# Task-Driven Three-Layer Distributed Scheduling for Emergency Earth Observation in Large Low-Earth-Orbit Constellations

arXiv: https://arxiv.org/abs/2608.14789 · HTML: https://mlsanity.com/paper/2608.14789/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions](https://mlsanity.com/paper/2003.06169/) (2020)
- [Simulated annealing based heuristic for multiple agile satellites scheduling under cloud coverage uncertainty](https://mlsanity.com/paper/2003.08363/) (2020)
- [RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problem](https://mlsanity.com/paper/2206.05694/) (2022)
- [A Maximum Independent Set Method for Scheduling Earth Observing Satellite Constellations](https://mlsanity.com/paper/2008.08446/) (2020)
- [Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization](https://mlsanity.com/paper/2206.10178/) (2022)
- [Scheduling multiple agile Earth observation satellites with multiple observations](https://mlsanity.com/paper/1812.00203/) (2018)
- [Bottom-up mechanism and improved contract net protocol for the dynamic task planning of heterogeneous Earth observation resources](https://mlsanity.com/paper/2007.06172/) (2020)
- [Multi-strip observation scheduling problem for ac-tive-imaging agile earth observation satellites](https://mlsanity.com/paper/2207.01257/) (2022)

## Abstract (arXiv)

Large low-Earth-orbit (LEO) Earth-observation (EO) constellations offer frequent access to geographically dispersed ground targets, but emergency requests may arrive after committed routine-plan execution has begun. The resulting dynamic emergency observation scheduling problem (DEOSP) requires urgent tasks to be inserted under intermittent ground contact without excessive routine-plan disruption. To address DEOSP, we propose a task-driven three-layer distributed scheduling (T3L-DS) method, which represents task demand and sensor footprints on a common geographic grid and forms temporary clusters from observation capabilities and current inter-satellite links. For intra-cluster coordination, T3L-DS introduces onboard dual-plan bidding and joint marginal evaluation. It also designs an inter-cluster coordination mechanism for unresolved demand. Extensive computational experiments compare T3L-DS with centralised simulated annealing (SA), an adapted selective time-variant better reply process (A-SeTVBRP), and a conventional contract-net protocol (CNP). T3L-DS achieves the highest emergency coverage among the distributed methods, with average relative improvements of approximately 2.8% and 17.1% over A-SeTVBRP and CNP, respectively. Its average relative gap from SA is approximately 7.1%. Under conflict-enhanced loads, it reduces routine-coverage loss by approximately 57.9% and 87.7% relative to A-SeTVBRP and CNP, respectively. The ablation study confirms the contribution of the proposed coordination enhancements. Overall, the results show that T3L-DS provides an effective distributed approach to DEOSP.