# Towards Realistic Earth-Observation Constellation Scheduling: Benchmark and Methodology

arXiv: https://arxiv.org/abs/2510.26297 · HTML: https://mlsanity.com/paper/2510.26297/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions](https://mlsanity.com/paper/2003.06169/) (2020)
- [Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems](https://mlsanity.com/paper/2412.15573/) (2024)
- [Markov Decision Processes For Multi-Objective Satellite Task Planning](https://mlsanity.com/paper/1910.08419/) (2019)
- [Attitude Trajectory Optimization for Agile Satellites in Autonomous Remote Sensing Constellation](https://mlsanity.com/paper/2102.07940/) (2021)

## Built on by (most cited first, 3 of 3)

- [Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach](https://mlsanity.com/paper/2604.13283/) (2026)
- [Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling](https://mlsanity.com/paper/2608.24470/) (2026)
- [Earth-Agent-Pro: Towards Real-World Full-Chain Earth Observation with Agents](https://mlsanity.com/paper/2609.12533/) (2026)

## Abstract (arXiv)

Agile Earth Observation Satellites (AEOSs) constellations offer unprecedented flexibility for monitoring the Earth's surface, but their scheduling remains challenging under large-scale scenarios, dynamic environments, and stringent constraints. Existing methods often simplify these complexities, limiting their real-world performance. We address this gap with a unified framework integrating a standardized benchmark suite and a novel scheduling model. Our benchmark suite, AEOS-Bench, contains 3,907 finely tuned satellite assets and 16,410 scenarios. Each scenario features 1 to 50 satellites and 50 to 300 imaging tasks. These scenarios are generated via a high-fidelity simulation platform, ensuring realistic satellite behavior such as orbital dynamics and resource constraints. Ground truth scheduling annotations are provided for each scenario. To our knowledge, AEOS-Bench is the first large-scale benchmark suite tailored for realistic constellation scheduling. Building upon this benchmark, we introduce AEOS-Former, a Transformer-based scheduling model that incorporates a constraint-aware attention mechanism. A dedicated internal constraint module explicitly models the physical and operational limits of each satellite. Through simulation-based iterative learning, AEOS-Former adapts to diverse scenarios, offering a robust solution for AEOS constellation scheduling. Experimental results demonstrate that AEOS-Former outperforms baseline models in task completion and energy efficiency, with ablation studies highlighting the contribution of each component. Code and data are provided in https://github.com/buaa-colalab/AEOSBench.