Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning

TL;DR

This study demonstrates a novel method combining drones and machine learning to efficiently locate and recover meteorites after observed falls, significantly improving search accuracy and speed.

Contribution

The paper introduces an integrated drone and machine learning approach for meteorite recovery, marking the first successful use of this technology in the field.

Findings

Meteorite recovered within 50 m of predicted fall line

Machine learning effectively identified meteorite candidates

Drone surveys covered 5.1 km² over 4 days

Abstract

We report the first-time recovery of a fresh meteorite fall using a drone and a machine learning algorithm. A fireball on the 1st April 2021 was observed over Western Australia by the Desert Fireball Network, for which a fall area was calculated for the predicted surviving mass. A search team arrived on site and surveyed 5.1 km2 area over a 4-day period. A convolutional neural network, trained on previously-recovered meteorites with fusion crusts, processed the images on our field computer after each flight. meteorite candidates identified by the algorithm were sorted by team members using two user interfaces to eliminate false positives. Surviving candidates were revisited with a smaller drone, and imaged in higher resolution, before being eliminated or finally being visited in-person. The 70 g meteorite was recovered within 50 m of the calculated fall line using, demonstrating the effectiveness of this methodology which will facilitate the efficient collection of many more observed meteorite falls.