Lunar Technosignatures: A Deep Learning Approach to Detecting Apollo Landing Sites on the Lunar Surface

Tom Sander, Christian Wöhler

2025

Abstract

Uncovering anomalies on the lunar surface is crucial for understanding the Moon’s geological and astronomical history. By identifying and studying these anomalies, new theories about the changes that have occurred on the Moon can be developed or refined. This study seeks to enhance anomaly detection on the Moon and replace the time-consuming manual data search process by testing an anomaly detection method using the Apollo landing sites. The landing sites are advantageous as they are both anomalous and can be located, enabling an assessment of the procedure. Our study compares the performance of various state-of-the-art machine learning algorithms in detecting anomalies in the Narrow-Angle Camera data from the Lunar Reconnaissance Orbiter spacecraft. The results demonstrate that our approach outperforms previous publications in accurately predicting landing site artifacts and technosignatures at the Apollo 15 and 17 landing sites. While our method achieves promising results, there is still room for improvement. Future refinements could focus on detecting more subtle anomalies, such as the rover tracks left by the Apollo missions.

Paper Citation

in Harvard Style

Sander T. and Wöhler C. (2025). Lunar Technosignatures: A Deep Learning Approach to Detecting Apollo Landing Sites on the Lunar Surface. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 491-499. DOI: 10.5220/0013179000003912

in Bibtex Style

@conference{visapp25,
author={Tom Sander and Christian Wöhler},
title={Lunar Technosignatures: A Deep Learning Approach to Detecting Apollo Landing Sites on the Lunar Surface},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={491-499},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013179000003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Lunar Technosignatures: A Deep Learning Approach to Detecting Apollo Landing Sites on the Lunar Surface
SN - 978-989-758-728-3
AU - Sander T.
AU - Wöhler C.
PY - 2025
SP - 491
EP - 499
DO - 10.5220/0013179000003912
PB - SciTePress