2025-09-04, 13:30–15:00 (Europe/Amsterdam), HugoTECH
In the Earth Observation (EO) domain, data analysis ranges from simple image processing tasks like dilation to complex workflows involving Machine Learning (ML) and Deep Learning (DL). While openEO provides numerous features and functions for data analysis, but given the broad scope of the field, not all potential needs are covered out of the box. To address this, openEO supports User-Defined Functions (UDFs). These UDFs are implemented as standard Python scripts using libraries such as Xarray or Numpy. They allow users to implement custom workflows tailored to specific research requirements.
Thus, in this course, we offer a high-level introduction to openEO with a focus on UDFs and include an example of an advanced EO workflow that applies this concept in practice.
The only requirement for the participants would be:
- Basic understanding of Earth Observation data
- Basic knowledge of Python
- An account in the Copernicus Data Space Ecosystem
We will use the Jupyter Environment offered by the ecosystem.
For users already familiar with these, they can already explore examples available here: https://github.com/Open-EO/openeo-community-examples/tree/main/python
Other links:
CDSE: https://dataspace.copernicus.eu/
CDSE documentation: https://documentation.dataspace.copernicus.eu/#/
openEO community examples: https://github.com/Open-EO/openeo-community-examples/tree/main/python
MOOC on cubes and cloud: https://eo-college.org/courses/cubes-and-clouds/
Example on openEO load_stac: https://github.com/Open-EO/openeo-community-examples/tree/main/python/LoadStac
APEx algorithm catalogue: https://algorithm-catalogue.apex.esa.int/
openEO algorithm plaza: https://marketplace-portal.dataspace.copernicus.eu/