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BD4EO Course: EO Processing Workflows & FAIR Data

Simula Research Laboratory
EO Processing Workflows & FAIR Data

Welcome to the course materials for understanding modern Earth Observation processing workflows and implementing FAIR (Findable, Accessible, Interoperable, Reusable) data principles in professional environments.

QR-Code of the EO Processing Workflows & FAIR Data course material

QR-Code of the EO Processing Workflows & FAIR Data course material

Course Overview

This course bridges the gap between technical EO processing capabilities and strategic data management approaches, designed specifically for lecturers, industry professionals, and government representatives who need to understand and implement modern EO workflows in their organizations.

What Makes This Course Different

Rather than focusing purely on technical details, we emphasize:

Session Structure

Session 1: EO Processing Workflows & Python

Tuesday 09:10-10:30 (80 minutes)

Technical foundation demonstrating cloud-native EO processing, Python ecosystem, and operational workflows through live demonstrations of ESA services.

Session 2: FAIR Data & Open Science

Tuesday 11:00-12:00 (60 minutes)

FAIR principles awareness applied to Earth Observation, with operational examples, readiness discussion, and introduction to implementation approaches.

Key Learning Outcomes

By completing this course, you will:

Interactive Learning Approach

This course combines multiple learning approaches:

ESA Context

This course showcases ESA’s comprehensive approach to modern EO processing and FAIR data implementation through operational examples:

Prerequisites

Essential:

Helpful but not required:

Technical Requirements

To fully engage with the interactive elements:

Support and Community

During the Course:

After the Course:

License and Reuse

These course materials are made available under Creative Commons Attribution 4.0 (CC-BY) license, following the FAIR principles Wilkinson et al. (2016) demonstrated throughout the course. You are encouraged to:


Bibliography

Acknowledgments

This course builds upon the collaborative efforts of the broader EO and open science communities, with special recognition to:

References
  1. Zellner. (2025). Cubes & Clouds - Cloud Native Open Data Sciences for Earth Observation. Zenodo. 10.5281/zenodo.15389591
  2. EOPF-Sample-Service. (2024). EOPF Sample Service: Collection of Python notebooks demonstrating the usage of the EOPF-CPM library. https://github.com/EOPF-Sample-Service/eopf-sample-notebooks
  3. ESA. (2025). EarthCODE: Earth Science Collaborative Open Development Environment. European Space Agency. https://earthcode.esa.int
  4. European Space Agency. (2024). Copernicus Data Space Ecosystem. https://dataspace.copernicus.eu/
  5. Pangeo Community. (2024). Pangeo: A community platform for Big Data geoscience. https://pangeo.io/
  6. Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., & others. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 1–9. 10.1038/sdata.2016.18