Evidence map›Paper›PMID 41640810›Full record

ArticleFrontiers in robotics and AI2025

WildDrone: autonomous drone technology for monitoring wildlife populations.

Ulrik Pagh Schultz Lundquist, Saadia Afridi, Clément Berthelot, Nguyen Ngoc Dat, Kasper Hlebowicz, Elena Iannino, Lucie Laporte-Devylder, Guy Maalouf, Giacomo May, Kilian Meier and 23 more

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

33 authors.

Ulrik Pagh Schultz LundquistSDU UAS, Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark.
Saadia AfridiSDU UAS, Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark.
Clément BerthelotDepartment of Migration, Max Planck Institute of Animal Behavior, Konstanz, Germany.
Nguyen Ngoc DatSchool of Computer Science, University of Bristol, Bristol, United Kingdom.
Kasper HlebowiczDepartment of Environmental Sciences, Wageningen University and Research, Wageningen, Netherlands.
Elena IanninoDepartment of Biology, University of Konstanz, Konstanz, Germany.
Lucie Laporte-DevylderDepartment of Biology, University of Southern Denmark, Odense, Denmark.
Guy MaaloufSDU UAS, Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark.
Giacomo MayEnvironmental Computational Science and Earth Observation Laboratory, Environmental Engineering Institute, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Switzerland.
Kilian MeierSchool of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, United Kingdom.
Constanza A Molina CatricheoInstitute for Geoinformatics and Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany.
Edouard G A RollandSDU UAS, Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark.
Camille Rondeau Saint-JeanDepartment of Biology, University of Southern Denmark, Odense, Denmark.
Vandita Shukla3D Optical Metrology (3DOM), Bruno Kessler Foundation (FBK), Trento, Italy.
Tilo BurghardtSchool of Computer Science, University of Bristol, Bristol, United Kingdom.
Anders Lyhne ChristensenSDU UAS, Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark.
Blair R CostelloeDepartment of Biology, University of Konstanz, Konstanz, Germany.
Matthijs DamenAvy, Amsterdam, Netherlands.
Andrea FlackDepartment of Migration, Max Planck Institute of Animal Behavior, Konstanz, Germany.
Kjeld JensenSDU UAS, Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark.
Henrik Skov MidtibySDU UAS, Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark.
Majid MirmehdiSchool of Computer Science, University of Bristol, Bristol, United Kingdom.
Fabio Remondino3D Optical Metrology (3DOM), Bruno Kessler Foundation (FBK), Trento, Italy.
Tom RichardsonSchool of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, United Kingdom.
Benjamin RisseInstitute for Geoinformatics and Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany.
Devis TuiaEnvironmental Computational Science and Earth Observation Laboratory, Environmental Engineering Institute, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Switzerland.
Magnus WahlbergDepartment of Biology, University of Southern Denmark, Odense, Denmark.
Dylan CawthorneSDU UAS, Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark.
Steve BullockSchool of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, United Kingdom.
William NjorogeOl Pejeta Conservancy, Nanyuki, Kenya.
Samuel MutisyaOl Pejeta Conservancy, Nanyuki, Kenya.
Matt WatsonSchool of Earth Sciences, University of Bristol, Bristol, United Kingdom.
Elzbieta PastuchaSDU UAS, Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Odense, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

autonomous dronesbiodiversity conservationcomputer visionconservation ecologywildlife monitoring

Identifiers

PMID41640810
PMCPMC12865209

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.