Learning in the Deep: Quantifying change in deep-sea benthic environments using 3D image reconstructions

Seafloor hydrothermal vents, surface-exposed gas hydrate fields and live coral reefs are known to form local hotspots for biological activity. The complex interactions that take place in these ecosystems makes it difficult to estimate the distribution of biomass in these areas, which in turn limits our ability to predict the potential effects of human activities in these environments. While recent advances in underwater robotics have made it possible to routinely generate multi-hectare scale, high-resolution 3D image reconstructions of the seafloor [1], the dense populations of mega-benthos (with reports of more than 100,000 animals from different species in a single field [2]) makes manual identification of seafloor organisms a major bottleneck in the flow of information from raw in-situ observation to the quantitative estimates needed for scientific interpretation.
The aim of this project is to develop computational methods to assist the interpretation of georeferenced seafloor visual imagery, and so enable quantitative studies of the temporal changes the distribution of mega-benthic organisms. This fits within the broader scope of improving our ability to monitor and understand the response of seafloor ecosystems to human influence.