BenthicNet: A global compilation of seafloor images for deep learning applications
This addresses the bottleneck in analyzing seafloor images for environmental monitoring, though it is incremental as it builds on existing datasets and annotation schemes.
The authors tackled the problem of analyzing extensive seafloor imagery by creating BenthicNet, a global compilation of over 11.4 million images with 3.1 million annotations, and trained a deep learning model that shows utility for automating image analysis tasks.
Advances in underwater imaging enable collection of extensive seafloor image datasets necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering mobilization of this crucial environmental information. Machine learning approaches provide opportunities to increase the efficiency with which seafloor imagery is analyzed, yet large and consistent datasets to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 3.1 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for reuse at https://doi.org/10.20383/103.0614.