Fishing Vessels Behavior Identification for Combating IUU Fishing: Enable Traceability at Sea

Overfishing is a critical catastrophe to the ecosystem and the global food chain. The leading causes are Illegal Unreported and Unregulated Fishing (IUU Fishing) linked to illegal labor. EU and the US have set up the fisheries policy that emphasis on traceability. The traceability principle is to monitor the entire seafood supply chain (Sea to Table). FAO’s technology gap analysis reveals that there is a lack of reliable and affordable automated systems or a lack of links to traceability. The challenge of traceability is tracing back to the catch source with existing data and technology. This study aims at the novel concept of a combination of global and local features of trajectory data for fishing vessel behavior identification and enabling seafood transparency. We present a new technique on a local feature of time series and transform the trajectory pattern to global features for Deep Learning. We apply this technique to AIS and VMS data of Thai fishing vessels (Surrounding Nets, Trawl, Longliner, and Reefer). Fishing vessel behaviors were classified as Fishing, Non-fishing, and Transshipment. Our proposed method gives a robust average accuracy result (97.50%). This concept could solve the IUU Fishing and enable traceability at sea, including monitoring, maritime, and marine resources conservation systems.

[1]  Lynn Andrea Stein.  Casting a Wider Net , 2012, Science.

[2]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[3]  Lei Chen,et al.  On The Marriage of Lp-norms and Edit Distance , 2004, VLDB.

[4]  Zhihai Wang,et al.  A large margin time series nearest neighbour classification under locally weighted time warps , 2018, Knowledge and Information Systems.

[5]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[6]  Houshang Darabi,et al.  Insights Into LSTM Fully Convolutional Networks for Time Series Classification , 2019, IEEE Access.

[7]  Chenghu Zhou,et al.  Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement , 2017, ISPRS Int. J. Geo Inf..

[8]  Xianfu Chen,et al.  Deep Learning with Long Short-Term Memory for Time Series Prediction , 2018, IEEE Communications Magazine.

[9]  Wang-Chien Lee,et al.  Mining geographic-temporal-semantic patterns in trajectories for location prediction , 2013, ACM Trans. Intell. Syst. Technol..

[11]  Francesco Gullo,et al.  From Patterns in Data to Knowledge Discovery: What Data Mining Can Do☆ , 2015 .

[12]  Robert Weibel,et al.  Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects , 2009, Comput. Environ. Urban Syst..

[13]  Maike Buchin,et al.  Computing similarity of coarse and irregular trajectories using space-time prisms , 2013, SIGSPATIAL/GIS.

[14]  Wesley W. Chu,et al.  Efficient processing of similarity search under time warping in sequence databases: an index-based approach , 2004, Inf. Syst..

[15]  D. McLean,et al.  trajr: An R package for characterisation of animal trajectories , 2018 .

[16]  D. Kroodsma,et al.  Identifying Global Patterns of Transshipment Behavior , 2018, Front. Mar. Sci..

[17]  Ronan Fablet,et al.  Fishing Gear Identification From Vessel-Monitoring-System-Based Fishing Vessel Trajectories , 2018, IEEE Journal of Oceanic Engineering.

[18]  Xun Li.  Using Complexity Measures of Movement for Automatically Detecting Movement Types of Unknown GPS Trajectories , 2014 .

[19]  Ulf Geir Indahl,et al.  Deriving Animal Movement Behaviors Using Movement Parameters Extracted from Location Data , 2018, ISPRS Int. J. Geo Inf..

[20]  Pablo Montero,et al.  TSclust: An R Package for Time Series Clustering , 2014 .

[21]  Fabio Mazzarella,et al.  Discovering vessel activities at sea using AIS data: Mapping of fishing footprints , 2014, 17th International Conference on Information Fusion (FUSION).

[22]  Ross Purves,et al.  How fast is a cow? Cross‐Scale Analysis of Movement Data , 2011, Trans. GIS.

[23]  Yonggang Wen,et al.  Power Series Classification: A Hybrid of LSTM and a Novel Advancing Dynamic Time Warping , 2016, ArXiv.

[24]  Samantha H. Cheng,et al.  Delivering on seafood traceability under the new U.S. import monitoring program , 2018, Ambio.

[25]  Simon Jennings,et al.  Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data , 2010 .

[26]  Lily Rachmawati,et al.  An Automatic Identification System (AIS) Database for Maritime Trajectory Prediction and Data Mining , 2016, ArXiv.

[27]  Michel J. Kaiser,et al.  A comparison of VMS and AIS data: the effect of data coverage and vessel position recording frequency on estimates of fishing footprints , 2018 .

[28]  Nicolas Longépé,et al.  Completing fishing monitoring with spaceborne Vessel Detection System (VDS) and Automatic Identification System (AIS) to assess illegal fishing in Indonesia. , 2018, Marine pollution bulletin.

