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Make AI in space make sense

#HYPERVIEW2 // EASi // ECAI

As we increasingly rely on AI for satellite operations, Earth observation, and mission-critical decisions, the question isn’t just what AI predicts—but why. EASi and HYPERVIEW 2 by KP Labs, MI² (Warsaw University of Technology), Poznan University of Technology and ESA Φ-Lab tackle the frontier of Explainable AI (XAI) for space, spotlighting the urgent need for trust, clarity, and accountability in high-stakes environments.

From understanding atmospheric changes to operating spacecraft autonomously, AI decisions impact real missions and real lives. But without transparency, we risk losing trust in these systems—especially when lives, missions, or environmental decisions are on the line.

With HYPERVIEW2, we’re challenging researchers, engineers, and data scientists to explore new ways of making AI interpretable and explainable for satellite data processing, space operations, and Earth monitoring.

Challenge? Workshop? Both!

The new HYPERVIEW2 Challenge is implemented as part of the Explainable AI in Space (EASi) workshop, that KP Labs, ESA, and the AI4EO team are organising as part of the European Conference on Artificial Intelligence (ECAI), which is hosted in Bologna, Italy from 25th–30th October 2025.

Participants are welcome to engage in multiple ways: you can take part in the challenge only, submit a paper to the workshop only, or combine both for a deeper contribution. Submitting a paper is not required to join the challenge, and vice versa—though we strongly encourage participants in the challenge to consider sharing their methods and insights through a workshop submission. This flexible format allows researchers, engineers, and AI practitioners to participate in the way that best suits their expertise and goals. Whether you’re aiming to publish, to compete, or to do both, EASi and HYPERVIEW2 provide a shared platform to advance the frontiers of trustworthy, explainable AI in space applications.

 

Transparency beyond the stratosphere

As space exploration and Earth observation increasingly rely on artificial intelligence (AI) for mission-critical tasks such as satellite operations, data analysis, and decision-making, ensuring the reliability, interpretability, and accountability of these AI systems becomes paramount. The EASi workshop addresses a critical intersection between Explainable Artificial Intelligence (XAI) and space applications, offering broad relevance to multiple research and industrial communities beyond traditional AI approaches. We promote methods to make AI models more explainable and transparent, particularly in safety-critical and high-stakes environments like space, targeting the topics having far-reaching implications across various sectors:

• Aerospace: The safety, reliability & transparency of AI in space applications impact both governmental space agencies and private sector efforts. Ensuring that AI-driven systems can be trusted, explained, and verified in mission-critical environments is crucial to avoiding catastrophic failures & ensuring public trust.
  
  
• Earth Observation and Environmental Sciences: With AI systems playing an increasing role in analyzing vast amounts of satellite imagery and data to monitor climate change, disasters, and ecosystem health, explainability and interpretability are essential for accurate, reliable insights. Transparency in AI decisions allows scientists, policymakers, and stakeholders to trust and act on AI-generated insights. Decision Intelligence further enhances this process by integrating AI-driven insights with human expertise, enabling more informed and effective decision-making in addressing environmental challenges.
  
• AI Research Community: The methods and techniques discussed are applicable beyond space. They offer valuable insights for improving AI transparency in other fields such as finance, healthcare, robotics, and automotive industries, where trust in AI decisions is critical.
  

 

The topics of interest at EASi include, but are not limited to: 

“Where XAI meets space”

Counterfactual explanations in Space applications	Presentation and interpretation of AI explanations in Space applications
Explainable on-board AI for critical and non-critical Space applications	Safety of AI models for Space applications (Earth observation, mission-critical)
Explaining AI models for Earth observation and satellite operations	Techniques for assessing the quality of explanations in Space applications
Physics-aware AI for Earth observation and satellite operations	Trustworthiness of AI systems in Space applications
Realistic generative AI models for optical and radar Earth observation	Verification and validation of AI in Space applications
AI specific Cal/Val paradigms	XAI methods for explaining adversarial attacks in Space
Theoretical bound for AI methods in Space applications	XAI methods for signal and image processing for Space applications
Exploring the potential impact of incorrect explanations in Space applications	XAI methods for risk assessment in critical Space applications
Multimodal XAI methods for Space applications	XAI methods for explaining federated and continual learning in Space
Simple, fast, low power, low bit rate AI for Space applications	XAI methods for privacy-preserving Space systems
Non-technical presentation of AI explanations in Space operations	XAI methods for model governance in Space applications
On-board explainable AI for Earth observation and satellite operations	XAI methods for situational awareness in Space
Optimization of AI models using XAI for Space applications	XAI methods for ensuring algorithmic transparency in Space applications

 

“XAI techniques and tools for (not only) space”

Benchmarking of XAI systems	Robustness and faithfulness of explanations
Dataset-centric explanations	XAI for time series analysis approaches
Explaining bias and fairness of XAI systems	XAI methods for estimating AI models’ confidence
Resource utilization and resource frugality of on-board XAI methods

 

 

Programme

The full EASi program will be announced soon.

