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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.25782 |
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| _version_ | 1866913069550010368 |
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| author | Yin, Qian Li, Jiaxing Cheng, Jiaqi Luo, Qizhang Riccardi, Annalisa Chatterjee, Abhijit Vazquez, Rafael Novara, Carlo Mavrovouniotis, Michalis Suganthan, Ponnuthurai Nagaratnam Bai, Shengzhou Hu, Xiaoxuan Xing, Lining Xu, Ming Li, Shuang Zheng, Zixuan Shen, Xin Chen, Xiaoyu Gu, Yi Song, Yanjie Pedrycz, Witold Kramer, Evan L. Seman, Laio Oriel Shoko, Cletah Wu, Guohua Wang, Xinwei |
| author_facet | Yin, Qian Li, Jiaxing Cheng, Jiaqi Luo, Qizhang Riccardi, Annalisa Chatterjee, Abhijit Vazquez, Rafael Novara, Carlo Mavrovouniotis, Michalis Suganthan, Ponnuthurai Nagaratnam Bai, Shengzhou Hu, Xiaoxuan Xing, Lining Xu, Ming Li, Shuang Zheng, Zixuan Shen, Xin Chen, Xiaoyu Gu, Yi Song, Yanjie Pedrycz, Witold Kramer, Evan L. Seman, Laio Oriel Shoko, Cletah Wu, Guohua Wang, Xinwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25782 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EOS-Bench: A Comprehensive Benchmark for Earth Observation Satellite Scheduling Yin, Qian Li, Jiaxing Cheng, Jiaqi Luo, Qizhang Riccardi, Annalisa Chatterjee, Abhijit Vazquez, Rafael Novara, Carlo Mavrovouniotis, Michalis Suganthan, Ponnuthurai Nagaratnam Bai, Shengzhou Hu, Xiaoxuan Xing, Lining Xu, Ming Li, Shuang Zheng, Zixuan Shen, Xin Chen, Xiaoyu Gu, Yi Song, Yanjie Pedrycz, Witold Kramer, Evan L. Seman, Laio Oriel Shoko, Cletah Wu, Guohua Wang, Xinwei Networking and Internet Architecture Robotics 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. |
| title | EOS-Bench: A Comprehensive Benchmark for Earth Observation Satellite Scheduling |
| topic | Networking and Internet Architecture Robotics |
| url | https://arxiv.org/abs/2604.25782 |