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

CORDIS · observation · Publication date unknown

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 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 end

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recordType

award

status

SIGNED

region

EU

value

1496433.13

unit

EUR