[29]  Zheng Zhang,et al.  Dynamic Time Warping under limited warping path length , 2017, Inf. Sci..

[30]  Toshiyuki Yamamoto,et al.  Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines , 2015 .

[31]  William Rucklidge,et al.  Efficiently Locating Objects Using the Hausdorff Distance , 1997, International Journal of Computer Vision.

[32]  Nima Hatami,et al.  Classification of time-series images using deep convolutional neural networks , 2017, International Conference on Machine Vision.

[33]  Allen Cheung,et al.  Animal navigation: the difficulty of moving in a straight line , 2007, Biological Cybernetics.

[34]  G. Sylvia,et al.  Seafood Traceability in the United States: Current Trends, System Design, and Potential Applications. , 2005, Comprehensive reviews in food science and food safety.

[35]  Robert Weibel,et al.  Movement similarity assessment using symbolic representation of trajectories , 2012, Int. J. Geogr. Inf. Sci..

[36]  Adam Switonski,et al.  Dynamic time warping in classification and selection of motion capture data , 2018, Multidimensional Systems and Signal Processing.

[37]  Robert B McMaster,et al.  A Statistical Analysis of Mathematical Measures for Linear Simplification , 1986 .

[38]  S. Benhamou.  How to reliably estimate the tortuosity of an animal's path: straightness, sinuosity, or fractal dimension? , 2004, Journal of theoretical biology.

[39]  Willem Bouten,et al.  Riding the tide: intriguing observations of gulls resting at sea during breeding , 2011 .

[40]  T. Pitcher,et al.  Estimating the Worldwide Extent of Illegal Fishing , 2009, PloS one.

[41]  Itai Cohen,et al.  Walking like an ant: a quantitative and experimental approach to understanding locomotor mimicry in the jumping spider Myrmarachne formicaria , 2017, Proceedings of the Royal Society B: Biological Sciences.

[42]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[43]  T. Pitcher,et al.  Estimates of illegal and unreported fish in seafood imports to the USA , 2014 .

[44]  Fabrizio Natale,et al.  Mapping Fishing Effort through AIS Data , 2015, PloS one.

[45]  Stan Matwin,et al.  Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning , 2016, PloS one.

[46]  Sophie Bertrand,et al.  Lévy trajectories of Peruvian purse-seiners as an indicator of the spatial distribution of anchovy ( Engraulis ringens ) , 2005 .

[47]  Robert Weibel,et al.  Towards a taxonomy of movement patterns , 2008, Inf. Vis..

[48]  Tieniu Tan,et al.  Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[49]  A. Leroy,et al.  The EU restrictive trade measures against IUU fishing , 2016 .

[50]  Mao Ye,et al.  Mining GPS Data for Trajectory Recommendation , 2014, PAKDD.

[51]  Hui Xiong,et al.  Introduction to special section on intelligent mobile knowledge discovery and management systems , 2013, ACM Trans. Intell. Syst. Technol..

[52]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.

[53]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[54]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[55]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[56]  Bogdan Gabrys,et al.  Comparing and Combining Time Series Trajectories Using Dynamic Time Warping , 2016, KES.

[57]  Andrea Belardinelli,et al.  Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities , 2016 .

[58]  Pang-Ning Tan,et al.  An Integrated Framework for Simultaneous Classification and Regression of Time-Series Data , 2010, SDM.

[59]  M. Boyle,et al.  The Expanding Role of Traceability in Seafood: Tools and Key Initiatives , 2017, Journal of food science.

[60]  Cyrus Shahabi,et al.  Robust Time-Referenced Segmentation of Moving Object Trajectories , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[61]  E. Fulton,et al.  How to Sustain Fisheries: Expert Knowledge from 34 Nations , 2019, Water.

[62]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[63]  S. J. Cripps,et al.  Defining and estimating global marine fisheries bycatch , 2009 .

[64]  A. Boza,et al.  Traceability in the Food Supply Chain: Review of the literature from a technological perspective , 2018 .

[65]  A. Haynie,et al.  Using Vessel Monitoring System Data to Identify and Characterize Trips Made by Fishing Vessels in the United States North Pacific , 2016, PloS one.

[66]  M. Imafuku,et al.  Behavioural mimicry in flight path of Batesian intraspecific polymorphic butterfly Papilio polytes , 2015, Proceedings of the Royal Society B: Biological Sciences.

[67]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[68]  José Antonio Vilar,et al.  Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study , 2010, J. Classif..

[69]  Eamonn J. Keogh,et al.  Derivative Dynamic Time Warping , 2001, SDM.

[70]  Daniel C. Dunn,et al.  Empowering high seas governance with satellite vessel tracking data , 2018 .

[71]  Claire M Postlethwaite,et al.  A new multi-scale measure for analysing animal movement data. , 2013, Journal of theoretical biology.