Towards Realistic Earth-Observation Constellation Scheduling: Benchmark and Methodology

Luting Wang, Yinghao Xiang, Hongliang Huang, Dongjun Li, Chen Gao, Si Liu

Keywords: Task Scheduling, Satellite Constellation, Realistic, Benchmark, Transformer

Abstract:

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.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 2168