Project description
Enhancing marine conservation and maritime surveillance through deep learning
The EU-funded AXOLOTL project aims to enhance the R&I capacity of the Cyprus Marine and Maritime Institute (CMMI) in marine biodiversity assessment and maritime surveillance using cutting-edge deep learning (DL) technologies. DL models will be trained using robustly built standardised data sets of in-situ sampled data and remotely sensed data from satellites and underwater acoustics. The proposed solutions will be validated in real-world scenarios around biodiversity assessment and maritime surveillance in Cypriot, Belgian and European contexts. The project will also seek to bridge the gap between CMMI, a Teaming Centre of Excellence and internationally recognised researchers and innovators from Belgium and France; to that end, it will facilitate knowledge transfer, networking for scientific excellence, and administrative and entrepreneurial competencies.
Objective
Technological areas such as Artificial Intelligence (AI) and ecosystems such as shipping, maritime and space have been strategically prioritised by Cyprus to improve its Research and Innovation (R&I) performance.
At their intersection lies the need to enhance marine conservation efforts and maritime surveillance by leveraging deep learning (DL) methodologies built on standardized and robust data sets. Indeed, in-situ sampling stands as a cornerstone in marine conservation, offering a direct approach to monitoring marine biodiversity; while remote sensing stands as a pivotal addition to maritime surveillance, expanding the scope beyond traditional Automatic Identification System capabilities. DL, as a cutting-edge AI tool, holds immense potential to enhance the analysis of in-situ samples and remotely sensed data.
AXOLOTL is proposed as a transformational international endeavour capable of enhancing the R&I capacity of CMMI, Cyprus and Europe in the interdisciplinary fields of DL-enhanced in-situ biodiversity assessment and DL-enhanced remote sensing for maritime surveillance. The project will contribute to closing the gap between a H2020 Teaming Centre of excellence and 2 strong innovators from France and Belgium through capacity-building, knowledge transfer, networking, and outreach activities at regional and international levels. Activities will go beyond the strictly scientific scope and support the mutual development, consolidation, and reinforcement of administrative, dissemination and entrepreneurial competencies, access to networks of excellence and the sustainable linkage between partners. The project’s R&I component will develop new strategies for improving data quality, standardization, and synchronization issues, devise novel interdisciplinary methodologies, develop robust DL models from state-of-the-art computer vision methods, and validate its proposed solutions in 2 relevant real-world contexts (biodiversity assessment and maritime surveillance).
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesbiological sciencesmarine biology
- social scienceseconomics and businessbusiness and managementinnovation management
- engineering and technologyenvironmental engineeringremote sensing
- natural sciencesbiological sciencesecologyecosystems
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
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Keywords
Programme(s)
Funding Scheme
HORIZON-CSA - HORIZON Coordination and Support ActionsCoordinator
6300 Larnaca
Cyprus