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

Abstract

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 ≥ 86 and lengths ≥ 98 arcseconds. This unsupervised framework strengthens global space sustainability efforts by monitoring RSO populations.

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BibTeX RIS

BibTeX

@misc{yadav2026efficient,
  title = {{Efficient Identification and Analysis of Space debris Trails in Time-Domain Astronomy}},
  author = {Sarvesh Kumar Yadav and Bhavya Ailawadhi and Talat Akhunov and Naveen Dukiya and Sara Filali and Paul Hickson and Priyanshi Kumari and Gokul Singh Mehra and Kuntal Misra and Vibhore Negi and Kumar Pranshu and Jean Surdej and Monalisa Dubey},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.04610},
  eprint = {2611.04610},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.04610},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Efficient Identification and Analysis of Space debris Trails in Time-Domain Astronomy
AU  - Sarvesh Kumar Yadav
AU  - Bhavya Ailawadhi
AU  - Talat Akhunov
AU  - Naveen Dukiya
AU  - Sara Filali
AU  - Paul Hickson
AU  - Priyanshi Kumari
AU  - Gokul Singh Mehra
AU  - Kuntal Misra
AU  - Vibhore Negi
AU  - Kumar Pranshu
AU  - Jean Surdej
AU  - Monalisa Dubey
PY  - 2026
DA  - 2026/11/03
PB  - arXiv
DO  - 10.48550/arxiv.2611.04610
UR  - https://arxiv.org/abs/2611.04610
AB  - 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 ≥ 86 and lengths ≥ 98 arcseconds. This unsupervised framework strengthens global space sustainability efforts by monitoring RSO populations.
ER  -

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