DOI: 10.5061/dryad.7482v2n
title: Data from: Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry

description: 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.
description: CNN Test Imagery and
 LabelsThis zip file includes
 the test images used for reported metrics along with the
 via_region_data.json label file that has the mask shapes in JSON format as
 needed for the neural network. Species specific .json files are also
 included.test.zipCNN Training Imagery and Labels Subset 1This zip file includes the first subset of training images used for training this CNN. This data is only subset for ease of upload and download but should be combined by the user on their local machine. The full label file for all training data is included with both subsets and is identical. That file "via_region_data.json" has the mask shapes in and species IDs in a JSON format as needed for the neural network.train_subset_1.zipCNN Training Imagery and Labels Subset 2This zip file includes the second subset of training images used for training this CNN. This data is only subset for ease of upload and download but should be combined by the user on their local machine. The full label file for all training data is included with both subsets and is identical. That file "via_region_data.json" has the mask shapes in and species IDs in a JSON format as needed for the neural network.train_subset_2.zipCNN Validation Imagery and LabelsThis zip file includes the validation images used for training this CNN. The label file for all validation data is included. That file "via_region_data.json" has the mask shapes in and species IDs in a JSON format as needed for the neural network.val.zip