scholarly journals A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2756
Author(s):  
Alessandro Galdelli ◽  
Adriano Mancini ◽  
Carmen Ferrà ◽  
Anna Nora Tassetti
Keyword(s):  
Large Scale ◽  
Automatic Identification ◽  
Identification System ◽  
Distributed Data ◽  
Close Proximity ◽  
Geographically Distributed ◽  
Remote Sensing Systems ◽  
Spatio Temporal ◽  
Sar Imagery ◽  
Point To Point

Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight “suspicious” AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel—and the gear it adopts—is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas.

scholarly journals Classification-Aided SAR and AIS Data Fusion for Space-Based Maritime Surveillance

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Maximilian Rodger ◽  
Raffaella Guida
Keyword(s):  
Data Fusion ◽  
Large Scale ◽  
Data Association ◽  
Classification Model ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Surveillance ◽  
Research Activities ◽  
Wide Range ◽  
Sar Imagery

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.

scholarly journals Modelling the Risk of Imported COVID-19 Infections at Maritime Ports Based on the Mobility of International-Going Ships

2022 ◽  
Vol 11 (1) ◽  
pp. 60
Author(s):  
Zhihuan Wang ◽  
Chenguang Meng ◽  
Mengyuan Yao ◽  
Christophe Claramunt
Keyword(s):  
Clustering Algorithm ◽  
Infection Risk ◽  
Data Driven ◽  
Risk Level ◽  
Automatic Identification ◽  
Identification System ◽  
Prevention Measures ◽  
Increase Rate ◽  
Spatio Temporal ◽  
Imported Infections

Maritime ports are critical logistics hubs that play an important role when preventing the transmission of COVID-19-imported infections from incoming international-going ships. This study introduces a data-driven method to dynamically model infection risks of international ports from imported COVID-19 cases. The approach is based on global Automatic Identification System (AIS) data and a spatio-temporal clustering algorithm that both automatically identifies ports and countries approached by ships and correlates them with country COVID-19 statistics and stopover dates. The infection risk of an individual ship is firstly modeled by considering the current number of COVID-19 cases of the approached countries, increase rate of the new cases, and ship capacity. The infection risk of a maritime port is mainly calculated as the aggregation of the risks of all of the ships stopovering at a specific date. This method is applied to track the risk of the imported COVID-19 of the main cruise ports worldwide. The results show that the proposed method dynamically estimates the risk level of the overseas imported COVID-19 of cruise ports and has the potential to provide valuable support to improve prevention measures and reduce the risk of imported COVID-19 cases in seaports.

scholarly journals Estimating the Speed of Icegoing Ships by Integrating SAR Imagery and Ship Data From Automatic Identification System

2018 ◽  
Author(s):  
Markku Simila ◽  
Mikko Lensu
Keyword(s):  
Mean Squared Error ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Squared Error ◽  
Ice Drift ◽  
Gulf Of Bothnia ◽  
Ice Conditions ◽  
Time Gap ◽  
Sar Imagery

Ship speeds extracted from AIS data vary with ice conditions. We extrapolated this variation with SAR data to a chart of expected icegoing speed. The study is for the Gulf of Bothnia in March 2013 and for ships with ice class 1A Super that are able to navigate without icbreaker assistance. The speed was normalized to 0-10 for each ship. As the matching between AIS and SAR was complicated by ice drift during the time gap, from hours to two days, we calculated a set of local SAR statistics over several scales. We used random tree regression to estimate the speed. The accuracy was quantified by mean squared error (MSE), and the fraction of estimates close to the actual speeds. These depended strongly on the route and the day. MSE varied from 0.4 to 2.7 units2 for daily routes. 65 % of the estimates deviated less than one unit and 82 % less than 1.5 units from the AIS speeds. The estimated daily mean speeds were close to the observations. Largest speed decreases were provided by the estimator in a dampened form or not at all. This improved when ice chart thickness was included as one predictor.

