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.

翻译：敏捷地球观测卫星（AEOS）星座为地表监测提供了前所未有的灵活性，但在大规模场景、动态环境和严格约束下的调度问题仍具挑战性。现有方法常简化这些复杂性，限制了其实际应用性能。我们通过整合标准化基准套件与新型调度模型的统一框架来填补这一空白。我们的基准套件AEOS-Bench包含$3,907$个精细调校的卫星资产和$16,410$个场景。每个场景包含$1$至$50$颗卫星及$50$至$300$个成像任务。这些场景通过高保真仿真平台生成，确保了轨道动力学与资源约束等真实卫星行为。每个场景均提供基准调度标注。据我们所知，AEOS-Bench是首个针对真实星座调度定制的大规模基准套件。基于此基准，我们提出AEOS-Former——一种集成约束感知注意力机制的Transformer调度模型。其专用内部约束模块显式建模每颗卫星的物理与操作限制。通过基于仿真的迭代学习，AEOS-Former能适应多样化场景，为AEOS星座调度提供鲁棒解决方案。实验结果表明，AEOS-Former在任务完成率与能源效率上均优于基线模型，消融实验验证了各模块的贡献。代码与数据公开于https://github.com/buaa-colalab/AEOSBench。