Data from: Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry

The flourishing application of drones within marine science provides more
opportunity to conduct photogrammetric studies on large and varied
populations of many different species. While these new platforms are
increasing the size and availability of imagery datasets, established
photogrammetry methods require considerable manual input, allowing
individual bias in techniques to influence measurements, increasing error
and magnifying the time required to apply these techniques. Here, we
introduce the next generation of photogrammetry methods utilizing a
convolutional neural network to demonstrate the potential of a deep
learning‐based photogrammetry system for automatic species identification
and measurement. We then present the same data analysed using conventional
techniques to validate our automatic methods. Our results compare
favorably across both techniques, correctly predicting whale species with
98% accuracy (57/58) for humpback whales, minke whales, and blue whales.
Ninety percent of automated length measurements were within 5% of manual
measurements, providing sufficient resolution to inform morphometric
studies and establish size classes of whales automatically. The results of
this study indicate that deep learning techniques applied to survey
programs that collect large archives of imagery may help researchers and
managers move quickly past analytical bottlenecks and provide more time
for abundance estimation, distributional research, and ecological
assessments.