A Cloud-Based Tool for Meteorite Recovery Using Drones and Machine Learning
Authors: Seamus L. Anderson, Hadrien A. R. Devillepoix, Lewis Lakerink, Sawitchaya Tippaya, Dale P. Giancono, Martin C. Towner, Iona Clemente, Martin Cupák, +25 more
Organizations: NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States. · Space Science and Technology Centre, Curtin University, GPO Box U1987, Perth WA 6102, Australia. · International Centre for Radio Astronomy Research, Curtin University, GPO Box U1987, Perth WA 6102, Australia. · Astronomy Data and Computing Services (ADACS), The Centre for Astrophysics & Supercomputing, Swinburne University of Technology, P.O. Box 218, Hawthorn, VIC 3122, Australia. · Curtin Institute for Data Science, Curtin University, GPO Box U1987, Perth WA 6102, Australia. · Centre for Rock Art Research and Management, University of Western Australia, Perth WA 6009, Australia. · Faculty of Mathematics, Physics and Informatics, Comenius University Bratislava, Mlynská dolina, 84248, Bratislava, Slovakia. · Institute of Geology, University of Bern, Bern, Switzerland. · Aix-Marseille University, CNRS, IRD, INRA, CEREGE, Institut Origines, Aix en Provence, France. · Royal Holloway University of London, Surrey, TW20 0EX UK. · Planétarium de Montréal, Espace pour la Vie, 4801 av. Pierre-de Coubertin, Montréal, H1V 3V4, Québec, Canada. · Department of Physics and Astronomy, The University of Western Ontario, London, Ontario, Canada. · School of Earth and Planetary Sciences, Curtin University, GPO Box U1987, Perth WA 6102, Australia. · Australian Nuclear Science and Technology Organisation, Clayton, Victoria 3168, Australia. · School of Earth, Atmosphere and Environment, Monash University, Melbourne, Victoria 3800, Australia.
We present a cloud-based tool that uses drones and machine learning to help recover instrumentally observed meteorite falls. We showcase a collection of improvements made upon previous iterations of our system, as well as detail the successes and limitations of this technique when applied to observed meteorite falls in South and Western Australia. This tool is available to the meteoritics research community upon request at https://find.gfo.rocks.
Nowadays, more and more disasters of different natures are appearing. Several disaster assessment approaches have been developed in order to identify damaged areas from aerial images. These damaged areas contain rich material that could be recycled towards several ecological purposes. In this paper, we present a lightweight approach that permits the efficient detection of recyclable material. Experimental results show the potential of the proposed approach towards localizing recyclable materials. Accordingly, we provide a rare dataset of material images that we labeled towards supporting the development of recyclable material detectors. The dataset of labeled material images is publicly available at: anonymous.
Machine learning models for climate and Earth science are becoming increasingly capable, yet model deployment into operational use remains a largely unaddressed challenge: general-purpose model-serving tools, such as MLflow and KServe, assume input data availability at the inference node, while data acquisition, failure handling, and preprocessing are trusted to a separate workflow. We present OpFML: Operational Forecasting with Machine Learning - a configurable pipeline integrating the four steps of operational inference into a single TOML-configured workflow: data consumption, contingency handling, preprocessing, and model inference. By consolidating these steps, OpFML removes the significant boilerplate code required for each new deployment. We demonstrate the pipeline on the operational forecasting of daily fire activity over southern Italy.
Animal ecology data collection using drones represents a substantial investment of time, expertise, and financial resources. Yet most existing datasets serve only a single research community, limiting interdisciplinary reuse. We propose a unified drone dataset standard, FAIR^2 Drones, that bridges ecology, robotics, and computer vision by building on existing FAIR and AI-ready data frameworks while adding essential platform metadata and annotation specifications. Our standard enables datasets to simultaneously support ecological analysis, robotics algorithm development, and computer vision benchmarking. We provide open-source validation tools, reference implementations, and multimodal extensions linking drone imagery with complementary sensors such as camera traps, GPS, and acoustics. By standardizing metadata across disciplines, this framework maximizes the scientific return on investment for costly field deployments and accelerates cross-domain collaboration in environmental monitoring.