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11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementThe components of the proposed model are illustrated using the example of a foreign vessel conducting illegalfishing activities.For the analysis to be performed, the raw data collected by different sensors must be segmented into appropriaterepresentations of the vessel's trajectory (step (a) in Figure 1). Applying these models to real mobile objects is anintensive task that can only be realized with an appropriate trajectory model. While different trajectory models havebeen proposed, most of them focus on spatio-temporal components (x,y,t) rather than semantic components (e.g.,movement in a restricted area) (Spaccapietra et al., 2008). Spatio-temporal positions alone do not provide sufficientinformation about the context in which mobile objects evolve, making it difficult to have a global interpretation ofmovement behaviors (Yan et al., 2012). Consequently, we decided to consider not only the spatio-temporal positionsof the trajectory but also semantic trajectory units (e.g., begin, stop, moves, end). These semantic units can be enrichedwith different types of knowledge (e.g., spatio-temporal, geographic, domain) to provide end-users with highlevelsemantic descriptions of trajectories and a better understanding of the situation. Using the illegal fishing scenariodescribed above, the semantic trajectory of the vessel could be represented as: (begin, port) → (move, sea,high-speed, 1 hour) → (move, restricted fishing area, fishing, 2 hours) → (end, port), where semantics units wereenriched with geographic (e.g., sea, restricted fishing area), spatio-temporal (e.g., move, begin) and domain (e.g.,fishing) properties to provide a better understanding of the vessel's trajectory.This first step allows performing further analyses of trajectories and identifying potential alerts that could relateto abnormal movements (step (b) in Figure 1). Depending on the human knowledge integrated in the surveillancesystem, different alerts can be automatically detected. Human knowledge can be encoded in the surveillance systemin the form of semantic rules. In the maritime domain, these rules can be simple, such as “if the speed of the vesselis above X, then generate an event of type Y”, or can be more complex and include different spatio-temporal elementssuch as “if two vessels are moving in parallel within a certain distance and during a certain time, then generatean event of type Z”. Using the previous example a typical rule could be “if a foreign fishing vessel enters a restrictedfishing area, then generate an event of type ‘illegal fishing’ ”. Once all of the rules are defined and the semantictrajectories are stored in the knowledge database, links with potential alerts have to be specified. This step isdone using a spatio-temporal inference engine that will automatically analyze semantic trajectories with the rules toidentify potential suspicious events. While many inference engines exist (e.g., Jena, FaCT++, Pellet), only a few ofthem are able to handle spatio-temporal data. For this research, further tests will be needed to select the most appropriateinference engine. Finally, the alerts detected will be described using the Simple Event Model proposed by VanHage et al. (2011) that provides the minimal set of classes, properties and constraint necessary to describe events.The ultimate goal of the proposed framework is the interpretation of vessels’ activities and behaviors (step (c) inFigure 1). This step uses a CBR (Schank, 1983) approach to compare previous behaviors defined by the operatorswith the current facts of the knowledge base. CBR comes from the field of AI and has been selected for its capacityto implement standard reasoning procedures similar to human reasoning and capture domain knowledge, even if thedomain is imperfectly understood or hard to codify. CBR relies on the assumption that successful solutions used tosolve past problems can be reused to solve new similar problems. Similarity between new and past problems iscomputed between sets of characteristics. For example, if a previous case similar to the illegal fishing scenario describedabove exists, then the identification of this situation as a potential illegal fishing behavior will be automaticallyproposed to the maritime operator.At this point, semantic events and semantic behaviors are just facts stored in the semantic store. To be analyzed,they need to be integrated through a specific user interface that provides a better visualization and a better understandingof the maritime situation (step (d) in Figure 1). The Hybrid Spatio-Temporal Filtering (HSF) interface developedby Enguehard et al. (2012) has been chosen due to its two major components that can be used to understandwhy potential events have been detected. These components are the geovisualization components and the interactivehybrid filtering components. The geovisualization component uses the Java World Wind framework and offers athree-dimensional view of the data. The interactive hybrid filtering component can be used to filter out uninterestingmovement data and to isolate specific movement patterns. This interface allows the operator's attention to focus onpotentially interesting data and then improves the understanding of the situation. To provide a better understandingof the situation, HSF will be extended by two other components. First, a query engine that allows maritime operatorsto query facts stored in the knowledge database and then identify specific data that can be used to improve the understandingof the maritime situation. Secondly, a validation engine that reduces false alarms by a weighted scorebasedrule adaptation mechanism through expert feedback.114
