A Cloud-Based Tool For Meteorite Recovery Using Drones And Machine Learning

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/ – Drone Meteorite Search Platform — AI-powered detection and collaborative recovery workflows

Seamus L. Anderson, Hadrien A. R. Devillepoix, Lewis Lakerink, Sawitchaya Tippaya, Dale P. Giancono, Martin C. Towner, Iona Clemente, Martin Cupák, Ashley F. Rogers, John H. Fairweather, Mia Walker, Daniel Burgin, Michael A. Frazer, Sophie E. Deam, Veronika Pazderová, Eleanor K. Sansom, Benjamin A. D. Hartig, Hely C. Branco, Thomas Stevenson, Isabella Hatty, Anna Zappatini, Anthony Lagain, Tom Lovelock, Auriane Egal, Lucy Forman, David Belton, Simon Windsor, Shibli Saleheen, Asher Leslie, Gregory B. Poole, Andrew Langendam, Rachel S. Kirby, Andrew G. Tomkins

Comments: 23 pages, 3 figures
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
Cite as: arXiv:2605.19179 [astro-ph.EP](or arXiv:2605.19179v1 [astro-ph.EP] for this version)
https://doi.org/10.48550/arXiv.2605.19179
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Submission history
From: Seamus Anderson
[v1] Mon, 18 May 2026 23:05:00 UTC (597 KB)
https://arxiv.org/abs/2605.19179

Astrobiology,