Aegis Earth: A Multi-Modal Machine Learning Framework for Real-Time Asteroid Impact Prediction and Risk Mitigation

Abstract

Aegis Earth is an advanced decision support and simulation platform that aims to transform planetary defense using Multimodal Machine Learning. While tools like NASA\'s Sentry track orbits with precision, they lack the ability to rapidly model terrestrial consequences. Our Triple Modal Fusion Architecture brings together NASA Near-Earth Object data, USGS topography, and spacecraft kinetic impactor telemetry to simulate crater size, seismic effects, and tsunami risks in under 100ms — achieving over 95% accuracy compared to computationally intensive hydrocode models. The system uses Focal Loss to overcome the accuracy trap of rare-but-dangerous NEO events, and a Dynamic Deflection Sandbox to optimize planetary defense missions in real time.

Introduction

The text describes Aegis Earth, an AI-powered system for real-time prediction and mitigation of Near-Earth Object (NEO) impacts, addressing limitations of current approaches. Traditional methods like NASA’s Sentry-II and JPL Scout provide orbital predictions but not impact consequences, while physics-based hydrocodes are too slow for urgent threats. Machine learning approaches often fail due to imbalanced datasets and siloed data.

Key features of Aegis Earth:

• Three-tier modular architecture:
  1. Presentation Tier: Real-time 3D visualization and command dashboard.
  2. Application Tier: Orchestrates data, feature engineering, and mitigation logic.
  3. Intelligence Tier: Integrates live NASA NEO, USGS terrain, and spacecraft telemetry data, supporting AI models.
• Triple-modal data fusion: Combines asteroid orbital parameters, terrestrial site data, and spacecraft performance metrics for a complete threat profile.
• Machine Learning Core: Uses a Voting Regressor ensemble (Random Forest + XGBoost) trained with Focal Loss to predict rare catastrophic events with >95% accuracy in under 100 milliseconds.
• Impact Simulation & Mitigation: Translates predictions into actionable strategies, optimizing spacecraft deflection parameters.
• Data Management: Stores raw data, simulation results, and model metadata to ensure reproducibility, version control, and rapid response in emergencies.

Summary: Aegis Earth bridges the gap between high-fidelity physics modeling and real-time operational needs by fusing multi-source data, leveraging ensemble machine learning for rare catastrophic event prediction, and providing interactive, explainable visualization and mitigation recommendations.

Conclusion

Aegis Earth demonstrates that surrogate machine learning models can replace computationally intensive physics simulations for planetary defense decision support — without sacrificing meaningful accuracy. By fusing multi-modal data streams and training with Focal Loss, the system delivers site-specific impact predictions at over 95% accuracy (R²) with end-to-end latency under 100ms. The Mitigation Strategy Engine proves equally strong, achieving a 98.1% success rate in recommending effective spacecraft configurations. Looking ahead, the research roadmap for Aegis Earth focuses on making the system more robust, autonomous, and battle-hardened for real deployment. Key goals include integrating uncertainty-aware probabilistic modeling to better quantify prediction confidence, developing autonomous intercept mission planning that can adapt on the fly, and applying reinforcement learning to coordinate multi-spacecraft mitigation campaigns. Hardware acceleration and secure execution environments will also be explored to further close the latency gap for the most time-critical scenarios.

References

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Copyright

Copyright © 2026 Dr. D. Anusha, V.V.K.M. Poojitha, V.M.S. Mahesh, B. N. Yoshita, U. Shanmukha Varma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.