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Gray whale detection in satellite imagery using deep learning

Publication ,  Journal Article
Green, KM; Virdee, MK; Cubaynes, HC; Aviles-Rivero, AI; Fretwell, PT; Gray, PC; Johnston, DW; Schönlieb, CB; Torres, LG; Jackson, JA
Published in: Remote Sensing in Ecology and Conservation
December 1, 2023

The combination of very high resolution (VHR) satellite remote sensing imagery and deep learning via convolutional neural networks provides opportunities to improve global whale population surveys through increasing efficiency and spatial coverage. Many whale species are recovering from commercial whaling and face multiple anthropogenic threats. Regular, accurate population surveys are therefore of high importance for conservation efforts. In this study, a state-of-the-art object detection model (YOLOv5) was trained to detect gray whales (Eschrichtius robustus) in VHR satellite images, using training data derived from satellite images spanning different sea states in a key breeding habitat, as well as aerial imagery collected by unoccupied aircraft systems. Varying combinations of aerial and satellite imagery were incorporated into the training set. Mean average precision, whale precision, and recall ranged from 0.823 to 0.922, 0.800 to 0.939, and 0.843 to 0.889, respectively, across eight experiments. The results imply that including aerial imagery in the training data did not substantially impact model performance, and therefore, expansion of representative satellite datasets should be prioritized. The accuracy of the results on real-world data, along with short training times, indicates the potential of using this method to automate whale detection for population surveys.

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Published In

Remote Sensing in Ecology and Conservation

DOI

EISSN

2056-3485

Publication Date

December 1, 2023

Volume

9

Issue

6

Start / End Page

829 / 840

Related Subject Headings

  • 4104 Environmental management
  • 3103 Ecology
  • 0602 Ecology
  • 0502 Environmental Science and Management
 

Citation

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Green, K. M., Virdee, M. K., Cubaynes, H. C., Aviles-Rivero, A. I., Fretwell, P. T., Gray, P. C., … Jackson, J. A. (2023). Gray whale detection in satellite imagery using deep learning. Remote Sensing in Ecology and Conservation, 9(6), 829–840. https://doi.org/10.1002/rse2.352
Green, K. M., M. K. Virdee, H. C. Cubaynes, A. I. Aviles-Rivero, P. T. Fretwell, P. C. Gray, D. W. Johnston, C. B. Schönlieb, L. G. Torres, and J. A. Jackson. “Gray whale detection in satellite imagery using deep learning.” Remote Sensing in Ecology and Conservation 9, no. 6 (December 1, 2023): 829–40. https://doi.org/10.1002/rse2.352.
Green KM, Virdee MK, Cubaynes HC, Aviles-Rivero AI, Fretwell PT, Gray PC, et al. Gray whale detection in satellite imagery using deep learning. Remote Sensing in Ecology and Conservation. 2023 Dec 1;9(6):829–40.
Green, K. M., et al. “Gray whale detection in satellite imagery using deep learning.” Remote Sensing in Ecology and Conservation, vol. 9, no. 6, Dec. 2023, pp. 829–40. Scopus, doi:10.1002/rse2.352.
Green KM, Virdee MK, Cubaynes HC, Aviles-Rivero AI, Fretwell PT, Gray PC, Johnston DW, Schönlieb CB, Torres LG, Jackson JA. Gray whale detection in satellite imagery using deep learning. Remote Sensing in Ecology and Conservation. 2023 Dec 1;9(6):829–840.

Published In

Remote Sensing in Ecology and Conservation

DOI

EISSN

2056-3485

Publication Date

December 1, 2023

Volume

9

Issue

6

Start / End Page

829 / 840

Related Subject Headings

  • 4104 Environmental management
  • 3103 Ecology
  • 0602 Ecology
  • 0502 Environmental Science and Management