11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementFuture works and conclusionThis paper has described a novel semi-supervised framework based on spatial ontologies that can be used formaritime surveillance. An important contribution of our approach is to offer a consistent framework based on ontologiesthat allows modeling not only semantic trajectory but also semantic events and behaviors. Semantic trajectoriesare obtained using different ontologies (e.g., geographic, domain) and semantic events and behaviors are automaticallydetected by using maritime operator's knowledge stored in a semantic store. Therefore the user interfaceallows the maritime operator to understand the reasons why the abnormal events detected are considered suspect.Finally, to reduce the number of potential false alarms, the validation or rejection of detected facts is taken into accountby the framework through expert feedback.The proposed framework is still a work in progress and a number of questions remain to be answered. The nextstep will be to link these components together and test the semantic model with real situations. Our future work willnot only focus on the analysis of the spatio-temporal events associated to a vessel, but also to its relationship withothers elements of the context (e.g., other vessels, proximity of an area of interest). Although the proposed frameworkhas been designed for maritime anomaly detection, its approach is generic enough to make it applicable to abroader range of movement data and its application to other domains would also be interesting to explore.ReferencesBürkle, A. and B. Essendorfer (2010), “Maritime surveillance with integrated systems”. In: Waterside Security Conference (WSS2010), IEEE, Marina di Carrara, Italy: 1–8.Enguehard, R.A., O. Hoeber, and R. Devillers (2012), “Interactive exploration of movement data: A case study of geovisual analyticsfor fishing vessel analysis”. Information Visualization, 12:85–101.Hunter, A. (2009), “Belief modeling for maritime surveillance”. In: 12th International Conference on Information Fusion (Fusion2009), IEEE, Burnaby, BC, Canada: 1926–1932.Laxhammar, R. (2008), “Anomaly detection for sea surveillance”. In: 11th International Conference on Information Fusion (Fusion2008), IEEE, Cologne, Germany: 1–8.Martineau, E. and J. Roy (2011), Maritime Anomaly Detection: Domain Introduction and Review of Selected Literature, DefenceR&D Canada, Canada.Riveiro, M. and G. Falkman (2009), “Interactive Visualization of Normal Behavioral Models and Expert Rules for MaritimeAnomaly Detection”. In: Sixth International Conference on Computer Graphics, Imaging and Visualization (CGIV 2009), Singapore:459–466.Riveiro, M. and G. Falkman (2010), “Supporting the Analytical Reasoning Process in Maritime Anomaly Detection: Evaluationand Experimental Design”. In: 14th International Conference on Information Visualisation (IV 2010), London, UK: 170–178.Schank, R.C. (1983), Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge UniversityPress, New York, NY, USA.Spaccapietra, S., C. Parent, M.L. Damiani, J.A. de Macedo, F. Porto, and C. Vangenot (2008), “A conceptual view on trajectories”.Data & Knowledge Engineering, 65:126–146.Studer, R., V.R. Benjamins, and D. Fensel (1998), “Knowledge engineering: Principles and methods”. Data & Knowledge Engineering,25:161–197.Vandecasteele, A. and A. Napoli (2012), “An Enhanced Spatial Reasoning Ontology for Maritime Anomaly Detection”. In: 7thInternational Conference on System Of Systems Engineering (SOSE 2012), IEEE, Genova, Italy: 247–252.Van Hage, W.R., V. Malaisé, G.K.D. De Vries, G. Schreiber, and M. Van Someren (2011), “Abstracting and reasoning over shiptrajectories and web data with the Simple Event Model (SEM)”. Multimedia Tools and Applications, 57:1–23.Van Laere, J. and M. Nilsson (2009), “Evaluation of a workshop to capture knowledge from subject matter experts in maritimesurveillance”. In: 12th International Conference on Information Fusion (Fusion 2009), IEEE, Seattle, USA: 171–178.Yan, Z., D. Chakraborty, C. Parent, S. Spaccapietra, and K. Aberer (2012), “Semantic Trajectories: Mobility Data Computationand Annotation”. ACM Transactions on Intelligent Systems and technology, 9:1–34.115
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CoastGIS Conference 2013: Monitorin
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Sponsors
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Steering Committee Secretariat main
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Santos Basin, BrazilA.F. Romero, R.
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Coastline development and associate
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Visual resource management system f
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Use of Terrestrial-LiDAR for quanti
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Community beach monitoring: utilisi
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Data policy implications arising fr
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Global ocean observing system for w
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ResultsMetadata11 th International
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Coastal vulnerability index to glob
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generating probabilities contours f
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maps, the most critical conditions
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Statistical and spatial toolbox for
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Global oceans and marine planning
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Washington Coastal Atlas: creating
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Methods11 th International Symposiu
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Methods11 th International Symposiu
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Acknowledgments11 th International
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Long-term continuous observations o
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Comparing endmember extraction meth
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Integration of scientific data as a
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Conclusions11 th International Symp
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