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

arXiv:2405.05241v1 Announce Type: cross
Abstract: Advances in underwater imaging enable the collection of extensive seafloor image datasets that are necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering expedient mobilization of this crucial environmental information. Recent machine learning approaches provide opportunities to increase the efficiency with which seafloor image datasets are analyzed, yet large and consistent datasets necessary to support development of such approaches are scarce. Here we present …

applications arxiv compilation cs.cv cs.lg deep learning global images type