scholarly journals Architecture for Trajectory-Based Fishing Ship Classification with AIS Data

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3782 ◽  
Author(s):  
David Sánchez Pedroche ◽  
Daniel Amigo ◽  
Jesús García ◽  
José Manuel Molina
Keyword(s):  
Real World ◽  
Binary Classification ◽  
Classification Problem ◽  
Automatic Identification ◽  
Identification System ◽  
Preparation Process ◽  
Data Preparation ◽  
Real World Data ◽  
Fishing Vessels ◽  
Ship Classification

This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information.

Automatic Identification System Receiver for Small Fishing Vessels

2020 ◽  
Author(s):  
Febus Reidj G. Cruz ◽  
Jeremiah A. Ordiales ◽  
Malvin Angelo C. Reyes ◽  
Pinky T. Salvanera
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Fishing Vessels

scholarly journals The global network of ports supporting high seas fishing

2021 ◽  
Vol 7 (9) ◽  
pp. eabe3470
Author(s):  
Jorge P. Rodríguez ◽  
Juan Fernández-Gracia ◽  
Carlos M. Duarte ◽  
Xabier Irigoien ◽  
Víctor M. Eguíluz
Keyword(s):  
Fishing Effort ◽  
Global Network ◽  
Automatic Identification ◽  
Identification System ◽  
Middle Income ◽  
Middle Income Countries ◽  
Fishing Vessels ◽  
National Jurisdiction ◽  
The Commons ◽  
High Seas

Fisheries in waters beyond national jurisdiction (“high seas”) are difficult to monitor and manage. Their regulation for sustainability requires critical information on how fishing effort is distributed across fishing and landing areas, including possible border effects at the exclusive economic zone (EEZ) limits. We infer the global network linking harbors supporting fishing vessels to fishing areas in high seas from automatic identification system tracking data in 2014, observing a modular structure, with vessels departing from a given harbor fishing mostly in a single province. The top 16% of these harbors support 84% of fishing effort in high seas, with harbors in low- and middle-income countries ranked among the top supporters. Fishing effort concentrates along narrow strips attached to the boundaries of EEZs with productive fisheries, identifying a free-riding behavior that jeopardizes efforts by nations to sustainably manage their fisheries, perpetuating the tragedy of the commons affecting global fishery resources.

scholarly journals Understanding Smartwatch Battery Utilization in the Wild

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3784 ◽  
Author(s):  
Morteza Homayounfar ◽  
Amirhossein Malekijoo ◽  
Aku Visuri ◽  
Chelsea Dobbins ◽  
Ella Peltonen ◽  
...  
Keyword(s):  
Real World ◽  
Binary Classification ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Indexing Method ◽  
In The Wild ◽  
Art Research ◽  
Battery Discharge ◽  
Changes Over Time ◽  
Application Developers

Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations. First, we employ clustering to identify common patterns of smartwatch battery utilization; second, we introduce a transparent low-parameter convolutional neural network model, which allows us to identify the latent patterns of smartwatch battery utilization. Our model converts the battery consumption rate into a binary classification problem; i.e., low and high consumption. Our model has 85.3% accuracy in predicting high battery discharge events, outperforming other machine learning algorithms that have been used in state-of-the-art research. Besides this, it can be used to extract information from filters of our deep learning model, based on learned filters of the feature extractor, which is impossible for other models. Third, we introduce an indexing method that includes a longitudinal study to quantify smartwatch battery quality changes over time. Our novel findings can assist device manufacturers, vendors and application developers, as well as end-users, to improve smartwatch battery utilization.

Research on Ship Classification Based on Trajectory Features

2017 ◽  
Vol 71 (1) ◽  
pp. 100-116 ◽  
Author(s):  
Kai Sheng ◽  
Zhong Liu ◽  
Dechao Zhou ◽  
Ailin He ◽  
Chengxu Feng
Keyword(s):  
Pattern Mining ◽  
Extraction Methods ◽  
Behaviour Pattern ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Trajectory Features ◽  
Ship Classification ◽  
Historical Trajectory

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

scholarly journals Ocean sentinel albatrosses locate illegal vessels and provide the first estimate of the extent of nondeclared fishing

2020 ◽  
Vol 117 (6) ◽  
pp. 3006-3014 ◽  
Author(s):  
Henri Weimerskirch ◽  
Julien Collet ◽  
Alexandre Corbeau ◽  
Adrien Pajot ◽  
Floran Hoarau ◽  
...  
Keyword(s):  
Threatened Species ◽  
Automatic Identification ◽  
Identification System ◽  
Fish Stocks ◽  
Conservation Policies ◽  
Fishing Vessels ◽  
Exclusive Economic Zones ◽  
Assessment Plans ◽  
Crucial Information ◽  
Economic Zones

