论文标题
半自动陨石恢复的机器学习
Machine Learning for Semi-Automated Meteorite Recovery
论文作者
论文摘要
我们提出了一种新的方法，用于恢复使用无人机和机器学习算法观察到的陨石瀑布并受火球网络的约束。这种方法使用当地地形的图像用于给定秋季地点，以训练旨在检测陨石候选物的人工神经网络。我们已经测试了我们的方法，以显示75-97％之间的陨石检测率，同时还提供了消除假阳性的有效机制。我们在西澳大利亚州许多地点进行的测试还展示了该培训计划的能力，可以推广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地识别使用传统搜索技术的本机秋季遗址中的3个陨石。我们的方法将用于恢复陨石在地球范围内的火球网络中广泛的位置。
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.
