--The emergence of Agile Earth Observation Satellites (AEOSs) has marked a significant turning point in the field of Earth Observation (EO), offering enhanced flexibility in data acquisition. Concurrently, advancements in onboard satellite computing and communication technologies have greatly enhanced data compression efficiency, reducing network latency and congestion while supporting near real-time information delivery. In this paper, we address the Agile Earth Observation Satellite Scheduling Problem (AEOSSP), which involves determining the optimal sequence of target observations to maximize overall observation profit. T o this end, we define a set of priority indicators and develop a constructive heuristic method, further enhanced with a Local Search (LS) strategy. The results show that the proposed algorithm provides high-quality information by increasing the resolution of the collected frames by up to 10% on average, while reducing the variance in the monitoring frequency of the targets within the instance by up to 83%, ensuring more up-to-date information across the entire set compared to a First-In First-Out (FIFO) method.

An energy-efficient learning solution for the Agile Earth Observation Satellite Scheduling Problem Antonio M. Mercado-Mart ınez, Beatriz Soret Senior Member, IEEE, Antonio Jurado-Navas Member, IEEE Abstract --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. I NTRODUCTION One of the most relevant advances in the realm of Earth Observation (EO) has been the introduction of Agile Earth Observation Satellites (AEOS) [1]. Unlike Conventional Earth Observation Satellites (CEOS), which can only adjust their attitude along the roll axis, AEOS have a strong attitude adjustment capability along three axes (roll, pitch, and yaw).

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.