Mining Vessel Tracking Data for Maritime Domain Applications

Alessandrini, Alfredo; Alvarez, Marlene; Greidanus, H.; Vincenzo, Gammieri; Arguedas, Virginia Fernández; Mazzarella, Fabio; Santamaría, Carlos; Stasolla, Mattia; Tarchi, D.; Vespe, Michele

doi:10.1109/icdmw.2016.0058

Cited by 20 publications

(12 citation statements)

References 9 publications

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“…Another significant portion of Set 1' focuses on technical aspects of maritime surveillance, illustrating the constant progress of maritime surveillance systems and the potential for improvement in terms of equipment [55][56][57], data acquisition and fusion [56,58,59], preprocessing and exploitation [60][61][62]. A large share of these papers focusses on event and activity recognition based on trajectory analysis [63,64], sometimes relying on artificial intelligence (deep-learning, data mining) [65,66]. A minor but non-negligible share of papers focuses on uses of maritime surveillance data to serve environmental objectives, either in realtime to ensure compliance with environmental regulations and detect offenders [67][68][69], or in delayed-time to assess impacts on ecosystems and marine and atmospheric pollutions [46,70,71].…”

Section: Discussionmentioning

confidence: 99%

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

et al. 2020

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Maritime Spatial Planning (MSP) has become of key interest in many countries around the world in the last decade, leading to an increased need to inform the historical spatial and temporal footprint of maritime activities. If this knowledge was still recently considered a blind spot, recent developments of maritime surveillance systems (e.g. AIS, VTS, VMS) have allowed to fill some of these gaps by generating significant amounts of spatial data on ships at sea. In this paper, the use of these maritime surveillance data for planning purposes is explored through the lens of the international scientific literature. A first set of 2030 articles dealing with maritime surveillance data and collected through the Web of Science collection was explored to determine the type of data used, the maritime activities addressed and the main objective of each paper (technical developments, safety and security, environmental protection, MSP). This allowed to highlight the growing interest on maritime surveillance data in the scientific literature over the past decades, predominantly towards AIS data and shipping. Over the 2030 papers, only 63 dealt specifically with MSP. These were extracted and explored, allowing to highlight the potential of these data to feed in MSP processes, with a specific focus on fisheries. Nevertheless, the description of actual uses of surveillance-based information within MSP processes remained particularly rare. If this can be seen as a result of a science-policy delay, we suggest that it is mostly related to issues of accessibility, acceptance by economic stakeholders and appropriation by decision makers.

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Section: Discussionmentioning

confidence: 99%

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

et al. 2020

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“…The proposed algorithm can be used by decision makers at different stages of maritime awareness and port security evaluation. The authors in Alessandrini et al (2016) demonstrate how different methodologies, such as data mining, information fusion and visual analytics, enable the automatic detection of structured anomalies, understanding of activities at sea and the analysis of their trends over time. This kind of knowledge can provide new possibilities for improving the safety of navigation.…”

Section: Related Workmentioning

confidence: 99%

Integration of a Self-Organizing Map and a Virtual Pheromone for Real-Time Abnormal Movement Detection in Marine Traffic

Venskus¹,

Treigys²,

Bernatavičienė³

et al. 2017

Informatica

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Abstract. In recent years, the growth of marine traffic in ports and their surroundings raise the traffic and security control problems and increase the workload for traffic control operators. The automated identification system of vessel movement generates huge amounts of data that need to be analysed to make the proper decision. Thus, rapid self-learning algorithms for the decision support system have to be developed to detect the abnormal vessel movement in intense marine traffic areas. The paper presents a new self-learning adaptive classification algorithm based on the combination of a self-organizing map (SOM) and a virtual pheromone for abnormal vessel movement detection in maritime traffic. To improve the quality of classification results, Mexican hat neighbourhood function has been used as a SOM neighbourhood function. To estimate the classification results of the proposed algorithm, an experimental investigation has been performed using the real data set, provided by the Klaipėda seaport and that obtained from the automated identification system. The results of the research show that the proposed algorithm provides rapid self-learning characteristics and classification.

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“…On the other hand, [1] shows how Big Data can be used to gain a better understanding of maritime activities, which is especially useful in remote areas such as the Arctic where shipping activity needs to be monitored to ensure sustainability and the information is otherwise difficult to access. It also discusses anomaly detection, such as detecting when a vessel deviates from the declared path, falsifies its AIS reports or turns off its AIS transponder to potentially engage in illegal activities.…”

Section: Safety Improvement / Unapređenje Sigurnostimentioning

confidence: 99%

“…The last received AIS message is represented by the blue star and the destination port is represented by the green star. Accurate prediction of routes can be used to better estimate times of arrival in ports [1].…”

Section: Logistics Optimization / Optimizacija Logistikementioning

confidence: 99%

Veliki skupovi podataka u pomorskoj industriji

Mirović¹,

Miličević²,

Obradovic³

2018

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SummaryMaritime industry is a complex system that requires a quick adaptation to changing conditions and in which decision-making needs to take into account a large number of parameters. As navigation systems become more advanced, there is a significant amount of ship performance and navigation data generated. Big Data analytics tools make it possible to analyze these large quantities of data in order to gain the insight that supports decision-making. This paper gives an overview of the applications of Big Data in maritime industry, specifically in logistics optimization, safety and energy efficiency improvement, as well as the challenges that systems involving Big Data face. SažetakPomorska industrija je složeni sustav koji zahtijeva brzu prilagodbu u promjenjivim uvjetima u kojima je potrebno uzeti u obzir velik broj parametara prilikom donošenja odluka. Napretkom navigacijskih sustava, generira se znatna količina podataka o performansama broda i navigaciji. Analitički alati za velike skupove podataka omogućuju analizu tih podataka kako bi se dobilo razumijevanje potrebno za podršku donošenju odlukâ. Ovaj članak daje pregled primjene velikih skupova podataka u pomorstvu, posebno u optimizaciji logistike, sigurnosti i poboljšanju energetske učinkovitosti, kao i izazove s kojima se suočavaju sustavi koji koriste velike skupove podataka. KEY

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Mining Vessel Tracking Data for Maritime Domain Applications

Cited by 20 publications

References 9 publications

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

Exploring uses of maritime surveillance data for marine spatial planning: A review of scientific literature

Integration of a Self-Organizing Map and a Virtual Pheromone for Real-Time Abnormal Movement Detection in Marine Traffic

Veliki skupovi podataka u pomorskoj industriji

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