Earth observation satellite imaging scheduling is a challenging NP-hard combinatorial optimisation problem central to space mission operations. While next-generation agile Earth observation satellites (EOS) increase operational flexibility, they also significantly raise scheduling complexity. The lack of a unified, open-source benchmark makes it difficult to compare algorithms across studies. This paper introduces EOS-Bench, a comprehensive framework for systematic and reproducible evaluation of scheduling methods. By integrating high-fidelity orbital dynamics and platform constraints, EOS-Bench generates 1,390 scenarios and 13,900 benchmark instances, spanning from small-scale validation cases to large coordination problems with up to 1,000 satellites and 10,000 requests. We further propose a scenario characterisation scheme to quantify structural difficulty based on factors such as opportunity density, task flexibility, conflict intensity, and satellite congestion. A multidimensional evaluation protocol is introduced, assessing performance across five metrics: task profit, completion rate, workload balance, timeliness, and runtime. The framework is evaluated using mixed-integer programming, heuristics, meta-heuristics, and deep reinforcement learning across both agile and non-agile settings. Results show that EOS-Bench effectively distinguishes solver performance across scales and conditions, revealing trade-offs between solution quality and computational efficiency, and providing deeper insight into scenario complexity. EOS-Bench offers a unified and extensible open testbed for advancing research in Earth observation satellite scheduling. The code and data are available at https://github.com/Ethan19YQ/EOS-Bench.

翻译：地球观测卫星成像调度是一个具有挑战性的NP难组合优化问题，是空间任务运行的核心环节。尽管新一代敏捷型地球观测卫星（EOS）提升了操作灵活性，但也显著增加了调度复杂度。由于缺乏统一的开源基准测试，不同研究间算法比较十分困难。本文提出EOS-Bench这一系统性、可复现的调度方法评估综合框架。通过集成高保真轨道动力学与平台约束，EOS-Bench生成了1,390个场景和13,900个基准实例，覆盖从小规模验证案例到包含上千颗卫星与上万次请求的大规模协调问题。我们进一步提出场景特征化方案，基于机会密度、任务灵活性、冲突强度及卫星拥挤度等因子量化结构难度。引入多维评估协议，通过任务收益、完成率、负载均衡、时效性与运行时间五项指标评估性能。该框架在敏捷与非敏捷场景下通过混合整数规划、启发式、元启发式及深度强化学习方法进行了验证。结果表明，EOS-Bench能有效区分不同规模与条件下求解器的性能，揭示解质量与计算效率间的权衡，并深化对场景复杂度的认知。EOS-Bench为推进地球观测卫星调度研究提供了统一且可扩展的开放测试平台。相关代码与数据已开源至https://github.com/Ethan19YQ/EOS-Bench。