Spatio-temporal data mining for maritime situational awareness

Arguedas, Virginia Fernández; Mazzarella, Fabio; Vespe, Michele

doi:10.1109/oceans-genova.2015.7271544

Cited by 16 publications

(6 citation statements)

References 5 publications

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“…Despite the claims about point-based analysis, the authors implemented the idea of updating the traffic knowledge from the input of AIS messages and the use of historical knowledge. The same clustering methodology was explored in [11]. Here, the historical spatiotemporal data are analyzed to detect waypoints of routes.…”

Section: Reviewmentioning

confidence: 99%

Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Venskus

Treigys

Bernatavičienė

et al. 2019

Sensors

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The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and time-consuming to detect abnormalities, thus rapid response algorithms should be developed for a decision support system to identify abnormal movements of vessels in areas of heavy traffic. This paper extends the previous study on a self-organizing map application for processing of sensor stream data received by the maritime automated identification system. The more data about the vessel’s movement is registered and submitted to the algorithm, the higher the accuracy of the algorithm should be. However, the task cannot be guaranteed without using an effective retraining strategy with respect to precision and data processing time. In addition, retraining ensures the integration of the latest vessel movement data, which reflects the actual conditions and context. With a view to maintaining the quality of the results of the algorithm, data batching strategies for the neural network retraining to detect anomalies in streaming maritime traffic data were investigated. The effectiveness of strategies in terms of modeling precision and the data processing time were estimated on real sensor data. The obtained results show that the neural network retraining time can be shortened by half while the sensitivity and precision only change slightly.

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Section: Reviewmentioning

confidence: 99%

Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Venskus

Treigys

Bernatavičienė

et al. 2019

Sensors

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“…Its main drawback is the selection of the number of clusters, which, as previously discussed, can be optimized by using an EM algorithm. An example of the maritime data clustering based on K ‐means can be found in Vespe, Pallotta, Visentini, Bryan, and Braca (); recently density‐based spatial clustering of applications with noise (DBSCAN) methods have become very popular for their convenient properties as compared to K ‐means: they are density‐based (which is a convenient property for the maritime data), they do not require to specify the number of clusters, they have the ability to derive arbitrarily shaped clusters and they incorporate by‐product the classification of the noise points; examples of such applications in the maritime domain can be found in Arguedas, Pallotta, and Vespe (), Pallotta and Jousselme (), Pallotta, Vespe, and Bryan () and Pallotta, Vespe, and Bryan (). Liu, De Souza, Hilliard, and Matwin () discusses an “ad‐hoc” similarity metric for trajectory partitioning and segment clustering, which can be seen as a DBSCAN version for trajectory clustering.…”

Section: Methodsmentioning

confidence: 99%

“…This can be an issue when the refresh rates from different sensors can generate intermittent, incomplete and unequal length trajectories. This problem is addressed in the following papers: Etienne, Ray, and McArdle (), Pallotta et al (, , ), de Vries and van Someren () and Arguedas, Mazzarella, and Vespe (). These methods look more suitable for maritime anomaly detection since they enable the sequential classification of track points with a scoring which can be updated as soon as a new vessel report, along with the monitored track, is received. Graph‐based methods are a relatively recent approach in maritime applications.…”

Section: Methodsmentioning

confidence: 99%

“…However, this network‐based representation is useful for descriptive purposes of the shipping activity but provides little room for classification and anomaly detection. A more promising representation based on geographical networks is discussed in Arguedas et al (, ) and Arguedas, Pallotta, and Vespe () where a simplified and structured picture of the maritime activity at sea is derived and some examples of anomaly detection are provided. The strategy presented in these works first derives the points of interest using a density‐based approach (i.e., DBSCAN); then it estimates the average paths between them by using the actual in‐between vessel tracks connecting the points and then derives the intermediate paths (called synthetic routes).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Maritime anomaly detection: A review

Riveiro¹,

Pallotta

Vespe

2018

WIREs Data Min & Knowl

Self Cite

108

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The surveillance of large sea areas normally requires the analysis of large volumes of heterogeneous, multidimensional and dynamic sensor data, in order to improve vessel traffic safety, maritime security and to protect the environment. Early detection of conflict situations at sea provides critical time to take appropriate action with, possibly before potential problems occur. In order to provide an overview of the state‐of‐the‐art of research carried out for the analysis of maritime data for situational awareness, this study presents a review of maritime anomaly detection. The found articles are categorized into four groups (a) data, (b) methods, (c) systems, and (d) user aspects. We present a comprehensive summary of the works found in each category, and finally, outline possible paths of investigation and challenges for maritime anomaly detection. This article is categorized under: Application Areas > Government and Public Sector Algorithmic Development > Spatial and Temporal Data Mining

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“…To provide a more complete and interactive Maritime Situational Picture (MSP) to operational authorities and policy-makers to support the decision-making process, Arguedas et al (2015) have proposed a spatio-temporal data mining framework based on the TREAD approach for route-based anomalous vessel detection. In the proposed framework, the authors used the TREAD approach to build a synthetic representation of all maritime traffic routes; afterward they perform maritime traffic anomaly detection.…”

Section: Review Of Recent Research Work On Automatic Anomalous Maritmentioning

confidence: 99%

Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels

Sidibé

Gao

2017

J. Navigation

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The maritime domain is the most utilised environment for bulk transportation, making maritime safety and security an important concern. A major aspect of maritime safety and security is maritime situational awareness. To achieve effective maritime situational awareness, recently many efforts have been made in automatic anomalous maritime vessel movement behaviour detection based on movement data provided by the Automatic Identification System (AIS). In this paper we present a review of state-of-the-art automatic anomalous maritime vessel behaviour detection techniques based on AIS movement data. First, we categorise some approaches proposed in the period 2011 to 2016 to automatically detect anomalous maritime vessel behaviour into distinct categories including statistical, machine learning and data mining, and provide an overview of them. Then we discuss some issues related to the proposed approaches and identify the trend in automatic detection of anomalous maritime vessel behaviour.

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Spatio-temporal data mining for maritime situational awareness

Cited by 16 publications

References 5 publications

Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding

Maritime anomaly detection: A review

Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels

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