Develop a deep-learning method for analysing underwater video footage of cold-water coral (CWC) reefs in the Bay of Biscay. This approach will automate the quantification of live and dead coral, as well as identify key species within the reef ecosystem.
This technology will be particularly valuable for studying deep-sea CWC reefs, such as those found in La Gaviera Canyon, due to the challenges associated with traditional manual analysis.
External Use Case
Cold Water Coral Reefs
Improving Knowledge About Cold Water Coral Reefs
Aim
Development actions during iMagine
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Development and Testing: The actual development of the service, including the implementation of the deep learning algorithms for image labelling, should be carried out in this phase. It is crucial to allocate enough time for testing and debugging to ensure the accuracy and reliability of the results.
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Expected Results
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This use case will contribute to effective conservation strategies in the face of climate change and human pressures by improving our understanding of CWC reef health, biodiversity, and ecological processes. The findings can also inform marine spatial planning efforts, promoting sustainable practices.
Involved Institutions
IEO
Instituto Español de Oceanografía (IEO, CSIC)