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

Remote Sensing in Ecology and Conservation

Atilim G. Baydin¹  Ben Moseley¹  Tarje Nissen‐Meyer²  Alexandre Szenicer²  Beth Mortimer³  Michael Reinwald³  Alex McDermott‐Roberts³  Zachary Mutinda Muteti⁴  Sandy Oduor⁴ 

[1] Department of Computer Science University of Oxford Oxford UK;Department of Earth Sciences University of Oxford Oxford UK;Department of Zoology University of Oxford Oxford UK;Mpala Research Centre Nanyuki Kenya;

关键词: African elephant;    machine learning;    seismic waves;    wildlife monitoring;   

DOI  :  10.1002/rse2.242

来源: DOAJ

【 摘 要 】

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 detecting elephants up to 100 m away, and over 90% balanced accuracy in recognising running and rumbling behaviours from the seismic data. We release the dataset used in this study: SeisSavanna represents a unique collection of seismic signals with the associated wildlife species and behaviour. Our results suggest that seismic data offer substantial benefits for monitoring wildlife, and we propose to further develop our methods using dense arrays that could result in a seismic shift for wildlife monitoring.

【 授权许可】

Unknown