EO4EU - AI-augmented ecosystem for Earth Observation data accessibility with Extended reality User Interfaces for Service and data exploitation

EO4EU is a European Commission-funded innovation project bringing forward the EO4EU Platform which will access and use of EO data easier for environmental, government, and even business forecasts and operations.

The EO4EU Platform, which will be accessible at www.eo4eu.eu, will link already existing major EO data sources such as GEOSS, INSPIRE, Copernicus, Galileo, DestinE among others and provide a number of tools and services to assist users to find and access the data they are interested in, as well as to analyse and visualise this data. The platform will leverage machine learning to support the handling of the characteristically large volume of EO data as well as a combination of Cloud computing infrastructure and pre-exascale high-performance computing to manage processing workloads.

Specific attention is also given to developing user-friendly interfaces for EO4EU allowing users to intuitively use EO data freely and easily, even with the use of extended reality.

EO4EU objectives are:

• Holistic DataOps ecosystem to enhance access and usability of EO information.
• A semantic-enhanced knowledge graph that augments the FAIRness of EO data and supports sophisticated data representation and dynamics.
• A machine learning pipeline that enables the dynamic annotation of the various EO data sources.
• Efficient, reliable and interoperable inter- and intra- data layer communications
• Advance stakeholders’ knowledge capacity through informed decision-making and policy-making support.
• A full range of use case scenarios addressing current data needs, capitalizing existing digital services and platforms, fostering their usability and practicality, and taking into account ethical aspects aiming at social impact maximization.

Technical and scientific innovation can be summarised as follows:

• Improve compression rates for image quality and reduce data volumes.
• Improve the quality of reconstructed compressed images, maintaining the same comparison rates
• Facilitate the design of custom services with a minimized labelled data requirement
• Learn robust and transferable representations of EO data
• Publishing original trained models on EO data with all relevant assisting material to support reusability in a public repository.
• Data fusion optimized execution in HPC and GPU environment
• Better accuracy of data representation
• Customizable visualization tools tailored to the needs of each use case
• Dedicated graphs for end-users with various granularities, modalities, metrics and statistics to observe the overall trends in time, correlations, and cause-and-effect relationships through a responsive web-interfaced module.

In this presentation, the status of the project, the adopted architecture and the findings from our initial user surveys pertaining to EO data access and discovery will be analysed. Finally, the next steps of the project, the early access to the developed platform and the challenges and opportunities will be discussed.