Characterisation of sediment patterns and benthic megafauna distribution using automated underwater image analysis
Seafloor habitat classification and marine biodiversity assessment studies are core fundamental activities in marine science research. These studies are crucial because they provide foundational data for the establishment of accurate and reliable baseline marine information. Established marine biodiversity and habitat baselines provide a sound scientific basis for tracking changes in ecosystem health, thereby supporting evidence-based decision making for the overall protection of marine environments. Mapping these vast remote marine ecosystems is typically achieved using (medium resolution) acoustics methods, whereas ground truthing and detailed investigations of specific target sites are usually performed using high resolution optical imaging. While marine scientists have used images to study marine ecosystems for decades, the manual annotation approaches that were traditionally used to interpret the images are no longer feasible in this terrabyte-scale marine big data regime. To complement conventional manual approaches, automated image analysis workflows are required to e.g improve visibility of degraded raw images, as well as to expedite the transformation of these terabyte-scale seafloor images (and videos) into semantic habitat classes or megafaunal taxa. The workflows should also investigate and account for potential sampling and scaling biases that arise from e.g the failure of the imaging platform to maintain a consistent altitude above the seafloor. Finally, the workflows must also allow for seamless integration of the generated annotations with spatio-ecological models, in order to provide geographic context to the image-derived annotations. Therefore, this thesis implements integrated workflows centered around the above mentioned aspects, and reports detailed findings based on specific case studies from scientific expeditions to the Clarion-Clipperton Zone in the Pacific, as well as the tropical North Atlantic.
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