Pubblicazione scientifica

Mining Vessel Trajectories for Illegal Fishing Detection

FILONE TECNOLOGICO	TEMA
economia circolare e sostenibilità
trasformazione e commercializzazione

RIASSUNTO

In this paper we propose a data-driven approach to detection and tracking of dark fishing in high-volume marine traffic datasets from vessel tracking services. Dark fishing refers to stealthy fishing operations by vessels trying to hide their illicit activities related to various forms of illegal fishing-one of the most serious threats to world fisheries and fish populations worldwide as well as to global food security. Our approach builds on profiling and ranking fishing vessels by analyzing their routine operations over extended time periods to uncover abnormal activity patterns associated with dark fishing. The focus is on vessel movement patterns rendered as a trajectory with defined starting and endpoints such as ports and known anchorage locations. Specifically, we analyze scenarios where the fishing pattern, with the fishing gear in the water, is obscured in a vessel's reported trip data. Our experimental evaluation, using a large dataset of fishing vessel trajectories from coastal waters of North America, shows the effectiveness and efficiency of the proposed method in differentiating between suspicious and normal fishing vessels irrespective of the vessel type.

DATA

Data di pubblicazione: 01/12/2019

AUTORI

AMIR YAGHOUBI SHAHIR
MOHAMMAD A TAYEBI
UWE GLASSER
TILEMACHOS CHARALAMPOUS
ZAHRA ZOHREVAND
HANS WEHN

ENTE DI AFFERENZA

SIMON FRASER UNIVERSITYSOFTWARE TECH LAB
MDA SYS LTD

RIVISTA

2019 IEEE International Conference on Big Data (Big Data) (Page(s): 1917-1927)

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