Machine Learning for Semi-Automated Meteorite Recovery
This provides a semi-automated solution for meteorite recovery in fireball networks, though it appears incremental as it builds on existing drone and neural network techniques.
The paper tackles the problem of meteorite recovery by using drones and machine learning to detect meteorites from terrain images, achieving a detection rate of 75-97% and successfully identifying 3 meteorites in field tests.
We present a novel methodology for recovering meteorite falls observed and constrained by fireball networks, using drones and machine learning algorithms. This approach uses images of the local terrain for a given fall site to train an artificial neural network, designed to detect meteorite candidates. We have field tested our methodology to show a meteorite detection rate between 75-97%, while also providing an efficient mechanism to eliminate false-positives. Our tests at a number of locations within Western Australia also showcase the ability for this training scheme to generalize a model to learn localized terrain features. Our model-training approach was also able to correctly identify 3 meteorites in their native fall sites, that were found using traditional searching techniques. Our methodology will be used to recover meteorite falls in a wide range of locations within globe-spanning fireball networks.