Visual analytics for maritime domain awareness

Maintaining situation awareness in the maritime domain is a challenging mandate. Task analysis activities were conducted to identify where visual analytics science and technology could improve maritime domain awareness and reduce information overload. Three promising opportunities were identified: the visualization of normal maritime behaviour, anomaly detection, and the collaborative analysis of a vessel of interest. In this paper, we describe the result of our user studies along with potential visual analytics solutions and features considered for a maritime analytics prototype.

[1]  Bradley J. Rhodes,et al.  Probabilistic associative learning of vessel motion patterns at multiple spatial scales for maritime situation awareness , 2007, 2007 10th International Conference on Information Fusion.

[2]  Rikard Laxhammar,et al.  Anomaly detection for sea surveillance , 2008, 2008 11th International Conference on Information Fusion.

[3]  Jarke J. van Wijk,et al.  Eurographics/ Ieee-vgtc Symposium on Visualization 2009 Visualization of Vessel Movements , 2022 .

[4]  M. Sheelagh T. Carpendale,et al.  Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations , 2009, IEEE Transactions on Visualization and Computer Graphics.

[5]  Kristin A. Cook,et al.  Illuminating the Path: The Research and Development Agenda for Visual Analytics , 2005 .

[6]  Egils Sviestins,et al.  Rule-based situation assessment for sea surveillance , 2006, SPIE Defense + Commercial Sensing.

[7]  F. Johansson,et al.  Detection of vessel anomalies - a Bayesian network approach , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[8]  Gennady L. Andrienko,et al.  Spatio-temporal aggregation for visual analysis of movements , 2008, 2008 IEEE Symposium on Visual Analytics Science and Technology.

[9]  Tobias Schreck,et al.  Visual Cluster Analysis of Trajectory Data with Interactive Kohonen Maps , 2008, 2008 IEEE Symposium on Visual Analytics Science and Technology.

[10]  Ben Shneiderman,et al.  Integrating Statistics and Visualization for Exploratory Power: From Long-Term Case Studies to Design Guidelines , 2009, IEEE Computer Graphics and Applications.

[11]  Tom Ziemke,et al.  Visual Analytics for the Detection of Anomalous Maritime Behavior , 2008, 2008 12th International Conference Information Visualisation.

[12]  Jean Roy,et al.  Rule-based expert system for maritime anomaly detection , 2010, Defense + Commercial Sensing.

[13]  John T. Stasko,et al.  Jigsaw: Supporting Investigative Analysis through Interactive Visualization , 2007, 2007 IEEE Symposium on Visual Analytics Science and Technology.

[14]  Lars Niklasson,et al.  Trajectory clustering for coastal surveillance , 2007, 2007 10th International Conference on Information Fusion.

[15]  Robert Harper,et al.  Configurable Spaces: Temporal analysis in diagrammatic contexts , 2008, 2008 IEEE Symposium on Visual Analytics Science and Technology.

[16]  Daniel A. Keim,et al.  Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[17]  Alexander W. Skaburskis,et al.  The Sandbox for analysis: concepts and methods , 2006, CHI.

[18]  Orland Hoeber,et al.  Geovisualization of fishing vessel movement patterns using hybrid fractal / velocity signatures , 2010 .