# Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions

arXiv: https://arxiv.org/abs/2003.06169 · HTML: https://mlsanity.com/paper/2003.06169/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 20 of 20)

- [RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problem](https://mlsanity.com/paper/2206.05694/) (2022)
- [Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities](https://mlsanity.com/paper/2308.13420/) (2023)
- [Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems](https://mlsanity.com/paper/2412.15573/) (2024)
- [Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions](https://mlsanity.com/paper/2106.03548/) (2021)
- [Towards Realistic Earth-Observation Constellation Scheduling: Benchmark and Methodology](https://mlsanity.com/paper/2510.26297/) (2025)
- [Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization](https://mlsanity.com/paper/2206.10178/) (2022)
- [An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problem](https://mlsanity.com/paper/2503.04803/) (2025)
- [Scalable Dynamic Distributed Constraint Optimization with Metareasoning and Application to Continual Satellite Operations](https://mlsanity.com/paper/2601.06188/) (2026)
- [A Constraint Programming Model for the Super-Agile Earth Observation Satellite Imaging Scheduling Problem](https://mlsanity.com/paper/2601.11967/) (2026)
- [An adaptive bi-objective optimization algorithm for the satellite image data downlink scheduling problem considering request split](https://mlsanity.com/paper/2207.00168/) (2022)
- [Earth Observation Satellite Scheduling with Graph Neural Networks and Monte Carlo Tree Search](https://mlsanity.com/paper/2408.15041/) (2024)
- [Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study](https://mlsanity.com/paper/2506.15207/) (2025)
- [On the Role of AI in Managing Satellite Constellations: Insights from the ConstellAI Project](https://mlsanity.com/paper/2507.15574/) (2025)
- [Efficient Policy Learning with Hybrid Evaluation-Based Genetic Programming for Uncertain Agile Earth Observation Satellite Scheduling](https://mlsanity.com/paper/2603.08447/) (2026)
- [Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach](https://mlsanity.com/paper/2604.13283/) (2026)
- [HADT: A Heterogeneous Multi-Agent Differential Transformer for Autonomous Earth Observation Satellite Cluster](https://mlsanity.com/paper/2605.31023/) (2026)
- [Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling](https://mlsanity.com/paper/2607.25835/) (2026)
- [Task-Driven Three-Layer Distributed Scheduling for Emergency Earth Observation in Large Low-Earth-Orbit Constellations](https://mlsanity.com/paper/2608.14789/) (2026)
- [Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling](https://mlsanity.com/paper/2608.24470/) (2026)
- [Implicit Q-learning-bootstrapped ant colony optimization for maritime moving-target observation scheduling with agile satellites](https://mlsanity.com/paper/2608.24471/) (2026)

## Abstract (arXiv)

Agile satellites with advanced attitude maneuvering capability are the new generation of Earth observation satellites (EOSs). The continuous improvement in satellite technology and decrease in launch cost have boosted the development of agile EOSs (AEOSs). To efficiently employ the increasing orbiting AEOSs, the AEOS scheduling problem (AEOSSP) aiming to maximize the entire observation profit while satisfying all complex operational constraints, has received much attention over the past 20 years. The objectives of this paper are thus to summarize current research on AEOSSP, identify main accomplishments and highlight potential future research directions. To this end, general definitions of AEOSSP with operational constraints are described initially, followed by its three typical variations including different definitions of observation profit, multi-objective function and autonomous model. A detailed literature review from 1997 up to 2019 is then presented in line with four different solution methods, i.e., exact method, heuristic, metaheuristic and machine learning. Finally, we discuss a number of topics worth pursuing in the future.