WildDrone: autonomous drone technology for monitoring wildlife populations
The rapid loss of biodiversity worldwide is unprecedented, with more species facing extinction now than at any other time in human history. Key factors contributing to this decline include habitat destruction, overexploitation, and climate change. There is an urgent need for innovative and effective conservation practices that leverage advanced technologies, such as autonomous drones, to monitor wildlife, manage human-wildlife conflicts, and protect endangered species. While drones have shown promise in conservation efforts, significant technological challenges remain, particularly in developing reliable, cost-effective solutions capable of operating in remote, unstructured, and open-ended environments. This paper explores the technological advancements necessary for deploying autonomous drones in nature conservation and presents the interdisciplinary scientific methodology of the WildDrone doctoral network as a basis for integrating research in drones, computer vision, and machine learning for ecological monitoring. We report preliminary results demonstrating the potential of these technologies to enhance biodiversity conservation efforts. Based on our preliminary findings, we expect that drones and computer vision will develop to further automate time consuming observational tasks in nature conservation, thus allowing human workers to ground conservation actions on evidence based on large and frequent data.
Authors
- Andrea Flack (ORCID: https://orcid.org/0000-0002-9099-2802)
- Benjamin Risse (ORCID: https://orcid.org/0000-0001-5691-4029)
- Dylan Cawthorne (ORCID: https://orcid.org/0000-0002-3068-0890)
- Elżbieta Pastucha (ORCID: https://orcid.org/0000-0001-6965-2574)
- Georg May (ORCID: https://orcid.org/0000-0001-8385-6979)
- Tom Richardson
- Tuia Devis
- Ulrik Pagh Schultz (ORCID: https://orcid.org/0000-0003-4119-2689)
- Kilian Meier (ORCID: https://orcid.org/0000-0002-6044-9040)
- Steve Bullock (ORCID: https://orcid.org/0000-0001-9086-1055)
- Saadia Afridi (ORCID: https://orcid.org/0000-0002-8295-2720)
- Fabio Remondino
- Henrik Skov Midtiby (ORCID: https://orcid.org/0000-0002-3310-5680)
- Anders Lyhne Christensen (ORCID: https://orcid.org/0000-0002-9994-2908)
- Blair R. Costelloe (ORCID: https://orcid.org/0000-0001-5291-788X)
- Guy Maalouf (ORCID: https://orcid.org/0009-0003-9175-5512)
- Clément Berthelot (ORCID: https://orcid.org/0009-0004-7687-625X)
- Constanza A. Molina Catricheo (ORCID: https://orcid.org/0009-0000-0716-7573)
- Elena Iannino (ORCID: https://orcid.org/0009-0009-8474-1861)
- Kasper Hlebowicz
- Kurt Jensen (ORCID: https://orcid.org/0000-0001-9014-2093)
- Vandita Shukla (ORCID: https://orcid.org/0009-0007-7242-2279)
- Majid Mirmehdi
- Matt Watson
- Camille Rondeau Saint-Jean
- Matthijs Damen
- Tilo Burghardt
- Nguyen Ngoc Dat
- William Njoroge
- Edouard G. A. Rolland
- Lucie Laporte-Devylder
- Magnus Wahlberg
- Samuel Mutisya
Institutions
- University of Southern Denmark (DK)
- Maersk (Denmark) (DK)
- University of Konstanz (DE)
- Kessler Foundation (US)
- University of Bristol (GB)
- V. A. Graičiūno Aukštoji Vadybos Mokykla (LT)
- Max Planck Institute of Animal Behavior (DE)
- GeoInformation (United Kingdom) (GB)
- École Polytechnique Fédérale de Lausanne (CH)
- Wageningen University & Research (NL)
Publication Details
- Journal
- Frontiers in Robotics and AI
- Published
- 2026-01-12
- DOI
- https://doi.org/10.3389/frobt.2025.1695319
- Citations
- 1
- Primary Topic
- UAV Applications and Optimization
- Type
- article
- Field-Weighted Citation Impact
- 104.69