Automatic Identification System based Fishing Trajectory Data Preprocessing Method using Map Reduce

Many countries use vessel monitoring system (VMS) data to monitor their fishery activities. However, VMS data is limited in terms of distinguishing operations involving illegal fishing gear. Recently introduced automatic identification system (AIS) data is advantageous for tracking fishing ship behaviors.AIS data include various types of information about a ship, such as its state of navigation and its broadcast rate on the radio channel. We interpolate AIS trajectory data with a regular time interval and extract the ship velocity and course change data for fishing ship gear classification. To simplify and condense the data, the course change index (CCI) and ship speed index (SSI) are applied to the ship velocity and course data. The proposed mapper combines CCIs and SSIs into key words, while the proposed reducer collects fishing ship gear type values that are of the same key.By using the proposed key-value dataset from the MapReduce procedure, we can classify fishing gear type. We evaluated the performance of the proposed model by using a test dataset. The results showed that the proposed model achieved 76.2% accuracy in the classification of fishing ship trajectories against the test dataset.

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Convolutional Neural Network-Based Gear Type Identification from Automatic Identification System Trajectory Data

Applied Sciences ◽

10.3390/app10114010 ◽

2020 ◽

Vol 10 (11) ◽

pp. 4010 ◽

Cited By ~ 3

Author(s):

Kwang-il Kim ◽

Keon Myung Lee

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Automatic Identification ◽

Identification System ◽

Fishing Gear ◽

Trajectory Data ◽

Data Set ◽

Prediction Module ◽

Fishing Gears ◽

Fully Connected

Marine resources are valuable assets to be protected from illegal, unreported, and unregulated (IUU) fishing and overfishing. IUU and overfishing detections require the identification of fishing gears for the fishing ships in operation. This paper is concerned with automatically identifying fishing gears from AIS (automatic identification system)-based trajectory data of fishing ships. It proposes a deep learning-based fishing gear-type identification method in which the six fishing gear type groups are identified from AIS-based ship movement data and environmental data. The proposed method conducts preprocessing to handle different lengths of messaging intervals, missing messages, and contaminated messages for the trajectory data. For capturing complicated dynamic patterns in trajectories of fishing gear types, a sliding window-based data slicing method is used to generate the training data set. The proposed method uses a CNN (convolutional neural network)-based deep neural network model which consists of the feature extraction module and the prediction module. The feature extraction module contains two CNN submodules followed by a fully connected network. The prediction module is a fully connected network which suggests a putative fishing gear type for the features extracted by the feature extraction module from input trajectory data. The proposed CNN-based model has been trained and tested with a real trajectory data set of 1380 fishing ships collected over a year. A new performance index, DPI (total performance of the day-wise performance index) is proposed to compare the performance of gear type identification techniques. To compare the performance of the proposed model, SVM (support vector machine)-based models have been also developed. In the experiments, the trained CNN-based model showed 0.963 DPI, while the SVM models showed 0.814 DPI on average for the 24-h window. The high value of the DPI index indicates that the trained model is good at identifying the types of fishing gears.

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Massive Automatic Identification System Sensor Trajectory Data-Based Multi-Layer Linkage Network Dynamics of Maritime Transport along 21st-Century Maritime Silk Road

Sensors ◽

10.3390/s19194197 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4197 ◽

Cited By ~ 3

Author(s):

Hongchu Yu ◽

Zhixiang Fang ◽

Feng Lu ◽

Alan T. Murray ◽

Zhiyuan Zhao ◽

...

Keyword(s):

Traffic Flow ◽

21St Century ◽

Network Structure ◽

Network Dynamics ◽

Silk Road ◽

Automatic Identification ◽

Identification System ◽

Trajectory Data ◽

Automatic Identification System ◽

Maritime Silk Road

Automatic Identification System (AIS) data could support ship movement analysis, and maritime network construction and dynamic analysis. This study examines the global maritime network dynamics from multi-layers (bulk, container, and tanker) and multidimensional (e.g., point, link, and network) structure perspectives. A spatial-temporal framework is introduced to construct and analyze the global maritime transportation network dynamics by means of big trajectory data. Transport capacity and stability are exploited to infer spatial-temporal dynamics of system nodes and links. Maritime network structure changes and traffic flow dynamics grouping are then possible to extract. This enables the global maritime network between 2013 and 2016 to be investigated, and the differences between the countries along the 21st-century Maritime Silk Road and other countries, as well as the differences between before and after included by 21st-century Maritime Silk Road to be revealed. Study results indicate that certain countries, such as China, Singapore, Republic of Korea, Australia, and United Arab Emirates, build new corresponding shipping relationships with some ports of countries along the Silk Road and these new linkages carry significant traffic flow. The shipping dynamics exhibit interesting geographical and spatial variations. This study is meaningful to policy formulation, such as cooperation and reorientation among international ports, evaluating the adaptability of a changing traffic flow and navigation environment, and integration of the maritime economy and transportation systems.

