An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

Maritime vessel movement prediction: A temporal convolutional network model with optimal look-back window size determination

Maritime traffic pattern extraction is a fundamental and crucial factor for maritime surveillance and anomaly detection. Emerging technologies like Automatic Identification System (AIS) provides multi-dimensional data which is used to construct a maritime traffic model. In this paper, we propose a framework of maritime traffic pattern extraction from vessel AIS information, which learns a traffic pattern using an unsupervised technique, and can be applied on historical Automatic Identification System data. AIS data is a kind of spatio-temporal data that contains information of location data, as well as time stamps. In this way, traffic pattern is described by AIS data. Furthermore, we conduct a simulation experiment that extracts traffic pattern from the AIS data through the unsupervised technique. The proposed framework takes advantage of AIS data, which is a type of the spatiotemporal data that consists of vessel motion information, to perform the experiment. The result shows that the unsupervised framework converts useful information from raw AIS data to effective traffic pattern. The proposed method strongly supports the further research on maritime traffic pattern extraction of AIS data. Besides, an overview of the framework and the unsupervised technique for high-level maritime situation awareness is presented.

Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.

Automatic Identification System (AIS) provides abundant near real-time information of moving vessels in the sea of the whole world and has been widely used in the fields of vessel collision avoidance, Maritime Situation Awareness (MSA) and ocean surveillance. The development of satellite-based AIS further expands the range of AIS and enables a wide converge of AIS data collection, which solves the problem of lacking AIS data in high-seas. At the same time, AIS data provides a rich source for data mining for maritime traffic analysis. In this paper, a typical clustering algorithm called K-means is applied to deal with the Space-based AIS(S-AIS) data received by “TianTuo-3” satellite developed by National University of Defense Technology. We use Elbow Rule to determine the optimal number of clusters and calculate the normalized standard deviation of COG(Course Over Ground) and SOG(Speed Over Ground) of vessels in south Africa area as their features to conduct clustering. This method is supposed to evaluate vessels' sailing stability and used in detection of low-likelihood behaviors or anomalies of vessels.

In order to study the ship traffic flow in LaoTieshan channel, this paper rewrites the rules of NS (Nagel-Schreckenberg) model of Cellular Automata (CA) based on AIS（Automatic Identification System） data. By referring to the idea of synchronous flow in three-phase traffic flow theory and introducing the speed adaptation mechanism into the simulation, the new model reproduces the traffic flow of LaoTieshan channel and gets the relevant fundamental diagrams. Compared with the actual flow-density diagram drawn from AIS data, it is found that the simulated flow generated by the improved model is more in line with the actual condition, and also conforms to the phase transition of traffic flow in LaoTieshan channel. The method and idea of the simulation are helpful to explain and investigate some complex situations in LaoTieshan channel.

Recreational boating is an increasing activity in coastal areas and its spatiotemporal overlap with key habitats of marine species pose a risk for negative noise impacts. Yet, recreational vessels are currently unaccounted for in vessel noise models using Automatic Identification System (AIS) data. Here we conduct a case study investigating noise contributions from vessels with and without AIS (non-AIS) in a shallow coastal area within the Inner Danish waters. By tracking vessels with theodolite and AIS, while recording ambient noise levels, we find that non-AIS vessels have a higher occurrence (83%) than AIS vessels, and that motorised recreational vessels can elevate third-octave band noise centred at 0.125, 2 and 16 kHz by 47–51 dB. Accordingly, these vessels dominated the soundscape in the study site due to their high numbers, high speeds and proximity to the coast. Furthermore, recreational vessels caused 49–85% of noise events potentially eliciting behavioural responses in harbour porpoises (AIS vessels caused 5–24%). We therefore conclude that AIS data would poorly predict vessel noise pollution and its impacts in this and other similar marine environments. We suggest to improve vessel noise models and impact assessments by requiring that faster and more powerful recreational vessels carry AIS-transmitters.

Maritime transportation is the cornerstone of the global economy, making understanding vessel activities and their impacts vital. This paper presents a graph-based approach for classifying navigational statuses of vessel trajectories, employing a hierarchical multi-classification deep learning framework using Automatic Identification System (AIS) data. AIS messages are first segmented into continuous tracks, then divided into fixed-size mini-trajectories called a ‘trajectlet’. Labels are assigned using domain knowledge and rule-based methods. Each trajectlet is modelled as a directed acyclic graph, capturing sequential and spatio-temporal movement dynamics. A Graph Neural Network (GNN) first classifies the AIS messages within each trajectlet as ‘stationary’ or ‘underway’. These node-level predictions are then pooled and combined with global features of each trajectlet, and then passed through a multi-layer perceptron to classify the entire trajectlet into one of four navigational statuses. The framework is validated on AIS data from vessels in UK waters, achieving 98% accuracy and F-score, and a 99% AUC–ROC, demonstrating strong predictive performance. This method enhances understanding of vessel operations and supports applications in emissions modelling, economic forecasting, autonomous navigation, and maritime situational awareness. • Models AIS messages as graphs with spatio-temporal and sequential links. • Classifies vessel status using feature-level attention in a GNN framework. • Combines node and graph-level outputs in a hierarchical multi-level classification. • Achieves 98% accuracy and F1-score, 99% AUC–ROC on UK AIS data. • Supports autonomy, port monitoring, emissions and economic modelling.

