@rafia_fatima276

AstroCleanAI is an advanced AI-powered space debris tracking and risk prediction system designed to enhance space safety. With the increasing amount of space junk threatening satellites and future missions, managing debris is more critical than ever. AstroCleanAI leverages artificial intelligence to detect, classify, and predict debris behavior, ensuring proactive collision avoidance and efficient satellite operations. 🛰️ Key Features: Real-Time Space Debris Detection & Classification – Uses deep learning models (YOLOv5, ResNet-50) to analyze satellite imagery and radar data, identifying debris based on size, trajectory, and risk level. AI-Powered Collision Risk Prediction – Implements an XGBoost-based risk assessment model to predict satellite-debris collision probabilities and suggest optimal avoidance maneuvers. Interactive Space Debris Simulation – Visualizes real-world debris movement and potential collision risks using 3D orbital mechanics modeling. Public Engagement Dashboard – Allows researchers, students, and enthusiasts to explore real-time space debris tracking and AI-driven insights. 🛠️ Technology Stack: AI & Machine Learning: TensorFlow, PyTorch, YOLOv5, ResNet-50, XGBoost Space Data & Analysis: NASA’s Open TLE Data, OpenCV for satellite imagery processing Web & Visualization: Streamlit, Matplotlib (3D trajectory simulation), Flask backend 🌍 Impact & Future Scope: AstroCleanAI contributes to sustainable space exploration by mitigating debris-related risks, preventing costly satellite damage, and optimizing space traffic management. Future enhancements may include integrating real-world satellite telemetry, collaborating with space agencies for improved predictions, and developing an autonomous debris mitigation system capable of orchestrating deorbiting maneuvers.

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