The OEMC project aims to build a F.A.I.R.-compliant cyberinfrastructure to accelerate the uptake of environmental information and help build user communities at European and global levels.
Funded by the Horizon-Europe programme of the European Commission, a consortium of 23 partners led by the OpenGeoHub foundation will provide operational solutions and decision-making tools for European and global initiatives such as DestinE, Digital Twin Initiative, Fit for 55, UN sustainable development goals and more.
OEMC serves over 30 use cases from various domains at global, continental, or national level. STML is contributing to the development of Machine Learning and in-situ data support for openEO. We further lead the implementation of a continental and a regional air quality monitor for Europe.
Embed2Scale wants to unlock the true potential of the Copernicus Programme, leveraging AI-based data compression to streamline the exchange of vast geospatial information. This initiative aims to pioneer compressed embeddings, enabling quicker access, decentralized applications and accelerated analytics across four different domains: maritime awareness, aboveground biomass estimation, climate and air pollution prediction, and crop stress & early yield detection.
At the forefront of our strategy lies the deployment of AI compressors, a revolutionary technology that promises to transform the handling of geospatial data. Embed2Scale explores the use of AI compressors trained by self-supervision on High-Performance Computing systems to distil valuable embeddings from raw data. These advanced AI compressors are capable of maintaining utility for multiple downstream tasks while achieving compression ratios of up to 1000x. This innovative approach not only simplifies the data storage, discovery, and sharing processes, but also ensures that Earth Observation data can be processed just once. As a result, the embeddings created are reusable across various applications, significantly reducing the latency and energy consumption associated with managing vast data volumes. By fostering responsible AI applications in Earth Observation, our strategy enables decentralised consumption of Copernicus data in combination with other modalities, providing efficient, near-real-time EO services at a large scale and affordable cost across multiple data providers and data hubs.
