Gray whale detection in satellite imagery using deep learning

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.

Automated summary

The combination of VHR satellite imagery and deep learning improves efficiency and spatial coverage of global whale population surveys. Regular and accurate surveys are important for conservation efforts due to recovering whale species and anthropogenic threats. In this study, a state-of-the-art object detection model (YOLOv5) was trained to detect whales in VHR satellite images, achieving high precision and recall rates across different experiments. The results suggest the prioritization of expanding representative satellite datasets for automated whale detection in population surveys.