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.

中文翻译：

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用于半自动陨石回收的机器学习

我们提出了一种使用无人机和机器学习算法来恢复由火球网络观察和约束的陨石坠落的新方法。这种方法使用给定坠落地点的局部地形图像来训练人工神经网络，旨在检测陨石候选者。我们对我们的方法进行了现场测试，结果表明陨石检测率在 75-97% 之间，同时还提供了一种有效的机制来消除误报。我们在西澳大利亚州的多个地点进行的测试也展示了该训练方案能够泛化模型以学习局部地形特征。我们的模型训练方法还能够正确识别 3 颗陨石在其原生陨石坑中的位置，这些陨石是使用传统搜索技术发现的。