Towards a fixed-gear AIS trajectory differentiation

Mirjam Bayer, Daniyal Kazempour, Peer Kröger
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Abstract

The increasing digital traces of fishing fleets nowadays available allow for automatized observation of the oceans, a vulnerable space which could hardly be monitored or governed previously. Data streams from satellite base communication systems are being used for a variety of applications such as collision avoidance, route optimization, and monitoring of illegal activities. Classification of fishing vessels according to the specific fishing method is a developing branch towards assessing the compliance of fishing regulations. It is not feasible to verify reported fishing efforts, fishing quota or even fishing methods at sea via manual inspections. Classification of the fishing trajectories into trawlers, long-liners, pure-seine, and fixed-gear are well researched. However, the distinction between different fixed-gear fishing methods has not been studied so far. Therefore, this work proposes the vision to distinguish the previously undifferentiated fixed-gear fishing trajectories. We outline our vision, discuss the challenges of exploiting the small differences in the trajectories, and potential approaches towards realizing this vision.
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向定齿轮AIS轨迹分化
如今渔船队越来越多的数字痕迹使得对海洋的自动化观测成为可能,而海洋是一个脆弱的空间,以前几乎无法监测或管理。来自卫星基地通信系统的数据流正被用于各种应用,如避免碰撞、路线优化和监视非法活动。根据特定的捕捞方法对渔船进行分类是评估捕捞法规合规性的一个发展方向。通过人工检查来核实报告的捕捞努力量、捕捞配额甚至海上捕捞方法是不可行的。将捕鱼轨迹分为拖网渔船、长绳渔船、纯围网渔船和固定渔具渔船进行了很好的研究。然而,不同的固定渔具捕鱼方法之间的区别至今尚未得到研究。因此,这项工作提出了区分以前未区分的固定渔具捕捞轨迹的愿景。我们概述了我们的愿景,讨论了利用轨迹上的微小差异所面临的挑战,以及实现这一愿景的潜在方法。
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