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.
Keywords: autonomous drones; biodiversity conservation; computer vision; conservation ecology; wildlife monitoring.
Copyright © 2026 Lundquist, Afridi, Berthelot, Ngoc Dat, Hlebowicz, Iannino, Laporte-Devylder, Maalouf, May, Meier, Molina Catricheo, Rolland, Rondeau Saint-Jean, Shukla, Burghardt, Christensen, Costelloe, Damen, Flack, Jensen, Midtiby, Mirmehdi, Remondino, Richardson, Risse, Tuia, Wahlberg, Cawthorne, Bullock, Njoroge, Mutisya, Watson and Pastucha.