This work has introduced a comprehensive framework for unsupervised computer vision in aerospace systems, addressing the fundamental challenges that have historically limited autonomous perception capabilities in both orbital and terrestrial aerospace domains. Through innovative methodologies that span from spacecraft jitter estimation to infrastructure monitoring, we have demonstrated that unsupervised and self-supervised approaches can bridge critical gaps in aerospace perception, reducing dependence on exhaustive labeled datasets while maintaining—and in some cases exceeding—the performance of supervised alternatives. This concluding chapter synthesizes the key contributions across domains, articulates remaining challenges, and charts a course for future research directions.