From sky to seafloor observation: Achieving eXcellence in Oceanic surveiLlance and cOnservation Through deep Learningcore
AXOLOTL · Horizon Europe grant · 2024-09-01–2027-08-31
EC contribution
Total cost
Beneficiaries
About the data
Source: CORDIS (official EU open data), Horizon Europe. Framework HORIZON · call HORIZON-WIDERA-2023-ACCESS-02 · scheme HORIZON-CSA · topic HORIZON-WIDERA-2023-ACCESS-02-01. CORDIS record →
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).
Beneficiaries (3)
| Organisation | Country | Role | EC contribution | SME |
|---|---|---|---|---|
| CMMI CYPRUS MARINE AND MARITIME INSTITUTE | CY | coordinator | €864,266 | |
| UNIVERSITE DE BRETAGNE SUD | FR | participant | €334,346 | |
| VLAAMS INSTITUUT VOOR DE ZEE | BE | participant | €297,821 |
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