Towards Realistic Earth-Observation Constellation Scheduling: Benchmark and Methodology

TL;DR

This paper introduces AEOS-Bench, a large-scale realistic benchmark suite for Earth observation satellite constellation scheduling, and proposes AEOS-Former, a Transformer-based model that effectively handles complex constraints and improves scheduling performance.

Contribution

The paper presents the first large-scale realistic benchmark suite for AEOS scheduling and a novel Transformer-based model incorporating constraint-aware attention for improved performance.

Findings

AEOS-Former outperforms baseline models in task completion.

AEOS-Former improves energy efficiency in scheduling.

The benchmark suite enables realistic evaluation of scheduling algorithms.

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.