Dagstuhl Seminar 23461
Space and Artificial Intelligence
( Nov 12 – Nov 17, 2023 )
- Saso Dzeroski (Jozef Stefan Institute - Ljubljana, SI)
- Holger H. Hoos (RWTH Aachen, DE)
- Bertrand Le Saux (ESA - Frascati, IT)
- Leon van der Torre (University of Luxembourg, LU)
- Michael Gerke (for scientific matters)
- Christina Schwarz (for administrative matters)
Monitoring climate change by using satellites measurements, using autonomous rovers to explore other planets, identifying celestial objects using astronomical observatories in orbit: these are just a few tasks where Artificial Intelligence (AI), and especially Machine Learning (ML), are helping space-related research and applications. With the intensification of space exploration, on one hand, and the rapid development of the field of AI, on the other hand, the time is ripe for a Dagstuhl Seminar to discuss current and novel space-related applications of AI, e.g., in Space Operations and Earth Observation, cutting edge and upcoming AI approaches to be used for this purpose, and challenges AI needs to address in this context, that will likely lead to new AI science.
This Dagstuhl Seminar covers topics of AI relevant for space-related applications, focusing on four major topics structured along two dimensions (AI approaches and Space applications):
- Data-driven AI, e.g., ML, for space. This topic addresses ML methods which can be used to analyze the ever larger quantities of data resulting from space related research and exploration, their current state-of-the-art and directions for further development.
- Knowledge-driven AI, e.g., explainable AI, for space. This topic is concerned with methods and techniques from knowledge representation and reasoning, as well as explainable AI, where the results of AI solutions can be understood by humans. It considers the current state-of-the-art in this area and explores directions for further development.
- Space Operations applications of AI. This topic encompasses various aspects of operating spacecraft and managing missions, many potential applications of AI approaches in this area, and the challenges these applications pose for AI methods.
- Earth Observation applications of AI. This topic of the seminar includes different aspects of applying AI to Earth observation data, a variety of potential applications of AI in this area, and the challenges they pose for AI methods.
Based on the interests of the participants of the seminar, we will also consider additional topics, such as legal, ethical and social aspects of Space AI; space robotics; or other AI applications, e.g., in astronomy.
The seminar will include concise tutorials, bringing participants from the different disciplines on the same page. It will also include brief contributed talks by the participants, introducing topics for discussion, further development and interactions. Most of the time will go to discussions and working sessions pursuing the main goals of the seminar outlined below.
With this Dagstuhl Seminar, we hope to: (1) Give researchers across the contributing disciplines an integrated overview of current research in the area of AI for space. (2) Reinforce the communication channel for researchers from different disciplines tackling challenges in space applications using AI, bridging the divide between computer science and space research. (3) Define the landscape of potential applications of AI in space, in particular in the areas of Space Operations and Earth Observation. (4) Identify the central research questions and challenges for AI approaches that need to be resolved for successful use of AI in space applications. (5) Produce a road-map of strategies for designing AI tools for space applications and for developing benchmarking suites for evaluating such tools.
To this end, the seminar will bring together a diverse set of players. This will include researchers from academia, on one hand, and practitioners from space agencies (ESA, NASA, JAXA) and industry, on the other hand. In this way, most relevant aspects for the further development of the field will be covered.
- Artificial Intelligence
- Machine Learning
- artificial intelligence
- machine learning
- space operations
- earth observation