Benthosearcher: a machine learning based tool to ultra-fast, automatic characterization of vulnerable marine ecosystems

The degradation and impoverishment of the seabed that has been detected during last decades is the result of numerous variables, among which are uncontrolled exploitation of the seabed concerning its vulnerability, based on trawling gear. Intending to bring the situation under control, European fish...

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Detalhes bibliográficos
Autores: Domínguez Arca, Vicente, Ovalle Macías, Juan Carlos, Taboada Antelo, Luis
Formato: artículo
Fecha de publicación:2023
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/392100
Acesso em linha:https://hdl.handle.net/2117/392100
Access Level:acceso abierto
Palavra-chave:Marine ecology
Machine learning
Vulnerable marine ecosystems
iObserver
Trawling gear
Ecologia marina
Aprenentatge automàtic
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Descrição
Resumo:The degradation and impoverishment of the seabed that has been detected during last decades is the result of numerous variables, among which are uncontrolled exploitation of the seabed concerning its vulnerability, based on trawling gear. Intending to bring the situation under control, European fishing authorities have concluded a series of proposals to promote, define and defend Marine Vulnerable Ecosystems (VMEs), among which one of the most controversial and with the greatest social and economic impact is the veto of trawling in numerous fishing areas/grounds that currently are exploited by a large c number of vessels from many countries of the EU, which see their livelihood in danger. In the process of proposing an alternative that is attractive to both parts, we propose an automatic, real-time tool (BentoSearcher) based on artificial intelligence so that trawlers have will have the autonomy to decide whether or not to cast the net on the seabed in which the vessel is operating based on the data of benthic species detected in previous fishing hauls or trips that characterize and allow to identify vulnerable seabeds.