OBSERVER: Artificial Intelligence and Earth Observation workshop looks into the future of EO in Europe
sonia
Artificial Intelligence is transforming how Earth Observation data are analysed and used to support environmental monitoring, climate research and action, as well as disaster risk reduction and emergency management. AI methods, including machine learning techniques, are helping researchers, public institutions, and industry extract insights more quickly from increasingly large volumes of satellite data. To explore these developments, policymakers, scientists, and industry representatives gathered in Brussels on 9–10 March 2026 for the workshop Artificial Intelligence and Earth Observation: From Innovation to Services. Discussions examined how AI is reshaping Copernicus and the Destination Earth (DestinE) initiative, supporting the evolution of operational EO services and modelling capabilities. In this Observer, we look at some highlights from the workshop.
Artificial Intelligence (AI) is reshaping how Earth Observation (EO) data are analysed and used to support services for environmental monitoring, climate research, and disaster response. These developments were the focus of the workshop Artificial Intelligence and Earth Observation: From Innovation to Services. Held in Brussels on 9–10 March 2026 and organised by the European Commission, the event featured high-level keynote presentations, technical briefings, panel discussions, and hands-on demonstrations, attracting more than 1,500 registrants, including around 200 who attended in person.
Opening the event, representatives from the Commission emphasised that AI has become a “game changer” for EO. As satellite missions and environmental monitoring systems generate ever larger volumes of data, AI techniques are increasingly enabling to extract insights quickly and integrate them into existing operational services.
Discussions throughout the two days highlighted Europe’s ambition to become the world’s “AI continent”, combining regulatory leadership with advanced and powerful digital infrastructure, high-quality datasets, and a dynamic innovation ecosystem. In this context, Copernicus and the Destination Earth (DestinE) initiative were repeatedly described as key pillars of Europe’s strategy. Copernicus provides one of the largest collections of open EO data globally, while DestinE is developing digital twins of the Earth system combining satellite observations, modelling, and AI.
Together, these initiatives create the environment in which AI can be increasingly integrated into the EO value chain, helping transform raw satellite data into operational knowledge for science, policy, and society.
Turning satellite data into operational insights
Machine learning techniques are allowing researchers and service providers to process large volumes of satellite observations more efficiently and identify patterns which would otherwise remain difficult to detect. During the workshop, the European Centre for Medium-Range Weather Forecasts (ECMWF) presented examples of how machine learning models trained using datasets such as Copernicus Climate Change Service’s ERA5 can improve environmental forecasting. ERA5, one of the most widely used climate reanalysis datasets, provides detailed historical records of atmospheric conditions and has become an important resource for developing AI-based weather prediction models. By combining these data-driven approaches with established physical models, researchers are developing new tools capable of improving predictions of environmental variables and supporting earlier warnings of extreme weather events.
AI is also enabling new ways for users to interact with EO data. Emerging tools based on conversational interfaces and intelligent assistants can help users navigate complex datasets more easily, identify relevant information, and generate customised analyses. One example is Copernicus Observia AI, an AI assistant built on the European Environment Agency’s (EEA) GPT-LAB platform which guides users through finding, exploring and using Copernicus Land Monitoring Service data, while ensuring responses are grounded in trusted Copernicus sources.
Infrastructure powering the AI revolution
While AI provides powerful analytical capabilities, its development depends on a robust digital infrastructure. A recurring theme throughout the workshop was the importance of combining high-quality data, efficient workflows, and advanced computing resources to support AI applications in EO.
Tiago Quintino, Head of Development Section at ECMWF, emphasised the importance of high-quality data for AI, noting that “data is the new oil” and that large volumes of reliable observations are essential for producing robust AI outputs. Copernicus was frequently highlighted as a key advantage for Europe in this context. Its satellite missions and open and free data policy provide large volumes of high-quality time series which can be used to train and validate machine learning models.
At the same time, the importance of expanding Europe’s computing capacity was also underlined. Initiatives such as EuroHPC are helping to strengthen the continent’s high-performance computing capabilities, while new AI factories and future AI “gigafactories” aim to significantly increase computing power available for AI development. Together, these investments are helping create an ecosystem in which EO data, AI, and advanced computing infrastructure can be combined to support the next generation of environmental monitoring and forecasting applications.
Digital twins open new possibilities for environmental modelling
The workshop also highlighted the growing role of digital twins, particularly within DestinE. These digital representations combine EO data with advanced modelling frameworks to simulate environmental processes and explore future, “what if”, scenarios.
Examples demonstrated how digital twins will support decision-making in areas such as ocean management. By combining satellite observations, modelling systems, and machine learning techniques, these platforms can improve predictions and allow researchers to explore how environmental systems may respond to different conditions.
Sessions also highlighted the potential efficiency gains delivered by AI-based approaches. Machine learning models can sometimes generate results comparable to those of traditional simulations while requiring significantly less computing power, allowing more frequent or interactive analyses.
Building trustworthy AI for the future of EO
Across both days, speakers emphasised the importance of developing trustworthy AI systems. In scientific and operational contexts, maintaining confidence in AI-powered insights requires transparency, reproducibility, and traceability back to the underlying datasets.
Speakers stressed that AI should complement rather than replace the numerical models based on physical laws used in Earth system science. By combining data-driven approaches with established scientific methods, researchers can develop more robust tools for analysing environmental change.
Transparency also emerged as an important issue. Users often want to understand how AI models generate their results, particularly when these outputs inform important decisions. Ensuring that AI systems are explainable and properly validated is therefore essential for building trust in AI-enabled EO services.
As discussions during the workshop made clear, Europe’s strategy for the future of EO relies on combining high-quality satellite data, advanced modelling capabilities, powerful computing infrastructure, and trustworthy AI.
