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Seismic savanna: machine learning for classifying wildlife and behaviours using ground-based vibration field recordings

Abstract:

We develop a machine learning approach to detect and discriminate elephants from other species, and to recognise important behaviours such as running and rumbling, based only on seismic data generated by the animals. We demonstrate our approach using data acquired in the Kenyan savanna, consisting of 8000 h seismic recordings and 250 k camera trap pictures. Our classifiers, different convolutional neural networks trained on seismograms and spectrograms, achieved 80%–90% balanced accuracy in d...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1002/rse2.242

Authors


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John Fell Fund More from this funder
Publisher:
Wiley Publisher's website
Journal:
Remote Sensing in Ecology and Conservation Journal website
Volume:
8
Issue:
2
Pages:
236-250
Publication date:
2021-11-09
Acceptance date:
2021-09-13
DOI:
ISSN:
2056-3485
Language:
English
Keywords:
Pubs id:
1207667
Local pid:
pubs:1207667
Deposit date:
2021-11-09

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