1/24A semi-supervised learning framework basedA semi-supervised learning framework basedon spatio-temporal semantic events...
2/24Background & Research problemsMaritime domainProblemSemantic Event ModellingWhat is an ontology ?Simple Event ModelVes...
3/24ContextEconomic90% of world trade is transported by seaIn Europe 90% of oil and gas are transported by seaIllegal Fish...
4/24Poor interfaceData OverflowFew informationLarge surveillance areaHigh maritime traffic densityCognitive OverflowNo too...
5/24?ImproveImprove Detection & AnalysisBetter understandingunderstandingfor maritime surveillanceHigh volume of dataHeter...
6/24Improve UnderstandingAn enriched formalization with spatial capabilities offers a betterway to describe and analyze th...
7/24Formalize VocabularyRepresentReuseSharingKnowledgeAutomated ReasoningHumans & Systems Interoperability“an ontology is ...
8/24Concept A Concept BRelationsIndividual 1Properties1properties2Individual 2Ontology componentsVessel Type of VesselhasT...
9/24“Functionplayed”ROLEACTORPLACE“Who”“Where”“With What”“What”EVENTSimple Event Model: 5 cores classesBackground & Resear...
10/24“Functionplayed”ROLEACTORPLACE“Who”“Where”“Whit What”“What”EVENTTime-Stamped EntitysubClassOfsubClassOfsubClassOfsubC...
11/24ROLEACTORPLACEEVENTTime-Stamped EntitysubClassOfsubClassOfsubClassOfsubClassOfsubClassOfTakes place inTimeStampPartic...
12/24Actor TypeRole Type Event Type Object Type Place TypeROLEACTORPLACEEVENTTime-Stamped EntitysubClassOfsubClassOfsubCla...
13/24Background & Research problems > Semantic Event Modelling >Semantic Event Modelling > Prototype & examples
14/24ACTORPLACEEVENTVesselid:mmsiTankerRdf:typeRdf:typePort ofVancouverGeoNameId:6173335Rdf:typeEez:CanadaGeoNameId : 6251...
15/24Prototype architectureBackground & Research problems > Semantic Event Modelling > Prototype & examplesPrototype & exa...
16/24Dataset :More than 5 millions of AIS positionsBetween February and December 2009InformationPosition, timestamp, headi...
17/24Vessels positionsSpatio-Temporal interpolationof vessels positionsVessels TrajectorySpatio-Temporal interpolationof V...
18/24Feed OntologyBackground & Research problems > Semantic Event Modelling > Prototype & examplesPrototype & examples
19/24Semantic EventBackground & Research problems > Semantic Event Modelling > Prototype & examplesPrototype & examples
20/24Spatio-Temporal Semantic EventsBackground & Research problems > Semantic Event Modelling > Prototype & examplesProtot...
21/24Timeline to navigatethrough timeTime widgetto animate the data3D Web MappinginterfaceVisualization of the resultsBack...
22/242D View3D ViewExample of acceleration eventsBackground & Research problems > Semantic Event Modelling > Prototype & e...
23/24ConclusionOntologies provide a richer way to describe eventsA richer description can provide a better understanding o...
24/24Arnaud Vandecasteelea.vandecasteele [at] mun.caQuestions ?
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A semi-supervised learning framework based on spatio-temporal semantic events for maritime anomaly detection and behaviour analysis

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A semi-supervised learning framework based on spatio-temporal semantic events for maritime anomaly detection and behaviour analysis

