ArticlePloS one2019
Aerial-trained deep learning networks for surveying cetaceans from satellite imagery.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Features of interest from a multi-season satellite survey of baleen whales on the West Antarctic Peninsula.Scientific data · 2025Article
- Enhancing crayfish sex identification with Kolmogorov-Arnold networks and stacked autoencoders.Scientific reports · 2025Article
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- An Experimental Methodology for Automated Detection of Surface Turbulence Features in Tidal Stream Environments.Sensors (Basel, Switzerland) · 2024Article
- Amazonian amphibians: diversity, spatial distribution patterns, conservation and sampling deficits.Biodiversity data journal · 2024Article
- Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys.PloS one · 2024Article
- Article
- Artificial intelligence convolutional neural networks map giant kelp forests from satellite imagery.Scientific reports · 2022Article
- Current global population size, post-whaling trend and historical trajectory of sperm whales.Scientific reports · 2022Article
- Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures.Sensors (Basel, Switzerland) · 2022Article
- Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models.Scientific data · 2022Article
- Leveraging social media and deep learning to detect rare megafauna in video surveys.Conservation biology : the journal of the Society for Conservation Biology · 2022Article
- Estimating the abundance of the critically endangered Baltic Proper harbour porpoise (Ecology and evolution · 2022Article
- Review
- The Potential of Satellite Imagery for Surveying Whales.Sensors (Basel, Switzerland) · 2021Article
- Article
- A comparison of baleen whale density estimates derived from overlapping satellite imagery and a shipborne survey.Scientific reports · 2020Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most cetacean species are wide-ranging and highly mobile, creating significant challenges for researchers by limiting the scope of data that can be collected and leaving large areas un-surveyed. Aerial surveys have proven an effective way to locate and study cetacean movements but are costly and limited in spatial extent. Here we present a semi-automated pipeline for whale detection from very high-resolution (sub-meter) satellite imagery that makes use of a convolutional neural network (CNN). We trained ResNet, and DenseNet CNNs using down-scaled aerial imagery and tested each model on 31 cm-resolution imagery obtained from the WorldView-3 sensor. Satellite imagery was tiled and the trained algorithms were used to classify whether or not a tile was likely to contain a whale. Our best model correctly classified 100% of tiles with whales, and 94% of tiles containing only water. All model architectures performed well, with learning rate controlling performance more than architecture. While the resolution of commercially-available satellite imagery continues to make whale identification a challenging problem, our approach provides the means to efficiently eliminate areas without whales and, in doing so, greatly accelerates ocean surveys for large cetaceans.
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