756 research outputs found
GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection
Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach-referred to as GeoTrackNet-for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn a probabilistic representation of AIS tracks, then uses a contrario detection to detect abnormal events. The neural network helps us capture complex and heterogeneous patterns in vessels' behaviors, while the a contrario detection takes into account the fact that the learned distribution may be location-dependent. Experiments on a real AIS dataset comprising more than 4.2 million AIS messages demonstrate the relevance of the proposed method
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Maritime data integration and analysis: Recent progress and research challenges
The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems
Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks
Data-driven methods open up unprecedented possibilities for maritime
surveillance using Automatic Identification System (AIS) data. In this work, we
explore deep learning strategies using historical AIS observations to address
the problem of predicting future vessel trajectories with a prediction horizon
of several hours. We propose novel sequence-to-sequence vessel trajectory
prediction models based on encoder-decoder recurrent neural networks (RNNs)
that are trained on historical trajectory data to predict future trajectory
samples given previous observations. The proposed architecture combines Long
Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data
and generate future predictions with different intermediate aggregation layers
to capture space-time dependencies in sequential data. Experimental results on
vessel trajectories from an AIS dataset made freely available by the Danish
Maritime Authority show the effectiveness of deep-learning methods for
trajectory prediction based on sequence-to-sequence neural networks, which
achieve better performance than baseline approaches based on linear regression
or on the Multi-Layer Perceptron (MLP) architecture. The comparative evaluation
of results shows: i) the superiority of attention pooling over static pooling
for the specific application, and ii) the remarkable performance improvement
that can be obtained with labeled trajectories, i.e., when predictions are
conditioned on a low-level context representation encoded from the sequence of
past observations, as well as on additional inputs (e.g., port of departure or
arrival) about the vessel's high-level intention, which may be available from
AIS.Comment: Accepted for publications in IEEE Transactions on Aerospace and
Electronic Systems, 17 pages, 9 figure
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
Deep Learning & Graph Clustering for Maritime Logistics: Predicting Destination and Expected Time of Arrival for Vessels Across Europe
In recent years, the need for improving operational processes internationally has drastically increased in the maritime logistics field. The lack of streamlined systems that provide reliable information about real-time maritime traffic for the main agents across countries, such as ports operators and ships authorities, has prompted several research questions. In this work, we propose Deep learning and Machine Learning based methods for (i) clustering ports across Europe using their maritime traffic connectivity, (ii) predicting the next destination of vessels, and (iii) forecasting their expected voyage duration. Several experiments based on public AIS data are developed to analyse and verify these methods, and the results of these experiments indicate that the proposed models achieve the state-of-the-art predictive performance considering the wide geographical scope of the problem across all over Europe. Furthermore, a big advantage of the proposed methods respect to other solutions is that the input data configuration and the intrinsic nature of the models enable the users to predict the aforementioned targets about the next destination of vessels right after they arrive at any European port, instead of waiting for the information given by the first submitted AIS messages once their corresponding next voyage has started. When deployed into production, the resulting system will help maritime industry agents to enhance their real-time situational awareness and operational planning
Combined AI Capabilities for Enhancing Maritime Safety in a Common Information Sharing Environment
The complexity of maritime traffic operations indicates an unprecedented necessity for joint introduction and exploitation of artificial intelligence (AI) technologies, that take advantage of the vast amount of vessels’ data, offered by disparate surveillance systems to face challenges at sea. This paper reviews the recent Big Data and AI technology implementations for enhancing the maritime safety level in the common information sharing environment (CISE) of the maritime agencies, including vessel behavior and anomaly monitoring, and ship collision risk assessment. Specifically, the trajectory fusion implemented with InSyTo module for soft information fusion and management toolbox, and the Early Notification module for Vessel Collision are presented within EFFECTOR Project. The focus is to elaborate technical architecture features of these modules and combined AI capabilities for achieving the desired interoperability and complementarity between maritime systems, aiming to provide better decision support and proper information to be distributed among CISE maritime safety stakeholders
Identification of Vessels on Inland Waters Using Low-Quality Video Streams
Video surveillance can be used to monitor recreational and commercial vessels on inland waters. It is difficult to identify vessels on the basis of registration numbers alone, especially when structure and size are not standardized in a way that allows for easy recognition. In this paper, we propose a novel vessel identification method that can work continuously on streams with a high compression rate or visible compression artifacts. This method can identify all types of moving vessels during daylight. It should work with existing video monitoring systems used to monitor inland waters. When a vessel has hull inscriptions, the method can identify that vessel. If hull inscriptions are not present, the method identifies the direction of movement of the vessel. The tests were performed by real-time video playback of prerecorded samples in the test environment. The method correctly identifies 94% of vessels and the performance indicates that it is suitable for practical application
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