cs.NIApr 28, 2026

EOS-Bench: A Comprehensive Benchmark for Earth Observation Satellite Scheduling

Authors: Qian Yin, Jiaxing Li, Jiaqi Cheng, Qizhang Luo, Annalisa Riccardi, Abhijit Chatterjee, Rafael Vazquez, Carlo Novara, +18 more

Organizations: School of Traffic and Transportation Engineering, Central South University, Changsha 410083, China · School of Engineering and Materials Science, Queen Mary University of London, London E1 4NS, UK · College of Automation, Central South University, Changsha, 410083, China · Mechanical and Aerospace Engineering, University of Strathclyde, Glasgow G1 1XQ, UK · Department of Computer Science, University of Exeter, Exeter EX4 4QJ, UK · Department of Aerospace Engineering, Universidad de Sevilla, Camino de los Descubrimientos s.n., Sevilla, 41092, Spain · Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi, 24, Turin, 10129, Italy · ERATOSTHENES Centre of Excellence, Limassol, 3012, Cyprus · Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, 3036, Cyprus · Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar · Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, South Korea · School of Management, Hefei University of Technology, Hefei, 230009, China · Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xian 710071, China · School of Astronautics, Beihang University, 102206 Beijing, China · Advanced Space Technology Laboratory, College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China · National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xian, 710072, China · State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China · School of Computer Science, China University of Geosciences, Wuhan, 430074, China · School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China · Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, Canada · Division of Geological and Planetary Science, California Institute of Technology, CA, USA · Department of Automation and Systems Engineering, Federal University of Santa Catarina, Florianopolis, SC, Brazil · School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Braamfontein, Johannesburg, South Africa

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Abstract

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.

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