FAIR^2 Drones: An AI-Ready Standard for Cross-Domain Wildlife Drone Datasets
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Kline, Jenna Meier, Kilian Shukla, Vandita Rolland, Edouard G. A. Iannino, Elena Laporte-Devylder, Lucie Catricheo, Constanza Andrea Molina Costelloe, Blair Campolongo, Elizabeth Midtiby, Henrik S. Tuia, Devis Risse, Benjamin Lundquist, Ulrik P. S. Christensen, Anders Lyhne Remondino, Fabio Richardson, Thomas Berger-Wolf, Tanya |
| author_facet | Kline, Jenna Meier, Kilian Shukla, Vandita Rolland, Edouard G. A. Iannino, Elena Laporte-Devylder, Lucie Catricheo, Constanza Andrea Molina Costelloe, Blair Campolongo, Elizabeth Midtiby, Henrik S. Tuia, Devis Risse, Benjamin Lundquist, Ulrik P. S. Christensen, Anders Lyhne Remondino, Fabio Richardson, Thomas Berger-Wolf, Tanya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00355 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FAIR^2 Drones: An AI-Ready Standard for Cross-Domain Wildlife Drone Datasets Kline, Jenna Meier, Kilian Shukla, Vandita Rolland, Edouard G. A. Iannino, Elena Laporte-Devylder, Lucie Catricheo, Constanza Andrea Molina Costelloe, Blair Campolongo, Elizabeth Midtiby, Henrik S. Tuia, Devis Risse, Benjamin Lundquist, Ulrik P. S. Christensen, Anders Lyhne Remondino, Fabio Richardson, Thomas Berger-Wolf, Tanya Robotics 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. |
| title | FAIR^2 Drones: An AI-Ready Standard for Cross-Domain Wildlife Drone Datasets |
| topic | Robotics |
| url | https://arxiv.org/abs/2606.00355 |