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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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Abstract

  1. Satellites allow large-scale surveys to be conducted in short time periods with repeat surveys possible <24hrs. Very high-resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very high-resolution satellite imagery and deep learning.

  2. In this study we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView-3 and 4 satellite data – the highest resolution satellite imagery commercially available. We train and test the model on eleven images from 2014-2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye-1) captured in Kenya to test if the algorithm can generalise to an elephant population outside of the training area.

  3. Our results show the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalise to detect elephants in a different geographical location and from a lower resolution satellite.

  4. Our study demonstrates the feasibility of applying state-of-the-art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customised training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with as high accuracy as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalisability of the technique.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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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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