VISION
Transforming Geospatial Data Accessibility and Efficiency
Embed2Scale envisions a future where the utilisation of geospatial data is not hindered by volume but empowered by efficiency and accessibility. A future where Copernicus and other Earth Observation data are effortlessly accessible, efficiently processed and widely utilised across diverse domains.Our goal is to integrate seamlessly within the Copernicus Programme and beyond and to break down the barriers of data gravity, enabling innovative EO services that respond to global challenges with unprecedented speed and accuracy.
STRATEGY
Transforming Geospatial Data Accessibility and Efficiency
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 (HPC) 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.
Building on this core innovation, Embed2Scale is dedicated to delivering specific advancements, striving for AI-compressors designed to produce embeddings that enable:
- Embed2Transfer: decentralised applications through substantial reduction of data gravity.
- Embed2Infer: portability of inference by significantly lowering computational demand.
- Embed2Train: training acceleration by few-shot model architectures minimising labelled data.
- Embed2Find: near-real-time similarity search at petabyte scale.
Continuing to build on this foundation, our approach to Reusable Data Embeddings takes it further:
Unlike traditional practices that adapt foundation models to specific applications, Embed2Scale will process geospatial data just once. This method creates reusable embeddings that simplify data storage, discovery, and sharing, while enabling more efficient model training for real-world applications.This novel strategy not only enhances operational efficiencies, but also empowers users across diverse sectors to leverage our advanced technological solutions more effectively.
Cross-Domain Collaboration: Embed2Scale thrives on the synergy among leading academic, government, and corporate partners. By drawing on collective expertise in AI, data science, and geospatial technologies, we aim to pioneer solutions that transcend traditional domain boundaries.
Environmental and Societal Impact: Our commitment extends beyond technological innovation. Embed2Scale aspires to contribute to climate research, improve environmental monitoring, and enhance societal well-being through improved information services and innovative Earth observation approaches.
Through these strategic pillars, Embed2Scale is not just redefining the management of geospatial data but is also setting new standards for accessibility, efficiency, and environmental stewardship in Earth Observation.
Join us as we embark on this ambitious journey to unlock the full potential of geospatial data for Europe and the world!