Block Course: Deep Learning. Introduction to Deep Learning For Forestry Applications Background
Block Course: Deep Learning. Introduction to Deep Learning For Forestry Applications Background
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
- Introduction to Python and Tensorflow Keras
- Introduction to Deep Learning and neural networks
- Deep Learning architectures and their application fields
- Exemplary forestry applications
- Deep Learning with Python and Keras
FORMAT
- Two-part intensive block course
- The course takes place as an online event
- Practical examples for reprogramming using Jupyter Notebook
- Module M.Forst.745: 6 ECTS | Max. 20 course participants
PERIOD
The course is offered as a full-day block course.
Part I on 21./22.10.2021 | Part II from 16. – 18.03.2022
TARGET GROUP
BA and MA students as well as PhD candidates with an interest in the topic are welcome to join the course.
CONTACT AND REGISTRATION
DR. NILS NÖLKE | EMAIL: