Efficient Identification and Analysis of Space debris Trails in Time-Domain Astronomy

This paper presents an automated end-to-end pipeline to detect and characterize artificial satellites and space debris streaks. As the exponential rise of orbital debris poses an escalating risk to global space assets, providing scalable, real-time tools is a critical contribution to the global Space Situational Awareness (SSA) network. The architecture utilizes a multi-stage geometric approach for raw image segmentation and high-precision morphological characterization. Experimental results demonstrate robust performance in real-world survey environments, successfully recovering streak profiles with an operational detection limit for features with integrated $SNR \ge 86$ and lengths $\ge 98$ arcseconds. This unsupervised framework strengthens global space sustainability efforts by monitoring RSO populations.