Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale

📰 ArXiv cs.AI

Learn to detect orbital anomalies at scale using multi-tier labeling and physics-informed learning, crucial for collision avoidance and decay forecasting

advanced Published 12 May 2026

Action Steps

Apply multi-tier labeling to orbital data to generate high-quality labels
Use physics-informed learning to model orbital anomalies
Train a machine learning model on the labeled data to detect anomalies
Evaluate the model's performance using metrics such as precision and recall
Deploy the model at scale to detect anomalies in real-time

Who Needs to Know This

Data scientists and machine learning engineers working on satellite analytics and space traffic management can benefit from this approach to improve anomaly detection accuracy and efficiency

Key Insight

💡 Multi-tier labeling and physics-informed learning can improve the accuracy and efficiency of orbital anomaly detection

Key Takeaways

Learn to detect orbital anomalies at scale using multi-tier labeling and physics-informed learning, crucial for collision avoidance and decay forecasting

Full Article

Title: Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale

Abstract:
arXiv:2605.09790v1 Announce Type: cross Abstract: Detecting orbital anomalies, such as maneuvers, atmospheric decay, and attitude upsets, across the rapidly growing population of low-Earth-orbit (LEO) satellites is a prerequisite for collision avoidance, decay forecasting, and conjunction screening. The bottleneck is not modeling capacity but labels: there is no public ground-truth corpus of orbital anomalies, manual review does not scale to approximately 10^4 active satellites, and pure rule-ba

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