Title
Mining Vessel Trajectories For Illegal Fishing Detection
Abstract
In this paper we propose a data-driven approach to detection and tracking of dark fishing in high-volume marine traffic datasets front 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 tralecoly 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.
Year
DOI
Venue
2019
10.1109/BigData47090.2019.9006545
2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
Keywords
Field
DocType
Dark Fishing, Maritime Situation Analysis, Trajectory Analysis, Anomaly Detection, Profiling and Ranking
Data mining,Anomaly detection,Fishing,Fishery,Port (computer networking),Ranking,Computer science,Profiling (computer programming),Illegal fishing,Trajectory,Food security
Conference
ISSN
Citations 
PageRank 
2639-1589
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
amir yaghoubi shahir182.59
Mohammad A. Tayebi2507.59
Uwe Glässer345659.36
Tilemachos Charalampous400.34
Zahra Zohrevand531.41
Hans Wehn6467.93