Proceedings ArticleDOI
Using Deep Learning for Classifying Ship Trajectories
Henrik Ljunggren
- pp 2158-2164
TLDR
How deep learning can be applied to the field of sea surveillance by classifying ship types from their trajectories by teaching 16 different neural networks to classify ships using only motion trajectories and without relying on the reported ship type.Abstract:
In this paper we demonstrate how deep learning can be applied to the field of sea surveillance by classifying ship types from their trajectories. Commercial ships using AIS continually report information such as their ship type, e.g. fishing or cargo ship. A problem with AIS information however is that it can easily be modified and therefore deliberately or accidentally incorrect. In an attempt to address this we use a 1100 hours long AIS data set to teach 16 different neural networks to classify ships using only motion trajectories and without relying on the reported ship type. We also test three baseline methods using a more conventional1-nearest neighbor approach. The evaluation showed that the best performing classifier was the one based on deep learning.read more
Citations
More filters
Proceedings ArticleDOI
Fishing Vessels Activity Detection from Longitudinal AIS Data
Saeed Arasteh,Mohammad A. Tayebi,Zahra Zohrevand,Uwe Glässer,Amir Yaghoubi Shahir,Parvaneh Saeedi,Hans Wehn +6 more
TL;DR: This analysis shows how FishNET can contribute to managing fisheries by learning more about spatiotemporal fishing effort distribution, and to law enforcement agencies by detecting unreported and underreported fishing effort of individual vessels.
Journal ArticleDOI
Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning
TL;DR: Wang et al. as mentioned in this paper proposed 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 a vessel, and (3) predict the CO 2 emissions.
Book ChapterDOI
Research on Ship Classification Based on Trajectory Association
TL;DR: A heterogeneous ensemble learning method based on EasyEnsemble and SMOTE when training the ship classification model that can identify almost all the minority class samples and has certain application value is proposed.
Proceedings Article
Detection of AIS Spoofing in Fishery Scenarios
TL;DR: This contribution addresses the question, up to which accuracy it is possible, to detect fishery vessels with spoofed Ais-type based only on ship's positional, motion, and dimensions' AIS-data.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Book
The Organization of Behavior: A Neuropsychological Theory
TL;DR: In this paper, the authors discuss the first stage of perception: growth of the assembly, the phase sequence, and the problem of Motivational Drift, which is the line of attack.