Machine learning for semi‐automated meteorite recovery

1Seamus Anderson et al. (>10)
Meteoritics & Planetary Science (in Press) Link to Article [https://doi.org/10.1111/maps.13593]
Space Science and Technology Center, Curtin University, GPO Box U1987, Perth, Western Australia, 6845 Australia
Published by arrangement with John Wiley & Sons

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% and 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 three 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.

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