Deep learning-assisted high resolution mapping of vulnerable habitats within the Capbreton Canyon System, Bay of Biscay

The Capbreton Canyon System is an area currently under study for its proposal as a Site of Community Importance under the EU Habitats Directive in the context of the LIFE IP INTEMARES project. Identifying and mapping benthic Vulnerable Marine Ecosystems (VMEs) plays a key role in this process. Although obtaining information on species distribution in deep sea rocky habitats has traditionally been a complicated task, the development of underwater remote sensing techniques resulted in a massive increase in the collection of digital imagery; however, processing all this information has led to another bottleneck due to the time-consuming nature of biota manual annotation. At this point, the use of computer vision and deep learning to automate image processing has substantial benefits but has rarely been adopted within the field of marine ecology. This study presents the integration of deep learning techniques for benthic fauna identification, high resolution multibeam echosounder (MBES) data and Species Distribution Models (SDMs), to map the potential habitat of the yellow coral Dendrophyllia cornigera, a representative species of the VME 1170 Reef habitat, on the circalitoral area of the Capbreton Canyon System. The localization and identification of the coral colonies was based on more than 7500 photographs taken during the INTEMARES-CapBreton 0619 and 0620 surveys using the photogrammetric ROTV Politolana. For the automatic annotation of the image set a deep learning based framework was developed by testing two different deep neural networks architectures; a FasterRCNN+Resnet101 model, accomplishing a precision of 100% over human expert annotation for presence/absence discrimination, was selected. Environmental data included different quantitative terrain attributes derived from high resolution MBES bathymetry data. A presence-only species distribution model, Maximum Entropy (MaxEnt), was used to infer the spatial distribution of D. cornigera over the study area. Pre