Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape

Zijing Wu, Ce Zhang, Xiaowei Gu, Isla Duporge, Lacey F. Hughey, Jared A. Stabach, Andrew K. Skidmore, J. Grant C. Hopcraft, Stephen J. Lee, Peter M. Atkinson, Douglas J. McCauley, Richard Lamprey, Shadrack Ngene and Tiejun Wang ()
Additional contact information
Zijing Wu: University of Twente
Ce Zhang: Lancaster University
Xiaowei Gu: University of Kent
Isla Duporge: Princeton University
Lacey F. Hughey: Smithsonian National Zoo and Conservation Biology Institute
Jared A. Stabach: Smithsonian National Zoo and Conservation Biology Institute
Andrew K. Skidmore: University of Twente
J. Grant C. Hopcraft: University of Glasgow
Stephen J. Lee: Army Research Office
Peter M. Atkinson: Lancaster University
Douglas J. McCauley: University of California
Richard Lamprey: University of Twente
Shadrack Ngene: Wildlife Research and Training Institute
Tiejun Wang: University of Twente

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.

Date: 2023
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DOI: 10.1038/s41467-023-38901-y

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