Towards Realistic Earth-Observation Constellation Scheduling: Benchmark and Methodology

• URL: http://arxiv.org/abs/2510.26297v1
• Date: Thu, 30 Oct 2025 09:31:47 GMT
• Title: Towards Realistic Earth-Observation Constellation Scheduling: Benchmark and Methodology
• Authors: Luting Wang, Yinghao Xiang, Hongliang Huang, Dongjun Li, Chen Gao, Si Liu,
• Abstract summary: We introduce a standardized benchmark suite and a novel scheduling model for Agile Earth Observation Satellites.<n>Our benchmark suite, AEOS-Bench, contains $3,907$ finely tuned satellite assets and $16,410$ scenarios.<n>Building upon this benchmark, we introduce AEOS-Former, a Transformer-based scheduling model that incorporates a constraint-aware attention mechanism.
• Score: 15.122110569996572
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Self-Improving World Modelling with Latent Actions [53.93276450137471]
  Internal modelling of the world is essential to reasoning and planning.<n>We propose SWIRL, a self-improvement framework that learns from state-only sequences.
  arXiv  Detail & Related papers  (2026-02-05T19:04:41Z)
• Data-Driven Generation of Neutron Star Equations of State Using Variational Autoencoders [1.5116328684219782]
  We develop a structured variational autoencoder (VAE) framework to reconstruct and generate neutron star equations of state (EOS)<n>VAE consists of an encoder network that maps high-dimensional EOS data into a lower-dimensional latent space and a decoder network that reconstructs the full EOS from the latent representation.<n>Based on a VAE trained on a Skyrme EOS dataset, we find that a latent space with two supervised NS observables, the maximum mass $(M_max)$ and the canonical radius $(R_1.4)$, can already reconstruct Skyrme EOSs with high
  arXiv  Detail & Related papers  (2026-01-29T03:48:05Z)
• Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method [54.461213497603154]
  Occupancy-centric methods have recently achieved state-of-the-art results by offering consistent conditioning across frames and modalities.<n>Nuplan-Occ is the largest occupancy dataset to date, constructed from the widely used Nuplan benchmark.<n>We develop a unified framework that jointly synthesizes high-quality occupancy, multi-view videos, and LiDAR point clouds.
  arXiv  Detail & Related papers  (2025-10-27T03:52:45Z)
• FLARE: Robot Learning with Implicit World Modeling [87.81846091038676]
  $textbfFLARE$ integrates predictive latent world modeling into robot policy learning.<n>$textbfFLARE$ achieves state-of-the-art performance, outperforming prior policy learning baselines by up to 26%.<n>Our results establish $textbfFLARE$ as a general and scalable approach for combining implicit world modeling with high-frequency robotic control.
  arXiv  Detail & Related papers  (2025-05-21T15:33:27Z)
• Adapting Segment Anything Model for Unseen Object Instance Segmentation [70.60171342436092]
  Unseen Object Instance (UOIS) is crucial for autonomous robots operating in unstructured environments. We propose UOIS-SAM, a data-efficient solution for the UOIS task. UOIS-SAM integrates two key components: (i) a Heatmap-based Prompt Generator (HPG) to generate class-agnostic point prompts with precise foreground prediction, and (ii) a Hierarchical Discrimination Network (HDNet) that adapts SAM's mask decoder.
  arXiv  Detail & Related papers  (2024-09-23T19:05:50Z)
• Semi-Supervised One-Shot Imitation Learning [83.94646047695412]
  One-shot Imitation Learning aims to imbue AI agents with the ability to learn a new task from a single demonstration. We introduce the semi-supervised OSIL problem setting, where the learning agent is presented with a large dataset of trajectories. We develop an algorithm specifically applicable to this semi-supervised OSIL setting.
  arXiv  Detail & Related papers  (2024-08-09T18:11:26Z)
• FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks [18.213174641216884]
  A large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space by commercial companies, such as SpaceX. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communication but also for various machine learning applications, such as space modulation recognition, remote sensing image classification, etc. We propose FedSN as a general FL framework to tackle the above challenges, and fully explore data diversity on LEO satellites.
  arXiv  Detail & Related papers  (2023-11-02T14:47:06Z)
• Multi-strip observation scheduling problem for ac-tive-imaging agile earth observation satellites [0.0]
  We investigate the multi-strip observation scheduling problem for an active-image agile earth observation satellite (MOSP) A bi-objective optimization model is presented along with an adaptive bi-objective memetic algorithm which integrates the combined power of an adaptive large neighborhood search algorithm (ALNS) and a nondominated sorting genetic algorithm II (NSGA-II) Our model is more versatile than existing models and provide enhanced capabilities in applied problem solving.
  arXiv  Detail & Related papers  (2022-07-04T08:35:57Z)
• Deep Learning for Real Time Satellite Pose Estimation on Low Power Edge TPU [58.720142291102135]
  In this paper we propose a pose estimation software exploiting neural network architectures. We show how low power machine learning accelerators could enable Artificial Intelligence exploitation in space.
  arXiv  Detail & Related papers  (2022-04-07T08:53:18Z)
• Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions [0.45687771576879593]
  We investigate the use of multi-agent allocation techniques on problems related to Earth observation scenarios with multiple users and satellites. As to solve EOSCSP, we propose market-based techniques and a distributed problem solving technique based on Distributed Constraint Optimization. These contributions are experimentally evaluated on randomly generated EOSCSP instances based on real large-scale or highly conflicting observation order books.
  arXiv  Detail & Related papers  (2021-06-04T09:34:20Z)
• Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions [69.47531199609593]
  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)
  arXiv  Detail & Related papers  (2020-03-13T09:38:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.