AIS Data Analytics for Intelligent Maritime Surveillance Systems

Fu, Xiuju; Xiao, Zhe; Xu, Haiyan; Jayaraman, Vasundhara; Othman, Nasri Bin; Chua, Chye Poh; Lind, Mikael

doi:10.1007/978-3-030-50892-0_23

Cited by 8 publications

(4 citation statements)

References 48 publications

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“…In general, there is a certain risk in dividing the trajectory according to the anchorage state, and there will be a problem that the time interval between two trajectory points is too large. In addition, the complexity of AIS in different regions is different, the frequency of AIS transmission is inconsistent, and the time interval of receiving data is inconsistent [38] which makes it impossible to use it uniformly. Therefore, this paper adopts the trajectory extraction method based on time and sailing speed and the missing value completion method.…”

Section: Time and Speed Based Trajectory Extractionmentioning

confidence: 99%

Improved sequence-to-sequence ship trajectory prediction based on AIS

Yuan,

Fang,

Zhang

et al. 2024

Preprint

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Due to the high density of ships and the complex traffic environment in coastal areas, developing ship trajectory prediction methods is an imperative task for effective collision avoidance. The performance of most previous methods is limited by the issues of regional discrepancy of Automatic Identification System (AIS) data and singularity in trajectory feature extraction. To address these two issues, this paper proposes an improved ship trajectory prediction framework based on AIS. The proposed framework mainly consists of two parts: (1) A trajectory data preprocessing module including the extraction of trajectories based on time and ship speed for imputing missing values based on which AIS data from different regions is normalized to keep the time interval consistency. (2) A sequence-to-sequence ship trajectory prediction model based on double CNNs called DCNN. The encoder of DCNN is composed of two parallel structures, namely the global temporal convolution and local temporal convolution, to extract both long-term and short-term dependencies of the ship trajectories. The decoder is composed of RNN and its variants which are used to generate the predicted ship trajectory sequence. The experimental results show that the proposed framework can effectively predict the ship's trajectory, so as to predict the ship encounter in different regional traffic modes in advance, and help the ship to actively avoid collision.

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Section: Time and Speed Based Trajectory Extractionmentioning

confidence: 99%

Improved sequence-to-sequence ship trajectory prediction based on AIS

Yuan,

Fang,

Zhang

et al. 2024

Preprint

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“…Specific applications may allow some imprecision based on their requirements. AIS datasets have long been used for maritime density maps and researchers have identified some of the underlying difficulties affecting the data fitness [19][20][21][22][23][24].…”

Section: Data Fitnessmentioning

confidence: 99%

The Big Picture: An Improved Method for Mapping Shipping Activities

Troupiotis-Kapeliaris,

Zissis,

Bereta

et al. 2023

Remote Sensing

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Density maps support a bird’s eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other factors, evidently lead to erroneous interpretations and misleading visualizations. In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS). The complete framework involves a number of computational steps for (1) cleaning and filtering AIS data, (2) improving the quality of the input dataset (through trajectory reconstruction and satellite image analysis) and (3) computing and visualizing the subsequent vessel traffic as density maps. The framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets. Real-world data are used to demonstrate the effectiveness of our framework. These experiments show that our trajectory reconstruction method results in significant improvements up to 15% and 26% for temporal gaps of 3–6 and 6–24 h, respectively, in comparison to the baseline methodology. Additionally, a use case in European waters highlights our capability of detecting “dark vessels”, i.e., vessel positions not present in the AIS data.

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“…A closely relevant example is CM in aviation, where it is found that collected raw data do not provide any additional information [37]. It is reported that less power is required for pre-processing than transmitting the raw data, often in the form of lossless compression, and the output of the analysis is suitable for further processing [17].…”

Section: Immediate Processing and Actuation Without Transmissionmentioning

confidence: 99%

“…Several barriers are often recognised including trust in the technology [8], data transfer to shore [31], Internet usage, communications costs and availability [1]. However, the growth of digital intelligence applications worldwide has increased awareness and demand in the maritime industry for collaboration and data-sharing to decrease inefficiencies and redundancy [17,34], increasing the transmission volume. Considering the cost of real-time transmission for larger volumes of data, more inclusive efforts towards reducing unnecessary information are needed [20,47].…”

Section: Introductionmentioning

confidence: 99%

Vibration Edge Computing in Maritime IoT

Michala

Vourganas

Coraddu

2021

ACM Trans. Internet Things

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IoT and the Cloud are among the most disruptive changes in the way we use data today. These changes have not significantly influenced practices in condition monitoring for shipping. This is partly due to the cost of continuous data transmission. Several vessels are already equipped with a network of sensors. However, continuous monitoring is often not utilised and onshore visibility is obscured. Edge computing is a promising solution but there is a challenge sustaining the required accuracy for predictive maintenance. We investigate the use of IoT systems and Edge computing, evaluating the impact of the proposed solution on the decision making process. Data from a sensor and the NASA-IMS open repository were used to show the effectiveness of the proposed system and to evaluate it in a realistic maritime application. The results demonstrate our real-time dynamic intelligent reduction of transmitted data volume by without sacrificing specificity or sensitivity in decision making. The output of the Decision Support System fully corresponds to the monitored system's actual operating condition and the output when the raw data are used instead. The results demonstrate that the proposed more efficient approach is just as effective for the decision making process.

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AIS Data Analytics for Intelligent Maritime Surveillance Systems

Cited by 8 publications

References 48 publications

Improved sequence-to-sequence ship trajectory prediction based on AIS

Improved sequence-to-sequence ship trajectory prediction based on AIS

The Big Picture: An Improved Method for Mapping Shipping Activities

Vibration Edge Computing in Maritime IoT

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