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|Title:||Unsupervised Maritime Pattern Analysis to Enhance Contextual Awareness|
|Authors:||FERNANDEZ ARGUEDAS VIRGINIA; PALLOTTA Giuliana; VESPE MICHELE|
|Citation:||Proceedings of the 1st International Workshop on Context-Awareness in Geographic Information Services (in conjunction with GIScience 2014), 2014 p. 50-61|
|Type:||Articles in periodicals and books|
|Abstract:||Maritime Situational Awareness aims at monitoring maritime activities and ensuring safety and security, based on contextual knowledge. Maritime contextual information is difficult to access, resource-consuming to update and sometimes unavailable. Thus, data-driven approaches to derive contextual information are required to support maritime situational awareness systems. In this paper, a data-driven algorithm is proposed to extrapolate maritime traffic contextual information from real-time self-reporting data. The knowledge discovery process focuses on the detection and definition of the maritime corridors, based on the construction of maritime traffic networks. The maritime traffic network provides maritime contextual knowledge to automatically update the Maritime Situational Picture, contributing towards Maritime Situational Awareness and risk management systems evolution.|
|JRC Directorate:||Space, Security and Migration|
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