  1. 1. 1/24A semi-supervised learning framework basedA semi-supervised learning framework basedon spatio-temporal semantic eventson spatio-temporal semantic eventsfor maritime anomaly detection and behaviour analysisfor maritime anomaly detection and behaviour analysisArnaud VandecasteeleArnaud VandecasteeleRodolphe DevillersRodolphe DevillersAldo NapoliAldo NapoliCoastGIS - GIS and New Technologies - June 20
  2. 2. 2/24Background & Research problemsMaritime domainProblemSemantic Event ModellingWhat is an ontology ?Simple Event ModelVessels behaviours analysisPrototype & examplesPrototype architectureComponents of the architectureExamples
  3. 3. 3/24ContextEconomic90% of world trade is transported by seaIn Europe 90% of oil and gas are transported by seaIllegal FishingOnly 6% of illegal fishing frauds are detected88% of fishing stocks in the EU are overexploitedIllegal immigration55% of illegal border crossing immigration is done bysea (EU)3000 illegal known immigrants lost their life at seaevery yearSource : ICC International Maritime BureauMaritime domainBackground & Research problems >Background & Research problems > Semantic Event Modelling > Prototype & examples
  4. 4. 4/24Poor interfaceData OverflowFew informationLarge surveillance areaHigh maritime traffic densityCognitive OverflowNo tools for automatic detectionMaritime information systemBackground & Research problems >Background & Research problems > Semantic Event Modelling > Prototype & examples
  5. 5. 5/24?ImproveImprove Detection & AnalysisBetter understandingunderstandingfor maritime surveillanceHigh volume of dataHeterogeneous dataand knowledgeDistributeddata and knowledgeAnalysis ofcomplex informationResearch problemBackground & Research problems >Background & Research problems > Semantic Event Modelling > Prototype & examples
  6. 6. 6/24Improve UnderstandingAn enriched formalization with spatial capabilities offers a betterway to describe and analyze the behaviour of the vessels1Formalizeexpert knowledgeAutomatedspatial reasoningSpatial Ontologies2Integrate the spatialdimension into ontologiesAutomatic detectionof suspicious eventsAutomatic identificationof abnormal behavioursResearch problemBackground & Research problems >Background & Research problems > Semantic Event Modelling > Prototype & examples
  7. 7. 7/24Formalize VocabularyRepresentReuseSharingKnowledgeAutomated ReasoningHumans & Systems Interoperability“an ontology is a formal, explicit specification of a shared conceptualisation”Studer, 1998Hepp, 2008Why an ontology ?Background & Research problems > Semantic Event Modelling >Semantic Event Modelling > Prototype & examples
  8. 8. 8/24Concept A Concept BRelationsIndividual 1Properties1properties2Individual 2Ontology componentsVessel Type of VesselhasTypeVessel 1IMO: 1234562Speed: 12TankerExample : how to describe a tanker ?hasTypesubConcept1 subConcept2subClassOf subClassOfBackground & Research problems > Semantic Event Modelling >Semantic Event Modelling > Prototype & examples
  9. 9. 9/24“Functionplayed”ROLEACTORPLACE“Who”“Where”“With What”“What”EVENTSimple Event Model: 5 cores classesBackground & Research problems > Semantic Event Modelling >Semantic Event Modelling > Prototype & examplesVan Hage, 2012
  10. 10. 10/24“Functionplayed”ROLEACTORPLACE“Who”“Where”“Whit What”“What”EVENTTime-Stamped EntitysubClassOfsubClassOfsubClassOfsubClassOfsubClassOfTimeStampLinked to a Time-Stamped EntityBackground & Research problems > Semantic Event Modelling >Semantic Event Modelling > Prototype & examples
  11. 11. 11/24ROLEACTORPLACEEVENTTime-Stamped EntitysubClassOfsubClassOfsubClassOfsubClassOfsubClassOfTakes place inTimeStampParticipates inas role(begins in place - ends in place)hasRoleTakes place inTakes place inInvolves inParticipates inLinked togetherBackground & Research problems > Semantic Event Modelling >Semantic Event Modelling > Prototype & examples
  12. 12. 12/24Actor TypeRole Type Event Type Object Type Place TypeROLEACTORPLACEEVENTTime-Stamped EntitysubClassOfsubClassOfsubClassOfsubClassOfsubClassOfTakes place inTimeStampParticipates inas role(begins in place - ends in place)hasRoleTakes place inTakes place inInvolves inParticipates inHas roletypeHas ActortypeHas EventtypeHas objecttypeHas placetypeHas ActrtypeLinked together with typesBackground & Research problems > Semantic Event Modelling >Semantic Event Modelling > Prototype & examples
  13. 13. 13/24Background & Research problems > Semantic Event Modelling >Semantic Event Modelling > Prototype & examples
  14. 14. 14/24ACTORPLACEEVENTVesselid:mmsiTankerRdf:typeRdf:typePort ofVancouverGeoNameId:6173335Rdf:typeEez:CanadaGeoNameId : 6251999Lat:49°1637" N Lon:123°0715" WHas ActortypeEventAnchorageParticipates inas role2013-06-16 2013-06-20begins at ends atExamples : Tanker anchored in a portBackground & Research problems > Semantic Event Modelling >Semantic Event Modelling > Prototype & examples
  15. 15. 15/24Prototype architectureBackground & Research problems > Semantic Event Modelling > Prototype & examplesPrototype & examples
  16. 16. 16/24Dataset :More than 5 millions of AIS positionsBetween February and December 2009InformationPosition, timestamp, heading, speed...http://www.chorochronos.org/?q=node/9Data from the French Naval Academy Resarch LabBackground & Research problems > Semantic Event Modelling > Prototype & examplesPrototype & examples
  17. 17. 17/24Vessels positionsSpatio-Temporal interpolationof vessels positionsVessels TrajectorySpatio-Temporal interpolationof Vessels TrajectorySpatio-Temporal filteringBackground & Research problems > Semantic Event Modelling > Prototype & examplesPrototype & examplesEtienne, 2012
  18. 18. 18/24Feed OntologyBackground & Research problems > Semantic Event Modelling > Prototype & examplesPrototype & examples
  19. 19. 19/24Semantic EventBackground & Research problems > Semantic Event Modelling > Prototype & examplesPrototype & examples
  20. 20. 20/24Spatio-Temporal Semantic EventsBackground & Research problems > Semantic Event Modelling > Prototype & examplesPrototype & examples
  21. 21. 21/24Timeline to navigatethrough timeTime widgetto animate the data3D Web MappinginterfaceVisualization of the resultsBackground & Research problems > Semantic Event Modelling > Prototype & examplesPrototype & examples
  22. 22. 22/242D View3D ViewExample of acceleration eventsBackground & Research problems > Semantic Event Modelling > Prototype & examplesPrototype & examples
  23. 23. 23/24ConclusionOntologies provide a richer way to describe eventsA richer description can provide a better understanding of a situationA semantic model linked to a webmapping interface has been createdThis prototype offers an interface to explore semantic eventsMore events type must be addedVessels must be linked to the timeline
  24. 24. 24/24Arnaud Vandecasteelea.vandecasteele [at] mun.caQuestions ?

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