Applying machine learning to satellite imagery and vessel-tracking data to detect chronic oil pollution from ships at sea and identify the polluters

2021 ◽  
Author(s):  
Jona Raphael ◽  
Ben Eggleston ◽  
Ryan Covington ◽  
Tatianna Evanisko ◽  
Sasha Bylsma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oil Spills ◽  
Oil Pollution ◽  
Satellite Image ◽  
Petroleum Products ◽  
Training Data ◽  
Automatic Identification ◽  
Identification System ◽  
Sar Imagery ◽  
Pollution Events

<p><strong>Operational oil discharges from ships</strong>, also known as “bilge dumping,” have been identified as a major source of petroleum products entering our oceans, cumulatively exceeding the largest oil spills, such as the Exxon Valdez and Deepwater Horizon spills, even when considered over short time spans. However, we still don’t have a good estimate of</p><ul><li>How much oil is being discharged;</li> <li>Where the discharge is happening;</li> <li>Who the responsible vessels are.</li> </ul><p>This makes it difficult to prevent and effectively respond to oil pollution that can damage our marine and coastal environments and economies that depend on them.</p><p> </p><p>In this presentation we will share SkyTruth’s recent work to address these gaps using machine learning tools to detect oil pollution events and identify the responsible vessels when possible. We use a convolutional neural network (CNN) in a ResNet-34 architecture to perform <strong>pixel segmentation</strong> on all incoming <strong>Sentinel-1 synthetic aperture radar</strong> (SAR) imagery to classify slicks. Despite the satellites’ incomplete oceanic coverage, we have been detecting an average of <strong>135 vessel slicks per month</strong>, and have identified several geographic hotspots where oily discharges are occurring regularly. For the images that capture a vessel in the act of discharging oil, we rely on an <strong>Automatic Identification System</strong> (AIS) database to extract details about the ships, including vessel type and flag state. We will share our experience</p><ul><li>Making sufficient training data from inherently sparse satellite image datasets;</li> <li>Building a computer vision model using PyTorch and fastai;</li> <li>Fully automating the process in the Amazon Web Services (AWS) cloud.</li> </ul><p>The application has been running continuously since August 2020, has processed over 380,000 Sentinel-1 images, and has populated a database with more than 1100 high-confidence slicks from vessels. We will be discussing <strong>preliminary results</strong> from this dataset and remaining challenges to be overcome.</p><p> </p><p>Our objective in making this information and the underlying code, models, and training data <strong>freely available to the public</strong> and governments around the world is to enable public pressure campaigns to improve the prevention of and response to pollution events. Learn more at https://skytruth.org/bilge-dumping/</p>

scholarly journals Atmosphere channel for “last mile problem” in quantum communication

2019 ◽  
Vol 220 ◽  
pp. 01006
Author(s):  
I.Z. Latypov ◽  
D.O. Akat’ev ◽  
V.V. Chistyakov ◽  
M.A. Fadeev ◽  
A.K. Khalturinsky ◽  
...  
Keyword(s):  
Data Protection ◽  
Communication Channel ◽  
Quantum Communication ◽  
Distributed Data ◽  
Data Centre ◽  
Last Mile ◽  
Geographically Distributed ◽  
The Creation ◽  
Point To Point ◽  
Last Mile Problem

The work is devoted to the creation of a telescopic transceiver system that organizes an atmospheric point-to-point communication channel, and its use for quantum communication at sideband frequencies as the “last mile” for data protection in a geographically distributed data centre

scholarly journals Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1078 ◽  
Author(s):  
Ramona Pelich ◽  
Marco Chini ◽  
Renaud Hostache ◽  
Patrick Matgen ◽  
Carlos Lopez-Martinez ◽  
...  
Keyword(s):  
Detection Rate ◽  
Large Scale ◽  
Added Value ◽  
Automatic Identification ◽  
Identification System ◽  
Dual Polarization ◽  
Ship Detection ◽  
Traffic Conditions ◽  
Intensity Images ◽  
Polarization Channel

This research addresses the use of dual-polarimetric descriptors for automatic large-scale ship detection and characterization from synthetic aperture radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the detection results are merged subsequently. In this study, we propose to make use of the complex coherence between the two polarization channels of Sentinel-1 and to perform vessel detection in this domain. Therefore, an automatic algorithm, based on the dual-polarization coherence, and applicable to entire large scale SAR scenes in a timely manner, is developed. Automatic identification system (AIS) data are used for an extensive and also large scale cross-comparison with the SAR-based detections. The comparative assessment allows us to evaluate the added-value of the dual-polarization complex coherence, with respect to SAR intensity images in ship detection, as well as the SAR detection performances depending on a vessel’s size. The proposed methodology is justified statistically and tested on Sentinel-1 data acquired over two different and contrasting, in terms of traffic conditions, areas: the English Channel the and Pacific coastline of Mexico. The results indicate a very high SAR detection rate, i.e., >80%, for vessels larger than 60 m and a decrease of detection rate up to 40 % for smaller size vessels. In addition, the analysis highlights many SAR detections without corresponding AIS positions, indicating the complementarity of SAR with respect to cooperative sources for detecting dark vessels.

