Title: Towards Realistic Earth-Observation Constellation Scheduling: Benchmark and Methodology
Venue: NeurIPS 2025
Accepted: yes
Keywords: Task Scheduling

Satellite Constellation

Realistic

Benchmark

Transformer
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors make two contributions towards the scheduling of a constellation of agile earth observation satellites: 1) a benchmark suite including a simulator which is more detailed and contains more and larger instances, and 2) a novel transformer-based method for finding solutions to this type of scheduling problems. The new approach scores better than the four baselines (inc. random).\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- two good quality contributions\n- the benchmark framework may bring more maturity to the field of multi-satellite scheduling\n- the transformer-based approach appears to be novel\n\nWeaknesses:\n- the description in the paper provides limited understanding as to why certain design choices of the transformer-based method are taken, and what alternative choices are\n- the experiments provide no understanding of which instances work better with which of the baselines, or what the limitations of the transformer-based method are\n\nQUESTIONS:\n1. In the introduction it says \"the operating environment is highly dynamic: new tasks can appear or expire at any moment, satellite positions and attitudes are continuously changing, batteries cycle through charge and discharge, and satellites may even join or leave the constellation. Scheduling algorithms must adapt on the fly without foreknowledge of these changes.\". Which of these changes are under control or at least partially influenced by the scheduling algorithm and which uncertainty is completely external?\n2. \"To bootstrap learning, we curated AEOS-Bench: a large dataset with constellation scheduling annotations.\" Where does the dataset of tasks come from? And where do the scheduling annotations come from? How has been decided which instances to include and which to exclude?\n3. What are the limitations of AEOS-Former? For how many instances does one of the other methods score better? Is there a pattern here, e.g. what properties do those instances have (e.g. considering size)?\n\nLIMITATIONS:\nThe limitations are not really discussed nor analyzed. See question 3 above.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work addresses the challenge of scheduling Agile Earth Observation Satellites (AEOSs) under complex, large-scale, and dynamic conditions. The authors introduce AEOS-Bench, the first large-scale, realistic benchmark suite for satellite constellation scheduling, featuring over 16,000 scenarios and 3,900+ satellite assets with ground truth annotations. They also propose AEOS-Former, a Transformer-based model with a constraint-aware attention mechanism and a dedicated module for modeling satellite limitations. AEOS-Former uses simulation-based iterative learning to adapt to diverse scenarios and outperforms existing baselines in both task completion and energy efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths- \n\nThe paper proposes a new scheduling model and a benchmark with 16k scenarios.  The AEOS-Bench and AEOS-Former models are highly useful for real-time, multi-satellite scheduling in Earth observation applications, including disaster response systems.  \n\nThe solution employs mathematical equations for scenario modeling and multiple constraints to obtain accurate simulation and scheduling. Furthermore, the work introduces a Transformer-based architecture customized with an Internal Constraint Module to predict feasibility and control time, and Cross-attention masking based on learned constraints. The individual ML components (Transformers, iterative learning, attention masking) exist elsewhere. But the cohesive integration of these techniques for the complex, real-world domain of satellite scheduling is interesting. \n\nGood ablation studies. \n\nThe performance improvements are marginal when compared to SOTA. The major highlight is the total energy consumed by the satellite sensors during imaging, which is good, as the onboard energy resources on a satellite are a precious commodity.  \n\nThe writing is good with a clear structure from motivation to results. \n\nThe code is provided in the appendix. \n\nWeaknesses - \n\nSome of the concepts are unclear. For example, what is a scenario? What is an asset? \n\nHow much energy is saved in doing ML-related tasks on the satellites if the proposed method is used? This is not evaluated. \n\nAll loss weights are assigned as ws = wt = wa = 1. Then what is the purpose of these weights in equation (9)? How were these values selected, and what will be the impact on performance if these values are varied? Similarly, how were values for wCR, wPCR, wTAT, etc were selected? \n\nThe paper makes two claims: In section 1, it mentions \"While effective on simplified benchmarks, their performance degrades sharply in realistic scenarios.\", and in Section 2, \"While these methods offer faster runtimes, their performance diminishes with large-scale or dynamic scenarios,\" without backing them up. \n\nThere are no details present on tasks like what the tasks were, how they were introduced into the system - randomly, uniformly, what their deadline was. \n\nMinor issues - \n\nSome types, such as out instead of ours.\n\nQUESTIONS:\nWho were the experts who annotated the dataset?  Were they subject experts? \n\nWhat is a scenario? What is an asset?\n\nLIMITATIONS:\nThe authors did not discuss any limitations or potential negative societal impact of their work. Can this work be used to create more powerful surveillance systems? How about licensing the code, data or using export controls?", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the scheduling challenges faced by Agile Earth observation satellite (AEOS) constellations in large-scale and dynamic scenarios.  \nThe authors propose a dual innovation strategy by integrating a standardized benchmark and a novel scheduling model to alleviate these challenges. \nThe key contributions are as follows: \n1. AEOS-Bench Benchmark: \nThe authors introduce AEOS-Bench, the first large-scale, high-fidelity scheduling benchmark consisting of 3,907 satellite assets and 16,410 dynamic scenarios (ranging from 1–50 satellites and 50–300 tasks). The dataset is generated using a physics-based simulation platform (Basilisk engine), providing realistic orbital dynamics and energy constraints along with labeled scheduling data. \n2. AEOS-Former Scheduling Model: \nA Transformer-based model, AEOS-Former, is proposed with a novel internal constraint module. This module explicitly encodes physical limitations of satellites (e.g., sensor field of view, battery status), and uses a constraint-driven attention mechanism to generate feasible scheduling policies. Further, the model is trained using a hybrid learning framework that combines supervised pre-training with simulation-based self-optimization, leading to significant improvements in task completion rate and energy efficiency.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- High-Fidelity Benchmark Construction: \nThe authors leverage a physics-based simulation engine to model orbital dynamics, energy cycles, and other physical constraints with expert-reviewed realism. The benchmark scenarios are diverse, covering a wide range of orbital parameters and task loads. \n\n- Scalability and Robustness: \nThe proposed model supports scheduling for 1–50 satellites and demonstrates strong robustness in the presence of dynamic task arrivals and fluctuating onboard resources. This reflects good scalability toward real-world AEOS operations. \n\n- Integration of Learning and Constraint Modeling: \nThe work tightly integrates benchmark construction, explicit constraint modeling, and reinforcement learning. The AEOS-Former achieves superior performance across multiple metrics compared to baseline approaches. \n\n**Weaknesses and concerns**\n- High Computational Cost: \nTraining AEOS-Former requires 8 RTX 4090 GPUs and nearly 48 GPU-hours, which raises concerns about computational efficiency. This cost may hinder real-time deployment in resource-constrained onboard environments. \n\n- Limited Validation on Truly Large-Scale Constellations: \nWhile the model supports up to 50 satellites, many real-world constellations involve significantly more satellites (e.g., hundreds or thousands). It would strengthen the work to evaluate or at least discuss scalability and limitations in truly large-scale constellations. \n\n- Execution Interface and Control Fidelity: \nThe current model outputs high-level scheduling commands, which still depend on downstream attitude control systems for actual execution. This raises a question: could the decoupling from low-level control introduce execution inaccuracies or delays?\n\nQUESTIONS:\n**Questions**\n\n- The paper does not elaborate on the benchmark’s use cases in sufficient detail. Could the authors clarify their choice of track, and explain why the benchmark/dataset track was not considered more appropriate for this submission?\n\n**Suggestions**\n\n- Generalization to Unseen Scenarios: While the model performs reasonably well under the val-unseen split setting, its completion rate and partial completion rate on the test split fall below those of some baseline methods. This suggests that the model’s generalization to truly unseen scenarios remains limited. The authors are encouraged to further analyze this performance gap and consider possible enhancements to improve adaptability in novel or out-of-distribution environments.\n- Scalability to Large-Scale Constellations: While the current setup supports up to 50 satellites, many operational constellations (e.g., Starlink, OneWeb) involve hundreds or thousands of satellites. The authors are encouraged to strengthen their evaluation or at least discuss the potential scalability of their approach under such large-scale constellation settings.  \n- Impact of High-Level Scheduling Decoupled from Low-Level Control: The proposed method generating high-level scheduling commands, which are then translated into low-level control actions (e.g., attitude maneuvers) by downstream systems. However, it remains unclear whether this decoupling between high-level scheduling and low-level execution might introduce inaccuracies or latency during real-world operations. A brief discussion on this potential issue—such as how the system ensures feasibility and timing alignment—would strengthen the clarity and completeness of the system design.  \n- Temporal Granularity and Feature Fusion:  \nThe model jointly embeds static orbital parameters and dynamic satellite states (e.g., real-time attitude). However, orbital dynamics typically evolve over hour-level timescales, whereas attitude control changes on the order of seconds. This significant mismatch in temporal granularity might require a hierarchical or decoupled processing scheme. The authors are encouraged to discuss whether such a temporal difference affects decision-making and how it is handled in the current model.\n\nLIMITATIONS:\n- Lightweight Deployment on Resource-Constrained Platforms.  Given that satellites typically operate in resource-constrained environments, it would be valuable for the authors to explore lightweight inference or training strategies suitable for deployment on onboard SoC (COTS) platforms. Discussing techniques such as model pruning, quantization, or knowledge distillation would enhance the practical applicability of the proposed method (in the future work).", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
The authors tackle the problem of large-scale satellite constellation scheduling. They introduce a new benchmark (AEOS-Bench) obtained through high-fidelity simulations. Further, a new architecture is proposed (AEOS-Former) which is a Transformer-based scheduling model featuring a constraint-aware attention mechanism and a dedicated internal constraint module that explicitly models the physical and operational limits of each satellite. This model is compared on the proposed benchmark to various baselines and demonstrates superior performance.

