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D3.2 Maritime Use Case: Initial Evaluation and Validation Report

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  <identifier identifierType="DOI">10.5281/zenodo.4034088</identifier>
      <creatorName>Project consortium members</creatorName>
    <title>D3.2 Maritime Use Case: Initial Evaluation and Validation Report</title>
    <date dateType="Issued">2020-06-29</date>
  <resourceType resourceTypeGeneral="Text">Project deliverable</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4034087</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf"></relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;This report summarises the results of the preliminary evaluation of the components developed for the Maritime Use&lt;/p&gt;

&lt;p&gt;Case that will be integrated and tested in a final experimentation at sea toward the end of the Project INFORE.&lt;/p&gt;

&lt;p&gt;The Maritime Use Case consists in developing and deploying a hybrid sensor network for ship target detection,&lt;/p&gt;

&lt;p&gt;localization and classification. Sensor data for the global scale and the local scale surveillance scenario, resulting in&lt;/p&gt;

&lt;p&gt;an extreme scale data stream, will be integrated in a maritime situation awareness platform to perform high level&lt;/p&gt;

&lt;p&gt;fusion tasks and target behavioural analysis in order to detect behavioural anomalies of vessels with respect to the&lt;/p&gt;

&lt;p&gt;normal traffic in the area of interest. The developed platform will be integrated/using the INFORE architecture,&lt;/p&gt;

&lt;p&gt;utilizing its capabilities to analyse extreme scale streamed data.&lt;/p&gt;

&lt;p&gt;The evaluation is performed using a set of key performance indices (KPIs) defined for the single components of the&lt;/p&gt;

&lt;p&gt;system and for the system as a whole, including human factors. The data used in the evaluation process are historical&lt;/p&gt;

&lt;p&gt;data available at the beginning of the Project INFORE or generated by simulating surveillance scenarios.&lt;/p&gt;

&lt;p&gt;The tested components include i) the CMRE coordinated sea surface robotic sensor network for target detection and&lt;/p&gt;

&lt;p&gt;localization by acoustic data, the target detection and classification systems for satellite data and for thermal and RGB&lt;/p&gt;

&lt;p&gt;video camera data, and the MarineTraffic situation awareness platform algorithms for complex event detection.&lt;/p&gt;

&lt;p&gt;The results of the evaluation are in general in line with the expectations. The coordinated robotic sensor network&lt;/p&gt;

&lt;p&gt;outperforms the base network without coordination, the classification from satellite data and camera data performs&lt;/p&gt;

&lt;p&gt;with classification accuracy around or greater than 90% and around 80%, respectively. The complex event classifier&lt;/p&gt;

&lt;p&gt;system has analogous performance with accuracy achieving, in some cases, values greater than 90%.&lt;/p&gt;

&lt;p&gt;The report provides a complete description of each component, including theoretical details, the evaluation of the&lt;/p&gt;

&lt;p&gt;components and finally a preliminary plan of the final experiment at sea to evaluate the Maritime Use Case as a whole.&lt;/p&gt;

&lt;p&gt;Future work before the final experiment includes implementation of the control and cooperation software on board&lt;/p&gt;

&lt;p&gt;the surface robot vehicles and further test of the classification algorithms for satellite and camera data and for complex&lt;/p&gt;

&lt;p&gt;events. Furthermore, the detailed plan, including logistic aspects, of the final experiment at sea will be started soon&lt;/p&gt;

&lt;p&gt;this year and finalized next year.&lt;/p&gt;</description>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/825070/">825070</awardNumber>
      <awardTitle>Interactive Extreme-Scale Analytics and Forecasting</awardTitle>
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