This repository contains all resources for the tutorial "Data-Efficient Deep Learning for Earth Observation" held at the International Geoscience and Remote Sensing Symposium 2024.
The goal of this tutorial is introduce and showcase methods for a more efficient training of Deep Learning models for Earth observation applications. The following topics will be introduced in our practical lab sessions:
- Data Fusion
- Multitask Learning
- Self-supervised Learning
In this tutorial we make use of the ben-ge dataset, which is available here. The ben-ge paper is available on arxiv.
Course materials are provided in the form of Jupyter Notebooks that use Pytorch to implement Deep Learning models. The Notebooks can be accessed via the launchers provided below, or they can be download by cloning this repository. If you are running the Notebooks in the cloud, we recommend to use Google Colab as it provides access to GPUs for more efficient training.
| Lab | Content | CoLab Notebook Launchers | MyBinder Notebook Launchers |
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| Lab 1 | Presentation (slides, compressed slides) / Deep Learning Recap + Data Fusion Notebook |  |
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| Lab 2 | Multi-task Learning | |
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| Lab 3 | Self-supervised Learning | |
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This tutorial will be presented by Michael Mommert (Stuttgart University of Applied Sciences), Joelle Hanna and Linus Scheibenreif (AIML Lab, University of St. Gallen).
All contents of this tutorial are provided under the BSD-3-Clause license.