scholarly journals SPATIO TEMPORAL DATA CUBE APPLIED TO AIS CONTAINERSHIPS TREND ANALYSIS IN THE EARLY YEARS OF THE BELT AND ROAD INITIATIVE – FROM GLOBAL TO LOCAL SCALE

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 71-78
Author(s):  
E. Arco ◽  
A. Ajmar ◽  
F. Cremaschini ◽  
C. Monaco
Keyword(s):  
Trend Analysis ◽  
Value Added ◽  
Data Cube ◽  
Early Years ◽  
Automatic Identification ◽  
Identification System ◽  
Export Trade ◽  
Time Data ◽  
Belt And Road ◽  
Spatio Temporal

Abstract. Maritime trade represents a significant part of all global import-export trade. The traffic of containerships can be monitored through Automatic Identification System (AIS), due to the fact that the International Maritime Organization (IMO) regulation requires AIS to be fitted aboard all ships of 300 gross tonnage and upwards engaged on international voyages. The approach proposed by the authors aimed to extract value added information from an AIS dataset, with a focus on maritime economy. Using an AIS dataset of global position of containerships from 01/01/2012 to 31/12/2016, the paper focuses on space-time data cube creation and analysis for a better understanding of maritime trades trends. Data cube creation has been tested at different spatio-temporal bins dimension and on different specific topics (TEU classes, alliances, chokepoints and port areas), analysing the sensitivity on trend results, and highlighting how appropriate spatio-temporal bins dimensions are important to effectively highlight relevant trends. Results of the trend analysis are discussed and validated with the main data and information found over the period 2012–2016. The aim of this paper is to demonstrate the suitability of this approach applied to AIS data and to highlight its limitations. The authors can conclude that the approach used has proved to be adequate in describing the evolution of the global import-export trade.

scholarly journals X-Band/C-Band-Comparison of Ship Wake Detectability

2020 ◽  
Author(s):  
Björn Tings ◽  
Sven Jacobsen ◽  
Stefan Wiehle ◽  
Egbert Schwarz ◽  
Holger Daedelow
Keyword(s):  
Detection Performance ◽  
Automatic Identification ◽  
Identification System ◽  
Sar Images ◽  
Ship Wake ◽  
X Band ◽  
System Data ◽  
Wake Detection ◽  
Sar Imagery ◽  
The Individual

Recent studies investigated the detectability of ship wake signatures on SAR imagery using a large number of SAR images collocated with Automatic Identification System data for training machine learning models. These detectability models are in agreement with oceanographic expectations from preceding studies and can therefore be used for comparing the performance of different SAR sensors in terms of wake detectability. Previous model comparisons showed better wake detection performance of TerraSAR-X (TS-X) than of RADARSAT-2 (RS2) and Sentinel-1 (S1). A comparison between CosmoSkymed (CSK) and RS2 is performed here, to examine the hypothesis that X-Band is generally better for wake detection than C-Band. Finally, this hypothesis is not confirmed, as the detectability models for TS-X, CSK and RS2 reveal similar performances. A comparison of wake detection performance should take the individual wake components into account separately.

scholarly journals Performance Analysis of a Medium Frequency Offshore Grid for Identification Of Vessels Sailing on High Density Maritime European Routes

2017 ◽  
Vol 24 (4) ◽  
pp. 18-26
Author(s):  
Alfonso López ◽  
Miguel Gutiérrez ◽  
Andrés Ortega ◽  
Cristina Puente ◽  
Alejandro Morales ◽  
...  
Keyword(s):  
Grid Method ◽  
Real Data ◽  
Satellite System ◽  
Simulation Software ◽  
High Density ◽  
Traffic Density ◽  
Automatic Identification ◽  
Identification System ◽  
Distributed Data ◽  
Medium Frequency

Abstract The paper analyses the performance of an Automatic Vessel Identification System on Medium Frequency (AVISOMEF), which works with the Grid Method (GM) on high density maritime European routes using real data and uniformly distributed data. Compared to other systems, AVISOMEF is a novelty, as it is not a satellite system, nor is it limited by a given coverage distance, in contrast to the Automatic Identification System (AIS), though in exceptional circumstances it leans towards it. To perform the analysis, special simulation software was developed. Moreover, a number of maritime routes along with their traffic density data were selected for the study. For each route, two simulations were performed, the first of which based on the uniform traffic distribution along the route, while the second one made use of real AIS data positioning of vessels sailing on the selected routes. The obtained results for both simulations made the basis for formulating conclusions regarding the capacity of selected routes to support AVISOMEF.

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