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Cited by 335Advanced Neural Network ApplicationsWildlife Ecology and ConservationSmart Agriculture and AI

The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.

Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning

Open access
Kellenberger, Benjamin, Marcos, Diego, Tuia, Devis·2018·Remote Sensing of Environment
DOI 10.1016/j.rse.2018.06.028
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Abstract

Knowledge over the number of animals in large wildlife reserves is a vital necessity for park rangers in their efforts to protect endangered species. Manual animal censuses are dangerous and expensive, hence Unmanned Aerial Vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative tool to estimate livestock. Several works have been proposed that semi-automatically process UAV images to detect animals, of which some employ Convolutional Neural Networks (CNNs), a recent family of deep learning algorithms that proved very effective in object detection in large datasets from computer vision. However, the majority of works related to wildlife focuses only on small datasets (typically subsets of UAV campaigns), which might be detrimental when presented with the sheer scale of real study areas for large mammal census. Methods may yield thousands of false alarms in such cases. In this paper, we study how to scale CNNs to large wildlife census tasks and present a number of recommendations to train a CNN on a large UAV dataset. We further introduce novel evaluation protocols that are tailored to censuses and model suitability for subsequent human verification of detections. Using our recommendations, we are able to train a CNN reducing the number of false positives by an order of magnitude compared to previous state-of-the-art. Setting the requirements at 90% recall, our CNN allows to reduce the amount of data required for manual verification by three times, thus making it possible for rangers to screen all the data acquired efficiently and to detect almost all animals in the reserve automatically.

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  1. Castelluccio, 2015, 1508, 1

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Metadata

Authors
Kellenberger, B., Marcos, D., & Tuia, D.
Affiliations
Wageningen University & Research (NL)
Corresponding author
Kellenberger, Benjamin
ORCID
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2018-10
Volume
216
Pages
139-153
ISSN
0034-4257 (ISSN-L)
DOI
10.1016/j.rse.2018.06.028
OpenAlex
W2810030371
Cited by
335
References
49
Citations by year
2026: 39 · 2025: 35 · 2024: 44 · 2023: 53 · 2022: 40 · 2021: 53 · 2020: 37 · 2019: 34
FWCI
15.51
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing of Environment (publishedVersion)
  • Socio-Environmental Systems Modeling (acceptedVersion, cc-by-nc-nd)
  • arXiv (Cornell University) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Funders
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (PZ00P2-136827)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Keywords
mammal detection, unmanned aerial vehicles, wildlife census, class imbalance, convolutional neural networks, object detection, false positive reduction, evaluation protocol, human verification, large-scale image databases, wildlife reserves, livestock monitoring

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