Path planning for large ships in inland waterways considering risk assessment of AIS data

The development of China’s digital waterways has led to the extensive deployment of cameras along inland waterways. However, the limited processing and utilization of digital resources hinder the ability to provide waterway services. To address this issue, this paper introduces a novel waterway perception approach based on an intelligent navigation marker system. By integrating multiple sensors into navigation markers, the fusion of camera video data and automatic identification system (AIS) data is achieved. The proposed method of an enhanced one-stage object detection algorithm improves detection accuracy for small vessels in complex inland waterway environments, while an object-tracking algorithm ensures the stable monitoring of vessel trajectories. To mitigate AIS data latency, a trajectory prediction algorithm is employed through region-based matching methods for the precise alignment of AIS data with pixel coordinates detected in video feeds. Furthermore, an augmented reality (AR)-based traffic situational awareness framework is developed to dynamically visualize key information. Experimental results demonstrate that the proposed model significantly outperforms mainstream algorithms. It achieves exceptional robustness in detecting small targets and managing complex backgrounds, with data fusion accuracy ranging from 84.29% to 94.32% across multiple tests, thereby substantially enhancing the spatiotemporal alignment between AIS and video data.

Data mining approach for automatic ship-route design for coastal seas using AIS trajectory clustering analysis

Artificial Force Fields for Multi-agent Simulations of Maritime Traffic: A Case Study of Chinese Waterway

Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities

Identifying illegal fishing activities from Automatic Identification System (AIS) data is difficult since AIS messages are broadcast cooperatively, the ship’s master controls the timing, and the content of the transmission and the activities of interest usually occur far away from the shore. This paper presents our work to predict ship types using AIS data from satellites: in such data, there is a pronounced imbalance between the data for different types of ships, the refresh rate is relatively low, and there is a misreporting of information. To mitigate these issues, our prediction algorithm only uses the sequence of ports the ships visited, as inferred from the positions reported in AIS messages. Experiments involving multiple machine learning algorithms showed that such port visits are informative features when inferring ship type. In particular, this was shown to be the case for the fishing vessels, which is the focus of this paper. We then applied a KD-tree to efficiently identify pairs of ships that are close to one another. As this activity is usually dangerous, multiple occurrences of such encounters that are linked to one ship sensibly motivate extra attention. As a result of applying the analysis approach to a month of AIS data related to a large area in Southeast Asia, we identified 17 cases of potentially illegal behaviours.

A wide range of research activities exploit spaceborne Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) for applications that contribute to maritime safety and security. An important requirement of SAR and AIS data fusion is accurate data association (or correlation), which is the process of linking SAR ship detections and AIS observations considered to be of a common origin. The data association is particularly difficult in dense shipping environments, where ships detected in SAR imagery can be wrongly associated with AIS observations. This often results in an erroneous and/or inaccurate maritime picture. Therefore, a classification-aided data association technique is proposed which uses a transfer learning method to classify ship types in SAR imagery. Specifically, a ship classification model is first trained on AIS data and then transferred to make predictions on SAR ship detections. These predictions are subsequently used in the data association which uses a rank-ordered assignment technique to provide a robust match between the data. Two case studies in the UK are used to evaluate the performance of the classification-aided data association technique based on the types of SAR product used for maritime surveillance: wide-area and large-scale data association in the English Channel and focused data association in the Solent. Results show a high level of correspondence between the data that is robust to dense shipping or high traffic, and the confidence in the data association is improved when using class (i.e., ship type) information.

Seatrack Web is a fully operational oil drift forecasting system. It covers the Baltic Sea and part of the North Sea. The system is available over the Internet and has the latest weather and ocean forecasts, thus giving the user the best possible decision tool in an oil combating situation. The drift model calculates the three-dimensional movements of substances or objects at sea, including sinking, stranding and turbulent dispersion. For oils, the evaporation, emulsification and wave-induced vertical dispersion are also calculated. Seatrack Web is the HELCOM system for forecasting of oil drift, and the primary users are oil combating authorities in the countries surrounding the Baltic Sea. It has been in operation since the early 1990s. The system now includes several new features, where a coupling to an AIS (Automatic Identification System) ship track data base is the most important. When finding a spill in the sea, it is possible to make a backtracking calculation with the system. The result shows every hour the location of the oil back in time. Adding AIS data to those oiled areas shows clearly which ships have been close to the oil during the whole calculation period. The system then fetches new ship tracks in space and time depending on the changing positions of the oil. A large number of possible ships are often found, and those can then be deleted successively after analyses and more information about the circumstances. The first year of experience with using AIS in Seatrack Web have led to many suggestions for improvement. It now, for instance, works faster to identify the suspected ships and it is also easier to ignore irrelevant ships. One valuable new feature is that if AIS data are missing it is possible to take those data from the log book and fill in so the ship track will be completed, thus having correct information when analysing the situation. During 2008 satellite information will be shown in the map, which simplifies the identification of illegal polluters even further. The satellite image can also show an ongoing release of a spill, by showing a ship connected to the oil while releasing it. Two radar satellites will be used, RADARSAT 1 and ENVISAT. A case with all information included can also be saved to show as evidence in court at later occasions as animation or special pictures.