Machine Learning for Semi-Automated Meteorite Recovery
Space Science and Technology Center, Curtin University（科廷大学空间科学与技术中心）；Spectre UAV Concepts（Spectre UAV Concepts公司）；Amotus Pty Ltd（Amotus Pty Ltd公司）；Department of Psychology, Clemson University（克莱姆森大学心理学系）；Western Australian Museum（西澳大利亚博物馆）
Seamus Anderson, Martin Towner, Phil Bland, Christopher Haikings, William Volante, Eleanor Sansom, Hadrien Devillepoix, Patrick Shober, Benjamin Hartig, Martin … 展开作者
Seamus Anderson, Martin Towner, Phil Bland, Christopher Haikings, William Volante, Eleanor Sansom, Hadrien Devillepoix, Patrick Shober, Benjamin Hartig, Martin Cupak, Trent Jansen-Sturgeon, Robert Howie, Gretchen Benedix, Geoff Deacon
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.
Recovery of Meteorites Using an Autonomous Drone and Machine Learning
University of California, Davis（加州大学戴维斯分校）；SETI Institute（SETI研究所）；NASA Ames Research Center（NASA艾姆斯研究中心）；Commonwealth Scientific and Industrial Research Organisation（澳大利亚联邦科学与工业研究组织）；Holberton School of Software Engineering（霍尔伯顿软件工程学院）；Cambridge University（剑桥大学）；Institut National de Recherche en Informatique et en Automatique（法国国家信息与自动化研究所）；Curtin University（科廷大学）
A Cloud-Based Tool for Meteorite Recovery Using Drones and Machine Learning
NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；Space Science and Technology Centre（空间科学与技术中心）；International Centre for Radio Astronomy Research（国际射电天文研究中心）；Astronomy Data and Computing Services (ADACS)（天文数据与计算服务）；Curtin Institute for Data Science（Curtin数据科学研究所）；Centre for Rock Art Research and Management（岩画研究与管理中心）；Faculty of Mathematics, Physics and Informatics, Comenius University Bratislava（布拉迪斯拉发大学数学、物理与信息学学院）；Institute of Geology, University of Bern（伯尔尼大学地质研究所）；Aix-Marseille University, CNRS, IRD, INRA, CEREGE, Institut Origines（阿维尼翁大学，CNRS，IRD，INRA，CEREGE，Origines研究所）；Royal Holloway University of London（皇家霍洛威大学）；Planétarium de Montréal, Espace pour la Vie（蒙特利尔天文馆，生命空间）；Department of Physics and Astronomy, The University of Western Ontario（滑铁卢大学物理与天文学系）；School of Earth and Planetary Sciences, Curtin University（Curtin大学地球与行星科学学院）；Australian Nuclear Science and Technology Organisation（澳大利亚核科学与技术组织）；School of Earth, Atmosphere and Environment, Monash University（莫纳什大学地球、大气与环境学院）
Finding Circumbinary Planets: A Semi-Automated Transit Search of TESS Eclipsing Binaries
University of Warwick（华威大学）；Centre for Exoplanets and Habitability, University of Warwick（华威大学系外行星与宜居性中心）；School of Physics and Astronomy, University of Birmingham（伯明翰大学物理与天文学学院）
Deep machine learning for meteor monitoring: advances with transfer learning and gradient-weighted class activation mapping
Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Institut de Ciències de l’Espai (ICE, CSIC)（西班牙国家研究委员会空间科学研究所）；Institut d’Estudis Espacials de Catalunya (IEEC)（加泰罗尼亚空间研究所）；Spanish Meteor Network (SPMN, CSIC)（西班牙流星网络（西班牙国家研究委员会））
