We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Build Advanced EO Workflows with Custom Functions in openEO within the Copernicus Data Space Ecosystem

Formal Metadata

Title
Build Advanced EO Workflows with Custom Functions in openEO within the Copernicus Data Space Ecosystem
Title of Series
Part Number
18
Number of Parts
34
Author
License
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
Identifiers
Publisher
Release Date
Language
Producer
Production PlaceDoorwerth

Content Metadata

Subject Area
Genre
Abstract
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.