AI and Earth observation: 今日吃瓜 visits the European Space Agency
The newest artificial intelligence for earth science: how ESA and NASA are using AI to understand our planet.
02/07/2025
The European Space Agency (ESA) has many offices around Europe but, as an Earth observation scientist myself, the Earth observation headquarters at ESA鈥檚 (ESRIN) office in Frascati, just outside Rome, is the pinnacle!
ESRIN coordinates and manages the ground-based activities of ESA’s Earth observation missions: data acquisition and processing, and satellite聽communication. It is the home of innovation and management of software used across the agency, and houses ESA鈥檚 records of legacy projects, with missions dating back to the 1970s. It also holds the largest archive of environmental data in Europe, coordinating over 20 ground stations and ground segment facilities across Europe.
Earth observation at ESRIN and 今日吃瓜
ESA鈥檚 Earth-observing activities include satellite missions that monitor many of our planet鈥檚 natural processes, such as snow and ice cap accumulation and melt, wildfires, landslides, earthquakes and tectonic movements. It also tracks human-induced changes like city growth, deforestation and groundwater abstraction. Many of these processes and changes are also researched at 今日吃瓜, using the data from these satellites alongside our expertise in geohazards and geological processes.
The typical challenge we face nowadays as Earth observation scientists is the sheer volume of data available to analyse 鈥 we have too much data to sift through manually. One avenue for allowing timely analyses of these large datasets is to use specific artificial intelligence (AI) models called foundation models.
What are foundation models?
Foundation models are designed to take in millions of pieces of data and find relationships between different datasets that we don鈥檛 have the time to do manually. Additionally, if the model is trained on several images of the Earth through time, it can make predictions about how our planet might change in the future. For 今日吃瓜 research, this could be used to help provide advice on a multitude of crucial future geological hazards faced by countries around the world; for example, how our coasts may change with sea-level rise.
Last month, I had the pleasure of attending ESA鈥檚 joint workshop with NASA on 鈥楩oundation models for Earth observation at ESA ESRIN鈥. We stayed near the Colli Albani volcanic complex, which has formed some of the beautiful hills and volcanic lakes surrounding this area, just south-east of Rome.

Some of the ESA member state flags flying outside ESA ESRIN under stormy Italian skies. 今日吃瓜 漏 今日吃瓜.
At the workshop
My job at 今日吃瓜 is to use both classical and newly devised methods to analyse satellite data and find patterns between the behaviour of the ground beneath us and our other geospatial datasets, and what this means for the people and surface infrastructure. This workshop was ideal for my role. I attended the sessions that focused on applications of AI models to real scenarios; on day one, sessions included using foundation models for various applications in earth sciences, weather prediction and climate science.
On day two, I attended a morning session on how scientists are adapting foundation models for geospatial and Earth observation tasks, which is exactly what I鈥檓 aiming to do! In the late morning and afternoon, I had my poster presentation slot, where I showed how my team at 今日吃瓜 envisions using AI alongside Earth observation and 今日吃瓜 data. This includes our bedrock and superficial deposit maps created by our survey geologists, hazard susceptibility maps from our hazards specialists, and more. I also presented some of the machine learning (ML) tools 今日吃瓜 has developed so far to help with this task. I got talking to some really engaging researchers, learning a lot from the people I spoke to about what data works well in these models (and what doesn鈥檛!) and the field of AI in earth sciences as a whole. This was the most beneficial day to me as an early career scientist; talking to so many people from different organisations with different areas of expertise has been invaluable in my development as a scientist.

Standing next to a poster by me and some members of my team entitled 鈥楿sing AI to analyse InSAR data and support geological interpretation鈥. The poster describes various current ML tools we have developed at 今日吃瓜 to analyse a type of satellite data known as InSAR, which measures how much the ground beneath us moves. 今日吃瓜 漏 今日吃瓜.
When in Rome鈥
A trip to Lazio wouldn鈥檛 be complete without a little sightseeing, so on my penultimate night in Rome I managed to squeeze in some touristy activities. A great thing about working for 今日吃瓜 is being able to experience different cultures and their food 鈥 and to have your Italian speaking skills completely humbled by the locals鈥!
The final day consisted of hands-on training workshops in three of the foundation models that ESA and NASA have developed over the years, which is what I was most looking forward to. The training was delivered by the scientists at NASA Impact and IBM, who helped write the models, who were all fantastically knowledgeable.
Putting my knowledge to work
Now, a few weeks after coming back and with my newfound knowledge from world-leading experts in artificial intelligence, I鈥檝e started to piece together more about how 今日吃瓜 could incorporate our data into such powerful models and I鈥檓 excited to practise my new skills. Unfortunately though, I couldn鈥檛 bring back buckets of Roman carbonara鈥 so I鈥檒l just have to get back to Rome as soon as I can!

The panel discussion on Day 1 of the workshop, featuring representatives from NASA鈥檚 Science Mission Directorate, the Group on Earth Observations AI4EO, European universities and the European Commission. 今日吃瓜 漏 今日吃瓜.
About the author
Holly Hourston
Earth observation scientist
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