{"id":97861843,"date":"2020-11-16T11:12:41","date_gmt":"2020-11-16T10:12:41","guid":{"rendered":"https:\/\/fundacion-biodiversidad.es\/programa_pleamar\/deep-ramp-deep-learning-to-improve-the-management-of-the-network-of-marine-protected-areas-of-the-north-atlantic-demarcation\/"},"modified":"2020-11-16T11:12:41","modified_gmt":"2020-11-16T10:12:41","slug":"deep-ramp-deep-learning-to-improve-the-management-of-the-network-of-marine-protected-areas-of-the-north-atlantic-demarcation","status":"publish","type":"programa_pleamar","link":"https:\/\/fundacion-biodiversidad.es\/en\/programa_pleamar\/deep-ramp-deep-learning-to-improve-the-management-of-the-network-of-marine-protected-areas-of-the-north-atlantic-demarcation\/","title":{"rendered":"DEEP-RAMP &#8211; Deep-learning to improve the management of the Network of Marine Protected Areas of the North Atlantic Demarcation."},"content":{"rendered":"<p style=\"text-align: justify\">\n            <strong>Description<\/strong>\n          <\/p>\n<p style=\"text-align: justify\">The protection and restoration of vulnerable habitats such as cold-water coral reefs or gorgonian forests are crucial from an environmental perspective, but also for the sustainability of some species of commercial fishing interest depend on them. In fact, many protected areas are considered <em>Essential Fish Habitats<\/em> (EFH), essential as recruitment or spawning areas. Management measures for these areas and Marine Protected Areas can help maintain productive fisheries and the good environmental health of the ocean. Today, visual analysis by an expert bentologist is the only method used to identify images, making it the bottleneck in these studies. So much so that today a huge amount of images are being collected with these vehicles that cannot be transformed into useful information for management and decision-making.    <\/p>\n<p style=\"text-align: justify\">In this sense, the DEEP-RAMP project will obtain information through optical sensors (cameras) integrated into remotely controlled underwater vehicles and will test and implement algorithms for automatic identification, improving the tasks of identification of structuring species for inventory and mapping of vulnerable benthic habitats of deep rocky bottoms of marine Natura 2000 Network areas. The target species will be found within sessile organisms (corals, gorgonians, deep-sea sponges), echinoderms, or similar. <em>Deep learning<\/em> algorithms will also be developed for automatic classification of species in underwater images.  <\/p>\n<p style=\"text-align: justify\">The results obtained on the development of the methodology, results and involvement of the project and the optimization of the management of protected areas will be disseminated.<\/p>\n<p style=\"text-align: justify\">\n<p style=\"text-align: justify\">\n            <a href=\"https:\/\/www.programapleamar.es\/proyectos\/deep-ramp-deep-learning-para-mejorar-la-gestion-de-la-red-de-areas-marinas-protegidas-de\">See the project<\/a>\n          <\/p>\n","protected":false},"featured_media":22405,"parent":0,"template":"","meta":{"_acf_changed":false},"financiaci_n":[652],"lineas_actuacion_tematicas":[655],"class_list":["post-97861843","programa_pleamar","type-programa_pleamar","status-publish","has-post-thumbnail","hentry","financiaci_n-fondo-europeo-maritimo-de-pesca-en","lineas_actuacion_tematicas-marine-ecosystems"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - 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