Advancing the Research-to-Industry Continuum for Earth Observation AI in Europe via the AI-on-Demand Platform

Earth Observation (EO) and environmental research increasingly relies on AI methods that require access to large datasets, scalable cloud infrastructures, and high-performance computing (HPC) resources. At the same time, the transition of research outcomes into operational, industry-ready services remains challenging, often demanding substantial re-engineering of data pipelines, execution environments, and deployment models. This separation between research-oriented and industry-oriented infrastructures continues to limit the reuse, scalability, and real-world impact of EO innovations.

Addressing this gap, the European AI-on-Demand Platform (AIoD) [1] was recently expanded to support both research and industry within a unified digital infrastructure. The platform brings together research-driven AI assets (such as models, workflows, and datasets) with industry-grade tools and services for the development, training, and operationalisation of AI applications across cloud and HPC infrastructures, in an efficient and responsible manner. As a unified gateway, the AIoD connects previously fragmented resources across the European AI ecosystem, making them accessible, reusable, and adaptable to diverse user needs. In parallel, efforts are underway to explore interoperability with emerging European AI Factory initiatives, including PHAROS (the Greek AI Factory) [2], aiming to support future federated access to specialised AI computing resources.

We illustrate this approach through Earth Observation and environmental services and use cases that are jointly accessible to researchers and practitioners, including the mapping of sea surface features and marine pollutants, satellite image enhancement through super-resolution, and AI-based prediction and analysis of extreme weather events, enabling a seamless transition from experimentation and validation to scalable, operational deployment. These developments extend earlier work on European AI and Earth Observation convergence [3].

[1] http://aiodp.eu
[2] https://www.pharos-aifactory.eu
[3] A. Troumpoukis et al., European AI and EO convergence via a novel community-driven framework for data-intensive innovation. Future Gener. Comput. Syst. 160: 505-521 (2024) https://doi.org/10.1016/j.future.2024.06.013

This work has received funding from the European Union’s Digital Europe Programme (DIGITAL) under grant agreement No 101146490.