With threats to nature becoming increasingly prominent, in order for biodiversity levels to persist, there is a critical need to improve implementation of conservation measures. In the oceans, the surveillance of fisheries is complex and inadequate, such that quantifying and locating nondeclared and illegal fisheries is persistently problematic. Given that these activities dramatically impact oceanic ecosystems, through overexploitation of fish stocks and bycatch of threatened species, innovative ways to monitor the oceans are urgently required. Here, we describe a concept of “Ocean Sentinel” using animals equipped with state-of-the-art loggers which monitor fisheries in remote areas. Albatrosses fitted with loggers detecting and locating the presence of vessels and transmitting the information immediately to authorities allowed an estimation of the proportion of nondeclared fishing vessels operating in national and international waters of the Southern Ocean. We found that in international waters, more than one-third of vessels had no Automatic Identification System operating; in national Exclusive Economic Zones (EEZs), this proportion was lower on average, but variable according to EEZ. Ocean Sentinel was also able to provide unpreceded information on the attraction of seabirds to vessels, giving access to crucial information for risk-assessment plans of threatened species. Attraction differed between species, age, and vessel activity. Fishing vessels attracted more birds than other vessels, and juveniles both encountered fewer vessels and showed a lower attraction to vessels than adults. This study shows that the development of technologies offers the potential of implementing conservation policies by using wide-ranging seabirds to patrol oceans.

scholarly journals A comparison of VMS and AIS data: the effect of data coverage and vessel position recording frequency on estimates of fishing footprints

2017 ◽  
Vol 75 (3) ◽  
pp. 988-998 ◽  
Author(s):  
Jennifer L Shepperson ◽  
Niels T Hintzen ◽  
Claire L Szostek ◽  
Ewen Bell ◽  
Lee G Murray ◽  
...  
Keyword(s):  
Missing Data ◽  
Monitoring System ◽  
Spline Interpolation ◽  
Automatic Identification ◽  
Identification System ◽  
Fishing Activity ◽  
Fishing Vessels ◽  
Position Data ◽  
Hermite Spline ◽  
Data Coverage

Abstract Understanding the distribution of fishing activity is fundamental to quantifying its impact on the seabed. Vessel monitoring system (VMS) data provides a means to understand the footprint (extent and intensity) of fishing activity. Automatic Identification System (AIS) data could offer a higher resolution alternative to VMS data, but differences in coverage and interpretation need to be better understood. VMS and AIS data were compared for individual scallop fishing vessels. There were substantial gaps in the AIS data coverage; AIS data only captured 26% of the time spent fishing compared to VMS data. The amount of missing data varied substantially between vessels (45–99% of each individuals' AIS data were missing). A cubic Hermite spline interpolation of VMS data provided the greatest similarity between VMS and AIS data. But the scale at which the data were analysed (size of the grid cells) had the greatest influence on estimates of fishing footprints. The present gaps in coverage of AIS may make it inappropriate for absolute estimates of fishing activity. VMS already provides a means of collecting more complete fishing position data, shielded from public view. Hence, there is an incentive to increase the VMS poll frequency to calculate more accurate fishing footprints.

scholarly journals Introduction to Coastal HF Maritime Surveillance Radars

2019 ◽  
Vol 26 (3) ◽  
pp. 153-162 ◽  
Author(s):  
Dimov Stojce Ilcev
Keyword(s):  
Ground Truth ◽  
Cost Effective ◽  
Hf Radar ◽  
Automatic Identification ◽  
Identification System ◽  
Ligurian Sea ◽  
Maritime Surveillance ◽  
Detection And Tracking ◽  
Multi Target Tracking ◽  
Ship Classification

Abstract This paper presents the main technical characteristics and working performances of coastal maritime surveillance radars, such as low-power High-Frequency Surface Wave Radars (HFSWR) and Over the Horizon Radars (OTHR). These radars have demonstrated to be a cost-effective long-range early-warning sensor for ship detection and tracking in coastal waters, sea channels and passages. In this work, multi-target tracking and data fusion techniques are applied to live-recorded data from a network of oceanographic HFSWR stations installed in Jindalee Operational Radar Network (JORN), Wellen Radar (WERA) in Ligurian Sea (Mediterranean Sea), CODAR Ocean Sebsorsin and in the German Bight (North Sea). The coastal Imaging Sciences Research (ISR) HFSWR system, Multi-static ISR HF Radar, Ship Classification using Multi-Frequency HF Radar, Coastal HF radar surveillance of pirate boats and Different projects of coastal HF radars for vessels detecting are described. Ship reports from the Automatic Identification System (AIS), recorded from both coastal and satellite Land Earth Stations (LES) are exploited as ground truth information and a methodology is applied to classify the fused tracks and to estimate system performances. Experimental results for all above solutions are presented and discussed, together with an outline for future integration and infrastructures.

