# An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problem

arXiv: https://arxiv.org/abs/2503.04803 · HTML: https://mlsanity.com/paper/2503.04803/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions](https://mlsanity.com/paper/2003.06169/) (2020)
- [Semantic and goal-oriented edge computing for satellite Earth Observation](https://mlsanity.com/paper/2408.15639/) (2024)

## Built on by (most cited first, 2 of 2)

- [An Automated Tip-and-Cue Framework for Optimized Satellite Tasking and Visual Intelligence](https://mlsanity.com/paper/2512.09670/) (2025)
- [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)

The Agile Earth Observation Satellite Scheduling Problem (AEOSSP) entails finding the subset of observation targets to be scheduled along the satellite's orbit while meeting operational constraints of time, energy and memory. The problem of deciding what and when to observe is inherently complex, and becomes even more challenging when considering several issues that compromise the quality of the captured images, such as cloud occlusion, atmospheric turbulence, and image resolution. This paper presents a Deep Reinforcement Learning (DRL) approach for addressing the AEOSSP with time-dependent profits, integrating these three factors to optimize the use of energy and memory resources. The proposed method involves a dual decision-making process: selecting the sequence of targets and determining the optimal observation time for each. Our results demonstrate that the proposed algorithm reduces the capture of images that fail to meet quality requirements by > 60% and consequently decreases energy waste from attitude maneuvers by up to 78%, all while maintaining strong observation performance.