Federated and reusable processing of Earth observation data.

Area of Science:

• Geospatial science
• Cloud computing
• Data science

Background:

• The exponential growth of Earth Observation (EO) and geospatial data requires new approaches for data analysis.
• Storing and processing massive datasets in a single cloud environment is becoming infeasible.
• Federated processing across multiple cloud platforms is emerging as a critical solution.

Purpose of the Study:

• To introduce key concepts for federated processing of large-scale geospatial data.
• To discuss interoperability aspects within a federated cloud environment.
• To present the openEO Platform as a solution for federated Earth Observation data processing.

Main Methods:

• Leveraging a community-based approach to federated processing.
• Utilizing openEO, a common Application Programming Interface (API) and defined process set.
• Implementing federated processing on the openEO Platform, a public cloud infrastructure.

Main Results:

• Demonstration of key concepts for effective federated processing of EO data.
• Highlighting interoperability solutions for multi-cloud geospatial data access.
• Showcasing the openEO Platform's capability to abstract and simplify complex data handling.

Conclusions:

• Federated processing is a necessary paradigm shift for managing and analyzing the increasing volume of Earth Observation data.
• The openEO Platform provides a robust, abstracted, and interoperable solution for federated geospatial data processing across multiple clouds.
• Standardized APIs and community-driven approaches are crucial for enabling efficient reuse and accessibility of distributed EO data.