PowerPoint: Seatube - Satlink's on Board Electronic Monitoring and Video Recording Solution Satlink is a Spanish engineering company that develops technological solutions in collaboration with the fishing industry, NGOs and governments worldwide. These solutions support seafood traceability and manage, optimize and preserve fish resources. The Satlink SeaTube System is a cost effective …
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PowerPoint: Machine Learning for Big Fishery Visual Data
PowerPoint: Machine Learning for Big Fishery Visual Data …
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PowerPoint: Utilizing Wireless Technologies to Monitor New England Groundfish
PowerPoint: Utilizing Wireless Technologies to Monitor New England Groundfish Once a profitable fishery supporting coastal communities, the groundfish fishery in New England is in crisis. One factor is the mixed stock nature of the fishery where stocks with low catch limits constrain the harvest of more abundant stocks. In addition, climate-mediated changes in this region are unprecedented, and …
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PowerPoint: Integrated Operational Approach to EM & ER Onboard Fishing Vessels-Now and Tomorrow
PowerPoint: Integrated Operational Approach to EM & ER Onboard Fishing Vessels-Now and Tomorrow Fisheries management is plagued by lack of verifiable data for compliance and stock assessments. Low vessel observer coverage, and delays in data transfer, transcription, integration, and analysis are the norm. These problems hinder timely analysis and updates of fishing regulations relevant to …
PowerPoint: Open Source Software Platform for Electronic Monitoring
PowerPoint: Open Source Software Platform for Electronic Monitoring Sea State Inc. has developed a suite of software applications to support commercial fisheries electronic monitoring (EM) data acquisition, review, summarization and archival. This National Fish and Wildlife Foundation funded project, in partnership with Saltwater Inc. and Chordata LLC, provides a platform that is adaptable to …
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PowerPoint: Query Learning for Fish Identification
PowerPoint: Query Learning for Fish Identification In fishery community, image-classification-based supervised approaches have been well studied for species identification. However, the classifiers learned from one dataset usually cannot directly be applied for a new dataset since the data captured from different years or regions often have large difference, like different colors, camera …
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