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

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

SUBMITTER: Lowe SC 

PROVIDER: S-EPMC11806053 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature