The EU requires fishing to be environmentally friendly, economically viable and socially sustainable to provide long-term European food security. To achieve such objectives the fishing industry needs to remain profitable by increasing its efficiency in a changing environment as catches have reached the limit in most cases. Copernicus, the EU's Earth Observation (EO) Programme, offers information services based on satellite EO and in situ environmental data for the benefit of European citizens. SUSTUNTECH project will use Big data approaches in daily operations of tuna fishing vessels in order to generate added value to the environmental data services provided by Copernicus and provide data feedback that can increase data supply and services improvement considering industrial needs and citizen needs of resilient high-quality sources of food that are economically and environmentally sustainable. Current fisheries operations are based in individual operations of ships bringing to great exploitation inefficiencies not yet explore the full potential to fisheries efficiency improvement by combining EO data from Copernicus services, optimization heuristics, machine learning and big data methodologies in a fleet basis and not in individual ship basis. SUSTUNTECH approach of high value products using Copernicus data and services expect fuel and cost savings in an order of 25% to 40% per fisheries fleet by producing commercialized tools at TR6-7 level using combined capabilities of the members of the consortium. Research institutions will provide their expertise in the areas of machine learning, good practices, monitoring and fisheries scientific knowledge. While industrial partners will provide their expertise in the areas of sensors, advanced visualization, market focused product development and commercialization that will be commercialized through the industrial partners networks of clients with shared royalties’ schemes and keeping individual intellectual property rights.
H20-ENV-Climate action, environment, resource efficiency and raw materials