The core objective of FAIRiCUBE is to enable players from beyond classic Earth Observation (EO) domains to provide, access, process, and share gridded data and algorithms in a FAIR and TRUSTable manner.
This project’s goal is to leverage the power of Machine Learning (ML) operating on multi-thematic datacubes for a broader range of governance and research institutions from diverse fields, who at present cannot easily access and utilize these potent resources. Stefan Jetschny (coordinator, NILU) and Kathy Schleidt (Scientific director, Epsilon Italia) briefly introduce the mission of the project in the following video clip:
Selected use cases will illustrate how data-driven projects can benefit from cube formats, infrastructure, and computational benefits. They will guide us in creating a user-friendly FAIRiCUBE HUB, which is tightly integrated to the common European data spaces. The FAIRiCUBE HUB will provide relevant stakeholders an overview of both data and processing modules readily available to be applied to these data sources. The HUB will support users not intimately familiar with the worlds of Earth observation and machine learning to scope the requirements and costs of their desired analyses. In this way the HUB will contribute to uptake of these resources by a broader community. The FAIR sharing of results with the community will be fostered by providing easy to use tools and workflows directly in the FAIRiCUBE HUB.
