# Machine Learning for Semi-Automated Meteorite Recovery

arXiv: https://arxiv.org/abs/2009.13852 · HTML: https://mlsanity.com/paper/2009.13852/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Recovery of Meteorites Using an Autonomous Drone and Machine Learning](https://mlsanity.com/paper/2106.06523/) (2021)
- [Successful Recovery of an Observed Meteorite Fall Using Drones and Machine Learning](https://mlsanity.com/paper/2203.01466/) (2022)

## Abstract (arXiv)

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.