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Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

View ORCID ProfileIsla Duporge, View ORCID ProfileOlga Isupova, View ORCID ProfileSteven Reece, View ORCID ProfileDavid W. Macdonald, View ORCID ProfileTiejun Wang
doi: https://doi.org/10.1101/2020.09.09.289231
Isla Duporge
1Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Recanati-Kaplan Centre, Tubney, UK
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  • For correspondence: isla.duporge@zoo.ox.ac.uk
Olga Isupova
2Department of Computer Science, University of Bath, Bath, UK
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Steven Reece
3Department of Engineering Science, University of Oxford, Oxford, UK
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David W. Macdonald
1Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Recanati-Kaplan Centre, Tubney, UK
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Tiejun Wang
4Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
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Posted September 10, 2020.
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Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes
Isla Duporge, Olga Isupova, Steven Reece, David W. Macdonald, Tiejun Wang
bioRxiv 2020.09.09.289231; doi: https://doi.org/10.1101/2020.09.09.289231
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Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes
Isla Duporge, Olga Isupova, Steven Reece, David W. Macdonald, Tiejun Wang
bioRxiv 2020.09.09.289231; doi: https://doi.org/10.1101/2020.09.09.289231

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