Mining Vessel Trajectories for Illegal Fishing Detection

Shahir, Amir Yaghoubi; Tayebi, Mohammad A.; Glässer, Uwe; Charalampous, Tilemachos; Zohrevand, Zahra; Wehn, Hans

doi:10.1109/bigdata47090.2019.9006545

Cited by 14 publications

(7 citation statements)

References 14 publications

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“…For that, the signal strength to the base station is used to create a probability map. Also, in [10], Shahir et al draw a probability distributions for suspected dark fishing. In [11], Kontopoulos et al identify AIS switch-off in near real-time by analysing large streams of AIS messages received from terrestrial base stations.…”

Section: A Ais Shutdown Detectionmentioning

confidence: 99%

Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)

Bernabé

Spieker

Legeard

et al. 2021

AAAI

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Maritime surveillance is essential to avoid illegal activities and for environmental protection. However, the unlabeled, noisy, irregular time-series data and the large area to be covered make it challenging to detect illegal activities. Existing solutions focus only on trajectory reconstruction and probabilistic models that do ignore the context, such as the neighboring vessels. We propose a novel representation learning method that considers both temporal and spatial contexts learned in a self-supervised manner, using a selection of pretext tasks that do not require to be labeled manually. The underlying model encodes the representation of maritime vessel data compactly and effectively. This generic encoder can then be used as input for more complex tasks lacking labeled data.

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Section: A Ais Shutdown Detectionmentioning

confidence: 99%

Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)

Bernabé

Spieker

Legeard

et al. 2021

AAAI

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“…Given the critical consequences of IUU fishing, and the lack of resources to reliably identify such activities, some works have started to develop AI-based systems to detect illegal fishing practices [6][7][8][9][10]. These works mainly focus on detecting IUU fishing through the identification of fishing vessels based on tracking data.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal trajectory data modeling for fishing gear classification

Rodriguez-Albala,

Peña,

Melzi

et al. 2024

Pattern Anal Applic

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International Organizations urge the protection of our oceans and their ecosystems due to their immeasurable importance to humankind. Since illegal fishing activities, commonly known as IUU fishing, cause irreparable damage to these ecosystems, concerned organisms are pushing to detect and combat IUU fishing practices. The automatic identification system allows to locate the position and trajectory of fishing vessels. In this study we address the task of detecting vessels’ fishing gears based on the trajectory behavior defined by GPS position data, a useful task to prevent the proliferation of IUU fishing practices. We present a new database including trajectories that span 7 different fishing gears and analyze these as in a time sequence analysis problem. We leverage from feature extraction techniques from the online signature verification domain to model vessel trajectories, and extract relevant information in the form of both local and global feature sets. We show how, based on these sets of features, the kinematics of vessels according to different fishing gears can be effectively classified using common supervised learning algorithms with accuracies up to $$90\%$$ 90 % . Furthermore, motivated by the concerns raised by several organizations on the adverse impact of bottom trawling on marine biodiversity, we present a binary classification experiment in which we were able to distinguish this kind of fishing gear with an accuracy of $$99\%$$ 99 % . We also illustrate in an ablation study the relevance of factors such as data availability and the sampling period to perform fishing gear classification. Compared to existing works, we highlight these factors, especially the importance of using sampling periods in the order of minutes instead of hours.

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“…As for shipping behavior analysis based on AIS data, Zhou et al [17] proposed a new methodology to achieve ship classification, which consists of distinguishing behavior clusters and classifying ships. Shahir et al [18] proposed an approach to detect dark fishing by profiling and ranking fishing vessels. To learn fishing vessel routine activity patterns, Shahir et al [19] further combined cluster methods with Hidden Markov Models to differentiate fishing trip types.…”

Section: Introductionmentioning

confidence: 99%

“…Fu et al [32] proposed that anomalous identification could be carried out according to the spatiotemporal correlation of the data. In addition, Shahir et al [18] used AIS data to analyze dark periods in ship trajectories to detect illegal fishing. Based on the previous studies and research, the following issues exist in the current research: the characteristics and similarities of ship trajectory have not been fully explored; most of the studies focus on ship trajectory in open water, where the trajectory characteristics are simple, and the number of trajectories is small, and the results of the cluster analysis lack verification; the clustering analysis of ship trajectory is not realized in various routes/voyages.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid-Clustering Model of Ship Trajectories for Maritime Traffic Patterns Analysis in Port Area

Lei

Zhang

et al. 2022

JMSE

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A hybrid-clustering model is presented for the probabilistic characterization of ship traffic and anomaly detection. A hybrid clustering model was proposed to increase the efficiency of trajectory clustering in the port area and analyze the maritime traffic patterns in port. The model identified dissimilarities between trajectories based on characteristics, using K-Means and the density-based spatial clustering of applications with noise algorithm (DBSCAN). Firstly, the ship’s trajectory characteristics are constructed based on real ship trajectories considering static characteristics and dynamic characteristics of ship trajectories to calculate the characteristic dissimilarity between trajectories. Simultaneously, the spatial dissimilarity could be quantified using the Hausdorff algorithm. Then, the ship trajectory is clustered initially based on the departure and destination characteristics using K-Means algorithms to obtain various sub-trajectories. However, there are still different types of trajectories in each sub-trajectory. Thus, the DBSCAN algorithm is adopted to cluster the sub-trajectory based on the analysis of the different trajectory characteristics. Finally, the proposed model is applied to the characterization of the Zhanjiang Port, and the results show that the hybrid-clustering method can effectively cluster ship trajectory and present probabilistic characterization of ship traffic and anomaly detection. This lays a solid theoretical foundation for the supervision and risk control of intelligent ships.

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Mining Vessel Trajectories for Illegal Fishing Detection

Cited by 14 publications

References 14 publications

Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)

Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)

Spatio-temporal trajectory data modeling for fishing gear classification

A Hybrid-Clustering Model of Ship Trajectories for Maritime Traffic Patterns Analysis in Port Area

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