FAIR^2 Drones: An AI-Ready Standard for Cross-Domain Wildlife Drone Datasets
Authors: Jenna Kline, Kilian Meier, Vandita Shukla, Edouard G. A. Rolland, Elena Iannino, Lucie Laporte-Devylder, Constanza Andrea Molina Catricheo, Blair Costelloe, +9 more
Organizations: The Ohio State University, Department of Computer Science and Engineering, Columbus, OH, USA · School of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, United Kingdom · 33D Optical Metrology (3DOM), Fondazione Bruno Kessler (FBK), Trento, Italy · 4Computer Vision and Machine Learning Systems Group, Institute for Geoinformatics, University of Muenster,2026 Muenster, Germany · 5Unmanned Aerial Systems Center, University of Southern Denmark, Odense, Denmark · Department of Collective Behavior, Max Planck Institute of Animal Behavior, Konstanz, Germany · Department of Biology, University of Konstanz, Konstanz, GermanyMay · Department of Biology, University of Southern Denmark, Odense, Denmark · 1The Ohio State University, Department of Computer Science and Engineering, Columbus, OH, USA · 9Environmental Computational Science and Earth Observation laboratory, Ecole Polytechnique F´ed´erale de Lausanne
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.