Industry Paper: Classification of vessel activity in streaming data

Ioannis Kontopoulos, Konstantinos Chatzikokolakis, Konstantinos Tserpes, and Dimitris Zissis

DEBS ’20, July 13–17, 2020, Virtual Event, 2020

Abstract

In this paper we motivate the need for real-time vessel behaviour classification and describe in detail our event-based classification approach, as implemented in our real-world industry strong maritime event detection service at MarineTraffic.com. A novel approach is presented for the classification of vessel activity from real-time data streams. The proposed solution splits vessel trajectories into multiple overlapping segments and distinguishes the ones in which a vessel is engaged in trawling or long-lining operation (e.g. fishing activity) from other segments that a vessel is simply underway from its departure towards its destination. We evaluate the effectiveness of our tool on real-world data, demonstrating that it can practically achieve high accuracy results. We present our results and findings intended for both researchers and practitioners in the field of intelligent ship tracking and surveillance.

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