 

Keynote speakers

The keynote speakers will be announced soon.

 

Important dates

Please note that we have deadlines to meet both for your participation at the EASi workshop as well as for the HYPERVIEW2 deadline

EASi paper submission time and deadlines Paper submission deadline: 26th June 2025 Notification to authors: 26th July 2025  Camera-ready deadline: TBA at a later date (deadline likely in mid-August 2025) EASi finalized program: TBA at a later date (deadline likely end of August 2025) EASi date: 25-26th October 2025

HYPERVIEW2 Challenge time and deadlines Challenge launch: 09th May 2025 Online workshop (introduction to HYPERVIEW2): Date announced soon XAI checkpoint 1 : 30th June 2025 Reproducibility check (top 10) 2 : 30th June – 11th July 2025 Release of top-performing models to other teams for XAI: 14th July 2025 Challenge closing: 31st August 2025 Award ceremony (during EASi workshop!): 25-26th October 2025

¹ Checkpoint to take top-5 best models for XAI.

² Verification of reproducibility of top-10 best models. The top 10 best performing teams will be asked to submit thier Jupyter notebooks with their models.

 

 

 

Prizes for the HYPERVIEW2 Challenge

 

• 1^st Place: 2000 EUR + 1-month access to Leopard DPU by KP Labs through Smart Mission Lab to benchmark the models on flight hardware with KP Labs’ support (+ an option to widely promote the benchmarking results through KP Labs’ social media) + diploma
• 2^nd Place: 1000 EUR + 1-month access to Leopard DPU by KP Labs through Smart Mission Lab to benchmark the models on flight hardware + diploma
• 3^rd Place: 500 EUR + 1-month access to Leopard DPU by KP Labs through Smart Mission Lab to benchmark the models on flight hardware + diploma

 

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Meet the team

Organizers and Chairs 

Przemysław Biecek Warsaw University of Technology, MI².AI Poland

 

Marek Kraft Poznan University of Technology, Poland

 

Nicolas Longépé Φ-Lab, European Space Agency Italy

 

Jakub Nalepa Silesian University of Technology, KP Labs Poland

 

Evridiki Ntagiou European Space Operations Centre, European Space Agency Germany

 

Lukasz Tulczyjew KP Labs, Silesian University of Technology Poland

 

Agata M. Wijata Silesian University of Technology, KP Labs Poland

 

Technical and Scientific Co-Chairs

Przemyslaw Aszkowski Poznan University of Technology Poland

 

Hubert Baniecki University of Warsaw Poland

 

Gabriele Cavallaro University of Iceland, Forschungszentrum Jülich Germany

 

Mihai Datcu University Politehnica Bucharest Romania

 

Nataliia Kussul University of Maryland United States of America

 

Tymoteusz Kwieciński Warsaw University of Technology Poland

 

Ribana Roscher Forschungszentrum Jülich, University of Bonn Germany

 

Bogdan Ruszczak Opole University of Technology Poland

 

Vladimir Zaigrajew MI².AI Poland

 

Scientific Committee 

Agata Wijata, Silesian University of Technology, KP Labs  Andrew Macdonald, Mission Control  Andrei Anghel, POLITEHNICA Bucharest  Begüm Demir, TU Berlin, BIFOLD  Diego Valsesia, Politecnico di Torino  Eduardo Soares, IBM Research – Brazil  Enrico Magli, Politecnico di Torino  Evgenios Tsigkanos, OHB Hellas  Evridiki Ntagiou, European Space Agency (ESA)  Fabio Del Frate, University of Rome “Tor Vergata”  Francesco Spinnato, University of Pisa  Gabriele Cavallaro, Forschungszentrum Jülich, Univ. of Iceland  Gianluca Valentino, University of Malta  Hubert Baniecki, University of Warsaw  Jakub Nalepa, Silesian University of Technology, KP Labs  Jocelyn Chanussot, INRIA  Jon Alvarez Justo, Norwegian Univ. of Science and Technology, ESA  Krzysztof Kotowski, KP Labs

Leonardo De Laurentiis, European Space Agency (ESA)  Mahtab Sarvmaili, Dalhousie University  Mattia Setzu, University of Pisa  Mihail Datcu, Politehnica Bucharest  Nataliia Kussul, University of Maryland  Nicolas Longepe, European Space Agency (ESA)  Nikolaos Dionelis, European Space Agency (ESA)  Peter Naylor, European Space Agency (ESA)  Plamen Angelov, Lancaster University  Przemysław Biecek, Warsaw University of Technology  Roberto Camarero, European Space Agency (ESA)  Roberto Del Prete, University of Naples Federico II  Samantha Lavender, Telespazio UK  Sebastian Lapuschkin, Fraunhofer HHI  Thomas Brunschwiler, IBM Research – Europe  Vincenzo Lomonaco, University of Pisa, ContinualAI  Wojciech Samek, TU Berlin, BIFOLD, Fraunhofer HHI  Xiaoxiang Zhu, Technical University of Munich