How well do you know your ocean creatures? Can you identify a sea anemone? How about a fanfin anglerfish, a sea gooseberry, a red spotted siphonophore, a flapjack octopus or a hagfish?
As users learn more they get better at classifying animals. And as they classify animals, they’re actually training machine learning algorithms. Eventually, Katija said, those algorithms will help scientists sort through the massive backlogs of aquatic visual data.
Ocean Vision AI is one year in to a $5 million grant from the National Science Foundation’s Convergence Accelerator program, which funds projects to “solve societal challenges.” “The ocean research community doesn’t have a central place like this yet,” said Lilli Carlsen, the project’s engagement coordinator. “We’re creating a space that brings together experts from machine learning and also the ocean research community to work together on this huge challenge of studying the ocean and of creating a better understanding of what’s out there so that we can inform responsible decision making and ocean stewardship.
Megan Cromwell, assistant chief data officer for NOAA’s National Ocean Service, has contributed. She worked on three major NOAA projects that produced massive amounts of visual data. She needed to figure out how to get those photos and videos — mostly recorded on hard drives now, but previously on physical media like mini-DVs and DVDs — to researchers in a useful way.