Ecosystem Monitoring at EMSO Sites by Video Imagery
To establish an operational and integrated service at the iMagine platform for automatic processing of video imagery, collected by cameras at EMSO underwater sites, identifying and further analysing interesting images for purposes of ecosystem monitoring.
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The use case involves underwater video monitoring at three EMSO sites: EMSO-Obsea, EMSO-Azores, and EMSO-SmartBay. The aim is to establish an integrated service on the iMagine platform for the automatic processing of video imagery, enabling the identification and analysis of relevant images for ecosystem monitoring. By leveraging the capabilities of the iMagine platform and implementing AI models, the project aims to automate and enhance the analysis of underwater video imagery at the EMSO sites, facilitating scientific research and improving ecological understanding.
At the EMSO-Obsea site, there is significant unexploited image data collected from an underwater camera observing various fish species. Applying AI tools to these images would allow the extraction of valuable biological content, creating derived datasets that marine scientists can use to draw ecological conclusions. Manual analysis of the extensive dataset is time-consuming, and analyzing only a subset of the data would result in losing important information. Utilising the iMagine platform, a Deep Learning service will be trained and deployed to obtain species abundance data from existing and future images. These derived datasets will be crucial for studying species presence/absence over time and understanding changes in abundance in relation to environmental parameters, providing insights into the impact of climate change on the local fish community.