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Inland Ship Trajectory Restoration by Recurrent Neural Network

Journal of Navigation ◽

10.1017/s0373463319000316 ◽

2019 ◽

Vol 72 (06) ◽

pp. 1359-1377 ◽

Cited By ~ 4

Author(s):

Cheng Zhong ◽

Zhonglian Jiang ◽

Xiumin Chu ◽

Lei Liu

Keyword(s):

Neural Networks ◽

Situational Awareness ◽

Short Term Memory ◽

Linear Method ◽

Automatic Identification ◽

Identification System ◽

Trajectory Data ◽

Proposed Model ◽

Data Restoration ◽

Complicated Geometry

The quality of Automatic Identification System (AIS) data is of fundamental importance for maritime situational awareness and navigation risk assessment. To improve operational efficiency, a deep learning method based on Bi-directional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs) is proposed and applied in AIS trajectory data restoration. Case studies have been conducted in two distinct reaches of the Yangtze River and the capability of the proposed method has been evaluated. Comparisons have been made between the BLSTM-RNNs-based method and the linear method and classic Artificial Neural Networks. Satisfactory results have been obtained by all methods in straight waterways while the BLSTM-RNNs-based method is superior in meandering waterways. Owing to the bi-directional prediction nature of the proposed method, ship trajectory restoration is favourable for complicated geometry and multiple missing points cases. The residual error of the proposed model is computed through Euclidean distance which decreases to an order of 10 m. It is considered that the present study could provide an alternative method for improving AIS data quality, thus ensuring its completeness and reliability.

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Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning

Journal of Marine Science and Engineering ◽

10.3390/jmse9080871 ◽

2021 ◽

Vol 9 (8) ◽

pp. 871

Author(s):

Yongpeng Wang ◽

Daisuke Watanabe ◽

Enna Hirata ◽

Shigeki Toriumi

Keyword(s):

Carbon Dioxide ◽

Real Time ◽

Time Management ◽

Carbon Dioxide Emissions ◽

Fixed Time ◽

Automatic Identification ◽

Identification System ◽

Time Interval ◽

Trajectory Data ◽

Fixed Time Interval

In this study, we propose an effective method using deep learning to strengthen real-time vessel carbon dioxide emission management. We propose a method to predict real-time carbon dioxide emissions of the vessel in three steps: (1) convert the trajectory data of the fixed time interval into a spatial–temporal sequence, (2) apply a long short-term memory (LSTM) model to predict the future trajectory and vessel status data of the vessel, and (3) predict the carbon dioxide emissions. Automatic identification system (AIS) database of a liquefied natural gas (LNG) vessel were selected as the sample and we reconstructed the trajectory data with a fixed time interval using cubic spline interpolation. Applying the interpolated AIS data, the carbon dioxide emissions of the vessel were calculated based on the International Towing Tank Conference (ITTC) recommended procedures. The experimental results are twofold. First, it reveals that vessel emissions are currently underestimated. This study clearly indicates that the actual carbon dioxide emissions are higher than those reported. The finding offers insight into how to accurately measure the emissions of vessels, and hence, better execute a greenhouse gases (GHGs) reduction strategy. Second, the LSTM model has a better trajectory prediction performance than the recurrent neural network (RNN) model. The errors of the trajectory endpoint and carbon dioxide emissions were small, which shows that the LSTM model is suitable for spatial–temporal data prediction with excellent performance. Therefore, this study offers insights to strengthen the real-time management and control of vessel greenhouse gas emissions and handle those in a more efficient way.

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Ship Trajectory Data Compression Algorithms for Automatic Identification System: Comparison and Analysis

Journal of Water Resources and Ocean Science ◽

10.11648/j.wros.20200902.11 ◽

2020 ◽

Vol 9 (2) ◽

pp. 42 ◽

Cited By ~ 1

Author(s):

Le Qi ◽

Yuanyuan Ji

Keyword(s):

Data Compression ◽

Automatic Identification ◽

Identification System ◽

Trajectory Data ◽

Compression Algorithms ◽

Automatic Identification System ◽

Comparison And Analysis

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Interpolation Between AIS Reports: Probabilistic Inferences Over Vessel Path Space

Journal of Navigation ◽

10.1017/s0373463311000208 ◽

2011 ◽

Vol 64 (4) ◽

pp. 595-607 ◽

Cited By ~ 6

Author(s):

D. J. Peters ◽

T. R. Hammond

Keyword(s):

Path Space ◽

Automatic Identification ◽

Identification System ◽

Time Interval ◽

Random Generation ◽

Automatic Identification System ◽

Illegal Dumping ◽

True Path ◽

Probabilistic Queries

We present a method for addressing probabilistic queries about the location of a vessel in the time interval between two position reports, such as from the Automatic Identification System (AIS). The heart of the method is the random generation of physically feasible paths connecting the two reports. The method empowers operators to answer probabilistic questions about any characteristic of the unknown true path. For illustrative purposes, we demonstrate the use of the method to identify which of several vessels is the most likely perpetrator, in a fictitious scenario in which illegal dumping of waste matter has taken place.

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