.
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.
Transcript of Records will be provided a month after the course ends.
Lecturers
- Dr. Nils Nölke / Dr. Lutz Fehrmann (University of Göttingen)
- Prof. Dr. Jean-Christophe Taveau (University of Bordeaux)
- Prof. Dr. Matias Valdenegro Toro (University of Groningen)
Plus lecturers from University of Groningen and University of Bordeaux.
Course dates
On-site period: March 17 - March 21 in Bordeaux.
Online period: January 13 - February 23. Details to be announced.
How to apply?
Students interested in the course need to apply via their home university. Applications are only possible if the course can fit in their curriculum 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 (via a google form to be provided by the University of Bordeaux).
There are approximately 30 places, divided into 10 students for each of the partner universities: University of Göttingen, University of Groningen and University of Bordeaux. If not all places get filled, the vacant places will be offered to other ENLIGHT universities.
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: 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. - University of Tartu: Application instructions for students at the University of Tartu
- Uppsala University: Application instructions for students at Uppsala University.
Contact
Prof. Jean-Christophe Taveau (