Support Vector Machine Classifier with WHM Offset for Unbalanced Data

2008 ◽  
Vol 12 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Boyang Li ◽  
◽  
Jinglu Hu ◽  
Kotaro Hirasawa
Keyword(s):  
Support Vector Machine ◽  
Real World ◽  
Support Vector Machine Classifier ◽  
Classification Problem ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Real World Data ◽  
Unbalanced Classification ◽  
Improved Support Vector Machine

We propose an improved support vector machine (SVM) classifier by introducing a new offset, for solving the real-world unbalanced classification problem. The new offset is calculated based on the unbalanced support vectors resulting from the unbalanced training data. We developed a weighted harmonic mean (WHM) algorithm to further reduce the effects of noise on offset calculation. We apply the proposed approach to classify real-world data. Results of simulation demonstrate the effectiveness of our proposed approach.

scholarly journals Sistema de monitoreo de embarcaciones de pesca artesanal de Anconcito, provincia de Santa Elena, Ecuador, utilizando tecnología AIS - Automatic Identification System

2017 ◽  
Vol 4 (1) ◽  
pp. 194-202
Author(s):  
Ashley Avilés Bastidas ◽  
Germán Sánchez Jordán ◽  
Freddy Villao Quezada
Keyword(s):  
Control System ◽  
Monitoring System ◽  
Effective Control ◽  
Automatic Identification ◽  
Identification System ◽  
Fishing Community ◽  
Artisanal Fishing ◽  
Automatic Identification System ◽  
Fishing Vessels ◽  
Santa Elena

Ante la evidente crisis que se está presentando en Ecuador debido al incremento en los índices de robos en altamar, principalmente de los motores fuera de borda de las lanchas de pesca artesanal, es necesario implementar un sistema de control eficaz que ofrezca seguridad para la comunidad pesquera; con este fin se propone un sistema de detección de embarcaciones de pesca artesanal con tecnología AIS. En este estudio se explican todos los beneficios que ofrece esta tecnología que actualmente se usa para el monitoreo de buques, así como las aplicaciones para un sistema de monitoreo de embarcaciones pequeñas, las clases de equipos disponibles y sus principales características. Con este objetivo se propone el diseño de una red que permita ubicar las embarcaciones de la comunidad pesquera de Anconcito, que es una de las zonas más afectadas por la delincuencia. Además, se anticipan los resultados esperados con este diseño y varias alternativas técnicas para mejorar la cobertura del sistema.AbstractGiven the evident crisis that is occurring in Ecuador due to the increase in the rates of robberies on the high seas, mainly of the outboard motorboats of the artisanal fishing boats, it is necessary to implement an effective control system that provides security for the fishing community. To this end, a system of detection of artisanal fishing vessels with AIS technology is proposed. In this study, all the benefits of this technology are explained which is currently used for monitoring ships, as well as the applications for a monitoring system of small boats, the kinds of equipment available and their main characteristics.With this objective, the design of a network that allows locate the vessels of the fishing community of Anconcito, which is one of the areas most affected by crime, is proposed. In addition, the expected results with this design and several technical alternatives to improve the coverage of the system are anticipated.

Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers

2013 ◽  
pp. 154-163
Author(s):  
Jan Žižka ◽  
Vadim Rukavitsyn
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Classification Accuracy ◽  
Large Data ◽  
Classification Problem ◽  
Experimental Results ◽  
Human Beings ◽  
Real World Data ◽  
Natural Languages ◽  
Automatic Categorization

E-shopping customers, blog authors, reviewers, and other web contributors can express their opinions of a purchased item, film, book, and so forth. Typically, various opinions are centered around one topic (e.g., a commodity, film, etc.). From the Business Intelligence viewpoint, such entries are very valuable; however, they are difficult to automatically process because they are in a natural language. Human beings can distinguish the various opinions. Because of the very large data volumes, could a machine do the same? The suggested method uses the machine-learning (ML) based approach to this classification problem, demonstrating via real-world data that a machine can learn from examples relatively well. The classification accuracy is better than 70%; it is not perfect because of typical problems associated with processing unstructured textual items in natural languages. The data characteristics and experimental results are shown.

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