BenthicNet: A global compilation of seafloor images for deep learning applications

TL;DR

BenthicNet is a comprehensive global dataset of over 11 million seafloor images with annotations, enabling the development of deep learning models for automated analysis of benthic ecosystems.

Contribution

This work introduces BenthicNet, the largest curated seafloor image dataset with annotations, and demonstrates its use in training deep learning models for environmental monitoring.

Findings

Deep learning model trained on BenthicNet shows promise for automating image analysis.

The dataset includes 11.4 million images and 3.1 million annotations.

Open access facilitates further research and model development.

Abstract

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.