A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network

With a wide use of AIS data in maritime transportation, there is an increasing demand to develop algorithms to efficiently classify a ship’s AIS data into different movements (static, normal navigation and manoeuvring). To achieve this, several studies have been proposed to use labelled features but with the drawback of not being able to effectively extract the details of ship movement information. In addition, a ship movement is in a free space, which is different to a road vehicle’s movement in road grids, making it inconvenient to directly migrate the methods for GPS data classification into AIS data. To deal with these problems, a Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) algorithm is proposed in this paper. The underlying concept of this method is to train a neural network to learn from the labelled AIS data, and the unlabelled AIS data can be effectively classified by using this trained network. More specifically, a Ship Movement Image Generation and Labelling (SMIGL) algorithm is first designed to convert a ship’s AIS trajectories into different movement images to make a full use of the CNN’s classification ability. Then, a CNN-SMMC architecture is built with a series of functional layers (convolutional layer, max-pooling layer, dense layer etc.) for ship movement classification with seven experiments been designed to find the optimal parameters for the CNN-SMMC. Considering the imbalanced features of AIS data, three metrics (average accuracy, score and Area Under Curve (AUC)) are selected to evaluate the performance of the CNN-SMMC. Finally, several benchmark classification algorithms (K-Nearest Neighbours (KNN), Support Vector Machine (SVM) and Decision Tree (DT)) are selected to compare with CNN-SMMC. The results demonstrate that the proposed CNN-SMMC has a better performance in the classification of AIS data

Reliable Navigational Scene Perception for Autonomous Ships in Maritime Environment

Due to significant advances in robotics and transportation, research on autonomous ships has attracted considerable attention. The most critical task is to make the ships capable of accurately, reliably, and intelligently detecting their surroundings to achieve high levels of autonomy. Three deep learning-based models are constructed in this thesis to perform complex perceptual tasks such as identifying ships, analysing encounter situations, and recognising water surface objects. In this thesis, sensors, including the Automatic Identification System (AIS) and cameras, provide critical information for scene perception. Specifically, the AIS enables mid-range and long-range detection, assisting the decision-making system to take suitable and decisive action. A Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) is used to detect ships or objects. Following that, a Semi- Supervised Convolutional Encoder-Decoder Network (SCEDN) is developed to classify ship encounter situations and make a collision avoidance plan for the moving ships or objects. Additionally, cameras are used to detect short-range objects, a supplementary solution to ships or objects not equipped with an AIS. A Water Obstacle Detection Network based on Image Segmentation (WODIS) is developed to find potential threat targets. A series of quantifiable experiments have demonstrated that these models can provide reliable scene perception for autonomous ships

A semi-supervised deep learning model for ship encounter situation classification

Maritime safety is an important issue for global shipping industries. Currently, most of collision accidents at sea are caused by the misjudgement of the ship’s operators. The deployment of maritime autonomous surface ships (MASS) can greatly reduce ships’ reliance on human operators by using an automated intelligent collision avoidance system to replace human decision-making. To successfully develop such a system, the capability of autonomously identifying other ships and evaluating their associated encountering situation is of paramount importance. In this paper, we aim to identify ships’ encounter situation modes using deep learning methods based upon the Automatic Identification System (AIS) data. First, a segmentation process is developed to divide each ship’s AIS data into different segments that contain only one encounter situation mode. This is different to the majority of studies that have proposed encounter situation mode classification using hand-crafted features, which may not reflect the actual ship’s movement states. Furthermore, a number of present classification tasks are conducted using substantial labelled AIS data followed by a supervised training paradigm, which is not applicable to our dataset as it contains a large number of unlabelled AIS data. Therefore, a method called Semi-Supervised Convolutional Encoder–Decoder Network (SCEDN) for ship encounter situation classification based on AIS data is proposed. The structure of the network is not only able to automatically extract features from AIS segments but also share training parameters for the unlabelled data. The SCEDN uses an encoder–decoder convolutional structure with four channels for each segment (distance, speed, Time to the Closed Point of Approach (TCPA) and Distance to the Closed Point of Approach (DCPA)) been developed. The performance of the SCEDN model are evaluated by comparing to several baselines with the experimental results demonstrating a higher accuracy can be achieved by our proposed model

Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning

In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message transmitted by on-board transponders, which are captured by surveillance satellites. However, insincere vessels often intentionally shut down their AIS transponders to hide illegal activities. In the open sea, it is very challenging to differentiate intentional AIS shutdowns from missing reception due to protocol limitations, bad weather conditions or restricting satellite positions. This paper presents a novel approach for the detection of abnormal AIS missing reception based on self-supervised deep learning techniques and transformer models. Using historical data, the trained model predicts if a message should be received in the upcoming minute or not. Afterwards, the model reports on detected anomalies by comparing the prediction with what actually happens. Our method can process AIS messages in real-time, in particular, more than 500 Millions AIS messages per month, corresponding to the trajectories of more than 60 000 ships. The method is evaluated on 1-year of real-world data coming from four Norwegian surveillance satellites. Using related research results, we validated our method by rediscovering already detected intentional AIS shutdowns.Comment: IEEE Transactions on Intelligent Transportation System

Maritime modular anomaly detection framework

Detecting maritime anomalies is an extremely important task for maritime agencies around the globe. With the number of vessels at seas growing exponentially, the need for novel automated methods to support them with their routines and upgrade existing technologies is undeniable. MARISA, the Maritime Integrated Surveillance Awareness project, aims at fostering collaboration between 22 governmental organisations and enhance the reaction and decision-making capabilities of the maritime authorities. This work describes our contributions to the development of MARISA’s common toolkit for the detection of maritime anomalies. These efforts, as part of a Masters’ dissertation, lead to the development of the Modular Anomaly Detection Framework, MAD-F, a full data pipe-line which applies efficient and reliable routines to raw vessel navigational data in order to output potential maritime vessel anomalies. The anomalies considered for this work were defined by the experts from various maritime institutions, through MARISA, and allowed us to implement solutions given the real needs in the industry. The MADF functionalities will be validated through actual real maritime exercises. In its current state, we believe that the MAD-F is able to support maritime agencies and be integrated into their legacy systems.Detetar anomalias marítimas é uma tarefa extremamente importante para agências marítimas á escala mundial. Com o número de embarcações em mar crescendo exponencial, a necessidade de desenvolver novas rotinas de suporte ás suas atividades e de atualizar as tecnologias existentes é inegável. MARISA, o projeto de Conscientização da Vigilância Integrada Marítima, visa fomentar a colaboração entre 22 organizações governamentais e melhorar as capacidades de reação e tomada de decisões das autoridades marítimas. Este trabalho descreve as nossas contribuições para o desenvolvimento do toolkit global MARISA, que tem como âmbito a deteção de anomalias marítimas. Estas contribuições servem como parte do desenvolvimento da Modular Anomaly Detection Framework (MAD-F), que serve como um data-pipeline completo que transforma dados de embarcações não estruturados em potenciais anomalias, através do uso de métodos eficientes para tal. As anomalias consideradas para este trabalho foram definidas através do projeto MARISA por especialistas marítimos, e permitiram-nos trabalhar em necessidades reais e atuais do sector. As funcionalidades desenvolvidas serão validadas através de exercícios marítimos reias. No estado atual do MAD-F acreditamos que este será capaz de apoiar agências marítimas, e de posteriormente ser integrado nos sistemas dos mesmos

An investigation into computational methods for classifying fishing vessels to identify illegal, unreported and unregulated fishing

Illegal, unreported and unregulated (IUU) fishing thwarts collective efforts to create a global model for sustainable fishing. Countering IUU fishing is an urgent priority given world population growth and increasing dependence on ocean-sourced food. This paper examines deep learning methods for the classification of fishing vessels with the intent to determine illicit fishing operations. This is achieved through supervised learning with highly irregular time series data in the form of signals from the automatic identification system (AIS). One of the main disadvantages of using such data is the intermittent frequency of signals. To deal with this problem two separate approaches have been followed. The first is feature engineering with zero padding and second is linear interpolation. Moving forward, these methods can be used for handling the ever-growing quantity of irregularly sampled time series data. Fundamentally, this work shows the existence of a distinct relationship between the movement pattern of a vessel and its method of fishing, through classification. Two neural network architectures have been used, firstly, stacked, bidirectional gated recurrent units (GRUs) and, secondly, one-dimensional convolutional neural networks (1D CNNs) with residual connection blocks. Each data pipeline has served as input for both neural networks, producing four different sets of results. The results show that feature engineering with the GRU performs very well with 95% overall accuracy, despite severe class imbalance in the large datasets. By creating a model that can classify the fishing method of a given vessel over 24 hours, this system can be used in real time to monitor behaviour in marine protected areas. It can also locate discrepancies between registered fishing gear and observed behaviour and could therefore be used to safeguard fish stocks

Web-based Geographical Visualization of Container Itineraries

Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair