Anomaly Detection Methods for Finding Technosignatures

Rohan Loveland, Ryan Sime

2024

Abstract

Machine learning based anomaly detection methods are used to find technosignatures, in this case human activity on the Moon, in high resolution imagery for four anomaly detection methods: autoencoder based reconstruction loss, kernel density estimate of probability density, isolation forests, and the Farpoint algorithm. A deep learning variational autoencoder was used which provided both a reconstruction capability as well as a means of dimensionality reduction. The resulting lower dimension latent space data was used for the probability density and isolation forest methods. For our data, we use Lunar Reconnaissance Orbiter high resolution imagery on four known mission locations, with large areas broken into smaller tiles. We rank the tiles by anomalousness and determine the gains in efficiency that would result from showing the tiles in that order as compared to using random selection. The resulting efficiency in reduction of necessary amount of analyst time ranges into factors in the hundreds depending on the particular mission, with the Farpoint algorithm generally having the best performance. We also combine the tiles into bounding boxes based on spatial proximity, and demonstrate that this could provide a further improvement in reduction efficiency.

Paper Citation

in Harvard Style

Loveland R. and Sime R. (2024). Anomaly Detection Methods for Finding Technosignatures. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 633-640. DOI: 10.5220/0012306400003654

in Bibtex Style

@conference{icpram24,
author={Rohan Loveland and Ryan Sime},
title={Anomaly Detection Methods for Finding Technosignatures},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={633-640},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012306400003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Anomaly Detection Methods for Finding Technosignatures
SN - 978-989-758-684-2
AU - Loveland R.
AU - Sime R.
PY - 2024
SP - 633
EP - 640
DO - 10.5220/0012306400003654
PB - SciTePress