#8235. Machine learning to detect marine animals in UAV imagery: effect of morphology, spacing, behaviour and habitat

October 2026publication date
Proposal available till 08-06-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Ecology, Evolution, Behavior and Systematics;
Ecology;
Nature and Landscape Conservation;
Computers in Earth Sciences;
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Abstract:
Machine learning algorithms are being increasingly used to process large volumes of wildlife imagery data from unmanned aerial vehicles (UAVs); however, suitable algorithms to monitor multiple species are required to enhance efficiency. Here, we developed a machine learning algorithm using a low-cost computer. We trained a convolutional neural network and tested its performance in: (1) distinguishing focal organisms of three marine taxa (Australian fur seals, loggerhead sea turtles and Australasian gannets; body size ranges: 0.8–2.5 m, 0.6–1.0 m, and 0.8–0.9 m, respectively); and (2) simultaneously delineating the fine-scale movement trajectories of multiple sea turtles at a fish cleaning station.
Keywords:
aerial surveys; artificial intelligence; deep learning; demography; movement ecology; satellite imagery

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