Decentralised EO

In the vein of collaboration between the Web3 and Earth Observation (EO) communities, we articulate our ethos for the convergence of blockchain, AI, and EO.


Data integrity and trust

We emphasise the importance of keeping Earth Observation data accurate, trustworthy and verifiable. Decentralised technologies play a key role here, making sure that the data we get from remote sensing is correct and secure.

Data ownership, security and selective disclosure

We believe in the power of data ownership. People and organisations should retain ownership and/or control over the data, and be able to choose what they share. ​​European Space Agency data cannot be recorded, only accessed on a per-need basis, enabling utilisation without appropriation. Moreover, the European Space Agency controls the context in which its data are produced and processed and how that context is activated.

Empowerment through technology

We highlight how emerging technologies, including blockchain and AI, can make Earth Observation more powerful. These technologies help us keep the data safe and share it transparently.

Commitment to building solutions

In line with the proactive spirit of problem-solving, we are committed to developing and implementing systems that keep Earth Observation data accurate, trustworthy, verifiable and shareable, while at the same time giving researchers and organisations data ownership.

Advocacy for open data

We stand for the free flow of information. While we ensure that the data is reliable and private, we also believe in the importance of openly sharing information


Data integrity and accessibility

Ensuring that the output of AI models is not corrupted, maintaining secure communication and ensuring data availability.

Powerful, verifiable storage

Addressing the need for massive storage capacity, privacy, data provenance, trustless computation, data provenance, and the need for verifiable data in legal or critical decision-making scenarios. This includes leveraging decentralised networks and implementing for transparent tracking and validation of model development, ensuring that each phase of research, from data collection to model output, is authenticated and traceable in a tamper-proof ledger

Tokenisation of EO Data

Challenges include establishing ownership, transforming data into assets, standardising data to reduce friction, and designing and aligning incentives for data sharing and research.


A foundation for future technologies and data-centered

We envision creating a system where EO data is beyond reproach, fostering an environment of trust not just among experts but also in the public domain. This aims to break down barriers, making crucial EO data accessible to a broader audience, empowering individuals, communities and organisations to make informed decisions based on reliable data.

In line with the ethos of the Web3 ecosystem, we envision a future where EO data empower public goods, accessible and beneficial to all.

We aim to ay down a foundation that will not only support current technological needs but also adapt and evolve with future advancements, ensuring long-term sustainability and relevance and positive social impact.

Next steps

Joining forces

Initiate a call for proposals to fund research and development projects that align with the integration of blockchain, decentralised storage, AI and Earth Observation (EO) data. Establish bounties to encourage the development of solutions and innovations that address specific challenges identified in the workshops.

Sharing resources

Schedule regular meetings with research fellows to monitor progress, discuss challenges and align ongoing research with the workshop's objectives.

Promotion and community engagement

Actively promote the findings and discussions from the workshops to a broader audience, including stakeholders, industry partners, and academic institutions. Develop and distribute demonstrations or prototypes that showcase the practical applications of integrating blockchain, AI and EO data. Engage with open source communities to help in developing offline data formats and other relevant technologies.

Exploring real AI use cases with federated learning

Investigate practical applications of AI in the context of federated learning, particularly in the management and analysis of EO data.

Defining collaborative projects between Swarm Foundation and Φ-Lab

Establish specific collaborative projects between the Swarm Foundation and ESA's Φ-Lab, focusing on the integration of decentralised storage solutions and federated learning in EO data processing. These projects will involve other Web3 players that took part in the workshop.

List of workshop participants

  1. Costanza Gallo, Head of Partnerships, Swarm Foundation
  2. Gregor Žavcer, Director at Swarm Foundation
  3. Črt Ahlin, Ops, Research, Swarm Foundation
  4. Niki Papadatou, Product Owner, Swarm Foundation
  5. Elad Verbin, Lead Scientist & Founding Partner at Lunar Ventures
  6. Paolo Rollo, Founder, Brian AI
  7. Tadej Fius, CTO Datafund
  8. Marco Mosh (Grendel)i, Director, Polygon Labs
  9. Muhammed Tanrıkulu, Developer, ENS Labs
  10. Nicolas Longepe, EO Data Scientist, ESA
  11. Alessandro Sebastianelli, Research Fellow, ESA
  12. Sabrina Ricci, AI Ecosystem Manager, ESA
  13. Peter Naylor, Research Fellow, ESA
  14. Mikolaj Czerkawski, Research Fellow, ESA
  15. Nikoalaos Dionelis, Research Fellow, ESA
  16. Claudio Iacopino, EO Digital Platform Engineer, ESA
  17. Roberto Alacevich, EO Services Coordinator, ESA
  18. Mattia Stipa, EO service manager, ESA
  19. Andrea Della Vecchia, EO Data Access System Engineer, ESA
  20. Albin Lacroix, Incubed Project Manager, ESA
  21. Giuseppe Borghi, Head of Φ-lab Division, ESA
  22. Robert Cowlishaw, Researcher, University of Strathclyde
  23. Mihaela Violeta Gheorghe, Project Manager, GMV
  24. Derek Ding, Co-founder, Orbuculum (DLTEO GmbH)
  25. Red Boumghar, CEO and Founder @


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