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[2509.07997] Learning-Based Planning for Improving Science Return of Earth Observation Satellites
Summary
The paper presents learning-based approaches to dynamic targeting for Earth observation satellites, demonstrating improved scientific data collection through reinforcement and imitation learning methods.
Why It Matters
This research is significant as it addresses the limitations of Earth observation satellites in optimizing data collection. By employing learning-based strategies, the study enhances the efficiency of satellite operations, potentially leading to better scientific insights and resource management in space exploration.
Key Takeaways
- Dynamic targeting can significantly improve the science return of Earth observation satellites.
- Reinforcement learning outperforms heuristic methods by an average of 13.7%.
- Imitation learning shows a 10.0% improvement over the best heuristic approach.
- Both learning methods can be effectively trained with limited data.
- The study highlights the importance of optimizing resource use in satellite operations.
Computer Science > Artificial Intelligence arXiv:2509.07997 (cs) [Submitted on 5 Sep 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Learning-Based Planning for Improving Science Return of Earth Observation Satellites Authors:Abigail Breitfeld, Alberto Candela, Juan Delfa, Akseli Kangaslahti, Itai Zilberstein, Steve Chien, David Wettergreen View a PDF of the paper titled Learning-Based Planning for Improving Science Return of Earth Observation Satellites, by Abigail Breitfeld and 6 other authors View PDF Abstract:Earth observing satellites are powerful tools for collecting scientific information about our planet, however they have limitations: they cannot easily deviate from their orbital trajectories, their sensors have a limited field of view, and pointing and operating these sensors can take a large amount of the spacecraft's resources. It is important for these satellites to optimize the data they collect and include only the most important or informative measurements. Dynamic targeting is an emerging concept in which satellite resources and data from a lookahead instrument are used to intelligently reconfigure and point a primary instrument. Simulation studies have shown that dynamic targeting increases the amount of scientific information gathered versus conventional sampling strategies. In this work, we present two different learning-based approaches to dynamic targeting, using reinforcement and imitation learning, respectively. These learning methods ...
