A. Exploratory research

Project n°1: BigData Ocean

  1. Project carriers: Exploiting Oceans
  2. Beneficiaries: Fuel Consumption Reduction, oil spill pollution,Maritime Security and Anomaly Detection, the next clean energy source
  3. Users: Maritime apps
  4. Need: Take full advantage of maritime data and transform them into actionable information
  5. Principle: Rocking the boat with BigDataOcean Platform
  6. Main technologies: Database, Scientific models
  7. Source: https://www.bigdataocean.eu/

Project n°2: Responsible Travel

  1. Project carriers: Travel Package
  2. Beneficiaries: Tourist who care marine life
  3. Users: Tourism who wants to be a part of marine protect
  4. Need: Helps tourists to book a trael package
  5. Principle: Combining robust data sets, unique digital infrastructure and machine learning.
  6. Main technologies: Machine learning, predictive modeling
  7. Source: https://www.responsibletravel.com/holidays/marine-conservation/travel-guide/responsible-marine-conservation

Project n°3: CLS

  1. Project carriers: Environment & climate monitoring Sustainable fisheries management Maritime security Fleet management Energies & infrastructures monitoring
  2. Beneficiaries: Earth
  3. Users: Companies
  4. Need: To deploy innovative space-based solutions to understand and protect our planet, and to manage its resources sustainably.
  5. Principle: Operating satellite systems and providing high value-added products and services
  6. Main technologies: Marchine learning models
  7. Source: https://www.cls.fr/en/cls-group/

Project n°4: Sea Cleaners

  1. Project carriers: Plastic pollution
  2. Beneficiaries: Land and sea
  3. Users: Organisations
  4. Need: Plastic pollution threatens ecosystems, economies and human health.
  5. Principle: They collect floating plastic waste in high concentration areas before it sinks or breaks down into microplastics, and we develop innovative collection and reuse solutions of plastic waste in the sea, with as little environmental impact as possible 
  6. Main technologies: Database, predictive modeling
  7. Source: https://www.theseacleaners.org/

B.Deepening

I.BigData

  1. Carriers and actors of the project:To propose and validate maritime big data scenarios
  2. Research question: How can we take full advantage of maritime data and transform them into actionable information?
  3. The reason you selected this project:  Unlike other companies , this is achieved through a multi-segmented platform that combines data of different velocity, variety, and volume in an inter-linked, trusted, multilingual system, producing a big-data repository of high value and veracity for project participants and local communities.

II.User scenario

  1. Users: EU-based companies, organisations and scientists
  2. Persona:
    • Ship owners, Maritime Equipment Constructors
    • Damage and mechanical failures detection and predictive maintenance of vessel equipment. Investigation of the impact of the environmental conditions and the operational decisions taken on the vessel's fuel consumption
  3. Key features: Vessel Fault Detection, Predictive Maintenance and Fuel Consumption Reduction
  4. UX storyboard
  5. Through this pilot, data from every available sensor will be collected and formulate a knowledge base that each owner and/or operator exploits towards the effort of being proactive rather than reactive and operationally efficient. These data, after being cleaned and integrated, will feed a complex prediction model for fault/damage/ failure prediction and a decision support tool for fuel consumption.

III.Technical analysis

  1. General principle: One to two phrases to explain briefly how the project technically works Digital Farming’s leading software platform
  2. Technical overview - AI version : use statistical and agronomic models, research and data (including historical, estimated and simulated weather and agronomic data), to generate the recommendations and precipitation, moisture, temperature, growth stage and other information. 
  3. Added value thanks to Artificial Intelligence: Collect, store, and visualize critical field data to monitor and measure the impact of your agronomic decisions on your fields to optimize yield and maximize profit.