This study presents a novel approach to high-resolution density distribution mapping of two key species of the 1170 "Reefs" habitat, Dendrophyllia cornigera and Phakellia ventilabrum, in the Bay of Biscay using deep learning models. The main objective of this study was to establish a pipeline based on deep learning models to extract species density data from raw images obtained by a remotely operated towed vehicle (ROTV). Different object detection models were evaluated and compared in various shelf zones at the head of submarine canyon systems using metrics such as precision, recall, and F1 score. The best-performing model, YOLOv8, was selected for generating density maps of the two species at a high spatial resolution. The study also generated synthetic images to augment the training data and assess the generalization capacity of the models. The proposed approach provides a cost-effective and non-invasive method for monitoring and assessing the status of these important reef-building species and their habitats. The results have important implications for the management and protection of the 1170 habitat in Spain and other marine ecosystems worldwide. These results highlight the potential of deep learning to improve efficiency and accuracy in monitoring vulnerable marine ecosystems, allowing informed decisions to be made that can have a positive impact on marine conservation.
Keywords: Artificial intelligence; Habitat mapping; Natura 2000 network; Object detection model; Vulnerable marine ecosystem.
© 2024 Gayá-Vilar et al.