  The ocean is experiencing unprecedented rapid change, and visually monitoring
marine biota at the spatiotemporal scales needed for responsible stewardship is
a formidable task. As baselines are sought by the research community, the
volume and rate of this required data collection rapidly outpaces our abilities
to process and analyze them. Recent advances in machine learning enables fast,
sophisticated analysis of visual data, but have had limited success in the
ocean due to lack of data standardization, insufficient formatting, and demand
for large, labeled datasets. To address this need, we built FathomNet, an
open-source image database that standardizes and aggregates expertly curated
labeled data. FathomNet has been seeded with existing iconic and non-iconic
imagery of marine animals, underwater equipment, debris, and other concepts,
and allows for future contributions from distributed data sources. We
demonstrate how FathomNet data can be used to train and deploy models on other
institutional video to reduce annotation effort, and enable automated tracking
of underwater concepts when integrated with robotic vehicles. As FathomNet
continues to grow and incorporate more labeled data from the community, we can
accelerate the processing of visual data to achieve a healthy and sustainable
global ocean.
