A Cloud-Based Tool for Meteorite Recovery Using Drones and Machine Learning
Seamus L. Anderson, Hadrien A. R. Devillepoix, Lewis Lakerink, Sawitchaya Tippaya, Dale P. Giancono, Martin C. Towner, Iona Clemente, Martin Cup\'ak, Ashley F. Rogers, John H. Fairweather, Mia Walker, Daniel Burgin, Michael A. Frazer, Sophie E. Deam, Veronika Pazderov\'a
TL;DR
This paper introduces a cloud-based system utilizing drones and machine learning to assist in meteorite recovery, demonstrating improvements and real-world applications in Australia.
Contribution
The paper presents a novel integrated drone and machine learning platform for meteorite recovery, with enhancements over previous versions and practical deployment insights.
Findings
System successfully applied in South and Western Australia
Improvements increased detection accuracy and efficiency
Identified limitations for future research
Abstract
We present a cloud-based tool that uses drones and machine learning to help recover instrumentally observed meteorite falls. We showcase a collection of improvements made upon previous iterations of our system, as well as detail the successes and limitations of this technique when applied to observed meteorite falls in South and Western Australia. This tool is available to the meteoritics research community upon request at https://find.gfo.rocks.
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