A Study of Correlation between Fishing Activity and AIS Data by Deep Learning

http://www.transnav.eu the International Journal Volume 14 on Marine Navigation Number 3 and Safety of Sea Transportation September 2020 DOI: 10.12716/1001.14.03.01 A Study of Correlation between Fishing Activity and AIS Data by Deep Learning K.Y. Shen, Y.J. Chu, S.J. Chang & S.M. Chang National Taiwan Ocean University, Keelung, Taiwan ABSTRACT: Previous researches on the prediction of fishing activities mainly rely on the speed over ground (SOG) as the referential attribute to determine whether the vessel is navigating or in fishing operation. Since more and more fishing vessels install Automatic Identification System (AIS) either voluntarily or under regulatory requirement, data collected from AIS in real time provide more attributes than SOG which may be utilized to improve the prediction. To be specific, the ships' trajectory patterns and the changes in course become available and should be considered. This paper aims to improve the accuracy in the identification of fishing activities. First, we do feature extraction from the AIS data of coastal waters around Taiwan and build a Recurrent Neural Network (RNN) model. Then, the activity data of fishing vessels are divided into fishing and non-fishing. Finally, based on the testing by feeding various fishing activity data, we can identify the fishing status automatically. 1 INTRODUCTION Ever since the 1982 United Nations Convention on the Law of the Sea (UNCLOS) entered into force in 1994, the rights and duties of coastal, port and flag States in respect of principal maritime zones, namely the territorial sea, the exclusive economic zone (EEZ) and the high seas became the bases and driving forces for the rapid changes in the maritime management. Systems or schemes have been introduced to enhance the safety of life at sea, the environment protection, and maritime security. As for the fisheries, besides the provisions of UNCLOS, the rapid depletion of key fish stocks has made it imperative that governments achieve greater control over fishing activities. In order to ensure sustainable fisheries, a mechanism called monitoring, control and surveillance (MCS) was introduced for implementation of agreed policies, plans or strategies for oceans and fisheries management [1]. Fisheries MCS can be defined as follows: − “Monitoring” includes the collection, measurement and analysis of fishing activities including, but not limited to: catch, species composition, fishing effort, by-catch, discards, area of operations, etc. − “Control” involves the specification of the terms and conditions under which resources can be harvested. − “Surveillance” involves the regulation and supervision of fishing activity to ensure the national legislation and terms, conditions of access, and management measures are observed. Flag States have the responsibility to know where their vessels are located. Flag States must also have some means of determining what each of their fishing 527 vessels is catching. Although the data to be reported will vary from fishery to fishery, flag States should require their fishing vessels to report timely, complete and accurate information concerning fishery activities, including: vessel identification, position, course, speed, fishing effort, catch composition, zone entry/exit (including closed areas entry/exit). Flag States should also establish a mechanism to verify the accuracy of reported data and should penalize the failure to report and misreporting of data. For serious offences, such sanctions should include withdrawal or suspension of the vessel’s authorization to fish. The term “illegal, unreported and unregulated fishing” or IUU fishing is used to describe a wide range of irresponsible fishing activities, such as reflagging of fishing vessels to evade controls, fishing in areas without authorization, failure to report or misreporting catches. Such activities undermine efforts to manage marine fisheries properly and impedes progress toward the goal of sustainable fisheries. Apparently, automatic detection and identification of fishing activities is essential to effective fishery MCS and sustainable fishery. This is the focus and main purpose of the work presented in this paper. It is envisioned that development of such functionality can further contribute to maritime spatial planning as well as maritime safety and security. One of the most efficient and cost-effective tool for fisheries MCS is Vessel Monitoring System (VMS). Over the past 20 years, a growing number of States have introduced VMS requirements for their fishing vessels or as a condition of access for foreign vessels to fish in waters under their jurisdiction. Most international agreements adopted by regional fisheries management organizations (RFMOs) also require VMS. In the early days of fishing activity detection, most researchers use data collected by VMS to predict when the vessels is in fishing operation. VMS mainly relies on satellite-based automatic location communicators, including Inmarsat-C, ARGOS, and Iridium, and the position report interval is usually set at 1 hour for coastal monitoring due to the cost. complicated fishing method, in [9] a novel approach is proposed for identifying fishing activity using the Conditional Random Fields. In [10], deep learning is used with auto-encoders to automatically find fishing features. However, the research in [10] is using S-AIS data to detect fishing activity of distant water fishing. So far in the literature, to the author’s knowledge, none of the AIS-based fishing activity detection is for small and medium-sized fishing vessels on coastal waters. To detect fishing activity and improve identification performance, we implement an identification methodology based on deep learning. Key features of fishing are created in advance and a multi-layered bidirectional long short term memory model is built to predict three types of fishing activities, namely trawling, trolling, and longline fishing, on coastal waters around Taiwan. This paper is organized as follows. Section II introduces terminologies used throughout this paper. Section III describes the data preprocessing and reports the results of the experiments. Conclusions are then presented in Section IV. 2 BACKGROUND OF METHODS 2.1 Recurrent Neural Network Recurrent Neural Network (RNN) is a well-known model to deal with sequential data. The structure of a simple RNN, illustrated in Fig. 1, has feedback loops which let model maintain memory over time. This means input has not only the result of the previous hidden layer, but also the value predicted at the previous time. An RNN can be described mathematically as follows. Given a sequence of feature vector X T = { x1 , x2 , … , xT }. An RNN with a hidden vector sequence H T = { h1 , h2 , … , hT } and output vector sequence YT = { y1 , y2 , …, yT } is calculated as follows: ht= σ h (W1 xt + Wh ht −1 + b1 ) (1) The vessel’s speed is used as a threshold to judge the behavior [2,3,4 (...truncated)