Mining Vessel Trajectories for Illegal Fishing Detection
Mining Vessel Trajectories for Illegal Fishing Detection
复制标题
用于非法捕鱼检测的矿船轨迹
DOI：
10.1109/bigdata47090.2019.9006545
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
H. Wehn
中科院分区：
文献类型：
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作者：
A. Shahir;M. A. Tayebi;U. Glässer;Tilemachos Charalampous;Zahra Zohrevand;H. Wehn
在本文中，我们提出了一种数据驱动的方法，用于检测和跟踪来自船只跟踪服务的大量海洋交通数据集中的黑暗捕鱼。暗捕捞是指船只试图隐藏与各种形式的非法捕捞有关的非法活动的秘密捕捞作业，这是对世界渔业和全世界鱼类种群以及全球粮食安全最严重的威胁之一。我们的方法建立在对渔船进行分析和排名的基础上，通过分析渔船在较长时间内的日常运营，以发现与暗捕捞相关的异常活动模式。重点是呈现为具有定义的起点和终点（例如港口和已知锚地位置）的轨迹的船舶运动模式。具体来说，我们分析了渔具在水中的捕鱼模式在船舶报告的行程数据中被掩盖的场景。我们使用北美沿海水域渔船轨迹的大型数据集进行的实验评估表明，无论船舶类型如何，所提出的方法在区分可疑渔船和正常渔船方面的有效性和效率。
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.