Conference paper Open Access

Pseudo-synthetic datasets in support to maritime surveillance algorithms assessment

Clément Iphar; Anne-Laure Jousselm; Cyril Ray


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  <identifier identifierType="DOI">10.5281/zenodo.2555341</identifier>
  <creators>
    <creator>
      <creatorName>Clément Iphar</creatorName>
      <affiliation>NATO Science and Technology Organization,  Centre for Maritime Research and Experimentation (CMRE)</affiliation>
    </creator>
    <creator>
      <creatorName>Anne-Laure Jousselm</creatorName>
      <affiliation>NATO Science and Technology Organization,  Centre for Maritime Research and Experimentation (CMRE</affiliation>
    </creator>
    <creator>
      <creatorName>Cyril Ray</creatorName>
      <affiliation>NATO Science and Technology Organization,  Centre for Maritime Research and Experimentation (CMRE)</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Pseudo-synthetic datasets in support to maritime surveillance algorithms assessment</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-02-01</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2555341</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2555340</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020_datacron</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In the maritime domain, the ever-growing availability of data from&lt;br&gt;
systems such as the Automatic Identification System (AIS) enables the monitoring of worldwide maritime activities. The processing of huge amounts of spatial and temporal data rises issues linked to Big Data analyses. In particular, this paper focuses on the lack of veracity of data, and specifically on the characterisation of AIS dataset quality. In this paper, we aim at producing datasets either with a known and controlled veracity levels, or with added spatial events. Such quantified variations taking into account the initial quality level of the dataset and the desired level of degradation are performed following the mechanisms enabling data degradation, data improvement or event injection. A library has been developed, enabling the generation of those pseudo-synthetic datasets to be further used as benchmark for the assessment of algorithms solving Maritime Situation Awareness (MSA) issues such as anomaly detection.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/687591/">687591</awardNumber>
      <awardTitle>Big Data Analytics for Time Critical Mobility Forecasting</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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