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.
Cite
@article{arxiv.2605.19179,
title = {A Cloud-Based Tool for Meteorite Recovery Using Drones and Machine Learning},
author = {Seamus L. Anderson and Hadrien A. R. Devillepoix and Lewis Lakerink and Sawitchaya Tippaya and Dale P. Giancono and Martin C. Towner and Iona Clemente and Martin Cupák and Ashley F. Rogers and John H. Fairweather and Mia Walker and Daniel Burgin and Michael A. Frazer and Sophie E. Deam and Veronika Pazderová and Eleanor K. Sansom and Benjamin A. D. Hartig and Hely C. Branco and Thomas Stevenson and Isabella Hatty and Anna Zappatini and Anthony Lagain and Tom Lovelock and Auriane Egal and Lucy Forman and David Belton and Simon Windsor and Shibli Saleheen and Asher Leslie and Gregory B. Poole and Andrew Langendam and Rachel S. Kirby and Andrew G. Tomkins},
journal= {arXiv preprint arXiv:2605.19179},
year = {2026}
}