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AXOLOTL

Financiado

Cerrado

From sky to seafloor observation: Achieving eXcellence in Oceanic surveiLlance a...

From sky to seafloor observation: Achieving eXcellence in Oceanic surveiLlance and cOnservation Through deep Learning 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 interse... ver más

31/08/2027

SciCo Cyprus

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

SCICO CYPRUS LTD No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-04-29

HORIZON-WIDERA-2023-ACCESS-02-01: Twinning Bottom-Up ExpectedOutcome:For the first time this Twinning call adopts a partially directional approach allowi...

Innovación

Cerrada hace 1 año

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

3 Participantes

SciCo Cyprus

864.27K€ | Lider

UNIVERSITE DE BRETAGNE SUD

334.35K€ | Participante

FLANDERS MARINE INSTITUTE IN...

297.82K€ | Participante

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Duración del proyecto: 40 meses Fecha Inicio: 2024-04-29
Fecha Fin: 2027-08-31

Líder del proyecto

SCICO CYPRUS LTD No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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).

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