STRENGTHS AND WEAKNESSES:
**Strengths**

* The paper is well written and the methods are explained comprehensively.
* The introduced benchmark is a nice contribution to the community surpassing earlier benchmarks quite significantly by e.g. number of scenes (Table 1).
* The authors provide an extensive ablation study on the individual components of AEOS-Former.


**Weaknesses**

* Experiments are only performed on the introduced new benchmark. It would be helpful to also evaluate the model on some of the other benchmarks presented in Table 1 to get an understanding whether the method is specially tuned for this particular benchmark.
* A bit more context on the different baselines would have made it easier to understand the improvement and how the method compares to earlier methods.

QUESTIONS:
* Are all of the presented baselines neural network based? 
* Were also Optimization-based Methods evaluated on the benchmark?
* How do you make sure that the sequences from the simulation in the data gathering phase are not present in the evaluation phase as it uses the same simulator?

LIMITATIONS:
No Limitations section and not really discussed in the Conclusion.
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a comprehensive framework for scheduling constellations of Agile Earth Observation Satellites (AEOSs), addressing the challenge of efficiently assigning imaging tasks to multiple satellites under dynamic conditions and stringent constraints. The authors introduce AEOS-Bench, a large-scale benchmark suite containing 3,907 satellite assets and 16,410 scenarios, generated through high-fidelity simulations to ensure realistic satellite behavior and operational constraints. Each scenario includes ground truth scheduling annotations. Building on this benchmark, they propose AEOS-Former, a Transformer-based scheduling model with a constraint-aware attention mechanism. This model incorporates an internal constraint module that explicitly models each satellite's physical and operational limits, such as battery state and sensor field of view. AEOS-Former is trained through a simulation-based iterative learning process, adapting to diverse scenarios and improving scheduling robustness.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n1. The paper introduces AEOS-Bench, a large-scale benchmark suite with 16,410 scenarios and 3,907 satellite assets, generated using a high-fidelity simulation platform. This ensures realistic satellite behavior, including orbital dynamics and resource constraints. The inclusion of ground truth scheduling annotations further enhances its utility.\n2. The benchmark is diverse, covering a wide range of satellite and task configurations (1 to 50 satellites and 50 to 300 tasks). It is publicly accessible, allowing researchers to evaluate and compare different scheduling models under realistic conditions.\n3. The use of a Transformer-based architecture allows the model to effectively capture contextual relationships between satellites and tasks. The constraint-driven attention mechanism guides the scheduling process, improving both efficiency and feasibility.\n# Weaknesses\n1. The training process of AEOS-Former requires significant computational resources (approximately 48 GPU-hours).\n2. While the model demonstrates strong performance on the provided benchmark, its scalability to even larger constellations or more complex scenarios is not fully explored. The computational cost may increase prohibitively with larger problem sizes.\n3. The Transformer-based architecture, while powerful, may lack interpretability compared to simpler heuristic or optimization-based methods.\n4. The paper does not provide theoretical guarantees or proofs regarding the optimality or convergence properties of the proposed model.\n\nQUESTIONS:\n1. How to extend the proposed solution to new constellations and tasks?\n2. How to quantify the computational cost?\n3. Any optimality or convergence properties of the proposed model?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-07"}