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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: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

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