Towards net zero: development of AI enabled biological observing

Sustainable management of the marine environment is a global concern, perhaps best highlighted by the UN declaration of 2021-2030 as the Decade of Ocean Science for Sustainable Development. Ecological understanding of the non-coastal marine environment has lagged behind the physical, chemical and geological understanding due to challenges in observing and monitoring marine life at depth and in open ocean environments. 

Modern seafloor survey and monitoring platforms, including Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), and Autonomous Landers, are able to collect an array of spatially and temporally explicit, multi-sensor data, including vast video and / or image datasets, offering either high or large spatially and temporally resolved datasets. While use of these platforms, and their ability to make concurrent visual and environmental observation have already transformed our understanding of marine ecosystems, particularly hard substrate systems like seamounts and hydrothermal vents, the full potential of these autonomous and robotic systems has not yet been realised. 

One of the greatest challenges to realising that potential lies in overcoming the bottleneck created by the need for manual (human) interpretation of images and video in order to extract quantitative biological data. Recently developments in artificial intelligence and computer vision have offered a potential mechanism to overcome that bottleneck, offering a faster, more consistent, cost effective and shareable alternative to manual annotation. 

We have established that deep learning (a branch of artificial intelligence) can be used to reliably and quickly count specific species in the right conditions. This capacity needs to be expanded to a wider selection of taxa and pipelines developed that can be applied in-situ, moving us toward a future of AI enabled biological observing. Realising this future is important to reducing the carbon footprint of marine biological research, and helping us achieve our climate change targets. In this project we will investigate the best methods to translate the large volume of data collected by autonomous and robotic systems, into ecological knowledge to then feed into models enabling us to make predictions on how biodiversity is distributed and may change over time. This will drastically improve our perception of the oceans ecology and better inform conservation and management measures.