The night sky, a canvas of stars and satellites, is increasingly cluttered with orbital debris. The European Space Agency reports over 28,000 tracked objects hurtling through space at speeds exceeding 28,000 kilometres per hour. This debris, including defunct satellites and spent rocket stages, poses a significant threat to operational satellites worth hundreds of millions of dollars.

As the volume of debris continues to grow, the traditional approach of human analysts tracking and predicting trajectories is becoming untenable. Machine learning has emerged as a potential solution, offering advanced algorithms that can process vast amounts of data and predict collision risks more efficiently. However, while machine learning has shown promise in classifying debris and predicting trajectories, it faces challenges such as data limitations, interpretability, and robustness against adversarial attacks.

The integration of machine learning with traditional methods, as seen in ESA's CREAM project, represents a hybrid approach that leverages the strengths of both, potentially offering a more effective solution to the growing space debris problem. As the space industry expands, with companies like SpaceX deploying mega-constellations, the role of machine learning in ensuring orbital safety will become increasingly critical.