Detecting animals in African Savanna with UAVs and the crowds
Open accessAbstract
Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks which are routinely performed through manual counting in large collections of images. In this paper, we propose a semi-automatic system able to detect large mammals in semi-arid Savanna. It relies on an animal-detection system based on machine learning, trained with crowd-sourced annotations provided by volunteers who manually interpreted sub-decimeter resolution color images. The system achieves a high recall rate and a human operator can then eliminate false detections with limited effort. Our system provides good perspectives for the development of data-driven management practices in wildlife conservation. It shows that the detection of large mammals in semi-arid Savanna can be approached by processing data provided by standard RGB cameras mounted on affordable fixed wings UAVs.
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Large mammal detection in the Kuzikus reserve in 2014 was improved from a recall rate of 20% to 80% for a common 75% precision rate.
“large mammal detection in the Kuzikus reserve in 2014 was improved significantly by improving the detection methodologies, from a recall rate of 20%35 to 80%37 (for a common 75% precision rate).”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [35]
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Perspectives in machine learning for wildlife conservation
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- Authors
- Rey, N., Volpi, M., Joost, S., & Tuia, D.
- Affiliations
- University of Zurich (CH)
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Tuia, Devis
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 200
- Pages
- 341-351
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2752508182
- Cited by
- 150
- References
- 28
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 18 · 2023: 21 · 2022: 17 · 2021: 29 · 2020: 18 · 2019: 21 · 2018: 4 · 2017: 1
- FWCI
- 9.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 333 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Remote Sensing of Environment (acceptedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- arXiv (Cornell University)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (PZ00P2-136827, http://p3.snf.ch/project-136827)
- Consortium for Ocean Leadership
- Wildlife Reserves Singapore Conservation Fund
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- animal detection, large mammals, African savanna, unmanned aerial vehicles, crowdsourced annotation, wildlife monitoring, semi-automatic detection, fixed-wing UAV, RGB images, wildlife conservation, semi-arid savanna
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