Vessel Monitoring Research Articles

Application Prospects and Challenges of VHF Data Exchange System (VDES) in Smart Fisheries

Smart fisheries are an important way to promote the sustainable development of fisheries, and efficient and reliable marine communication systems are the key to realizing smart fisheries. As an emerging marine communication technology, the VHF Data Exchange System (VDES) has the advantages of a high data transmission rate, large communication capacity, and wide coverage, providing new opportunities for the transformation and upgrading of smart fisheries. This paper introduces the technical architecture and functions of the VDES, compares it with existing marine communication technologies, analyzes the key requirements of a smart fishery, and assesses how the VDES meets these requirements. The potential application scenarios of the VDES in smart fishery fields such as fishing vessel monitoring, fishery resource management, and maritime security are discussed. The challenges faced by the VDES in the application of smart fisheries, such as technology, policies and regulations, and construction cost layout, are analyzed, and its future development trend is prospected. Suggestions such as its integration with emerging technologies, the realization of global seamless coverage, and the strengthening of international cooperation and data sharing are proposed. This paper aims to provide theoretical guidance and scientific reference for the promotion and application of the VDES in smart fisheries.

Using High‐Resolution Fisheries Data to Identify Spatial Patterns in Retained Catch Compositions for Mixed Fisheries

ABSTRACTMixed fisheries exploit fish stocks that are heterogeneously distributed in space using gears that are not species selective. This poses a challenge for management as catch limits for less productive stocks constrain catches of more productive stocks leading to losses in yield and economic value. Decoupling catches of stocks caught together in mixed fisheries could increase potential yields and may be achieved through changes in spatial fishing patterns. This study identifies fine‐scale spatial patterns of retained catches for the UK otter trawlers in UK waters by combining vessel monitoring system positional information and logbook data on retained catches. Spatially contiguous units were identified through a combination of Principal Components Analysis and spatial clustering. Our results show a complex spatial structure in the fish assemblage which differs across UK sea areas, with greater similarity between the Northern North Sea and West of Scotland compared to other sea areas. Through simulation, we highlight how fine‐scale spatially resolved fisheries data can identify areas where choke risks from catches of low‐quota, low‐productivity species associated with target species can be reduced. This underscores the value of fine‐scale data for enhancing efficiency and sustainability in mixed fisheries. Wider benefits from the use of fine‐scale data include the ability to identify consistent spatial métier definitions for use in modelling technical interactions. Ultimately, our study informs strategies and approaches that decouple catches of low‐quota, low‐productivity species caught together in mixed fisheries, improving sustainability and the conservation of living resources under complex management challenges.

Identifying narco-trafficking landing zones using satellite imagery and geospatial indicators in Costa Rica

Transnational cocaine trafficking, or ‘narco-trafficking’, networks often move large shipments of harmful and illegal drugs by sea through the use of unregulated boats in remote maritime spaces. This study presents a framework for identifying narco-trafficking drop-off zones by detecting and analyzing Unreported and Unregulated Boats (UUBs) potentially linked to narco-trafficking in Costa Rica’s Guanacaste and Puntarenas regions, utilizing a combination of high-resolution satellite imagery, machine learning, and spatial analysis. By training a YoloV5 convolutional neural network model, we detected boat wakes in PlanetScope satellite images, which were then cross-referenced with legal vessel traffic data (Automatic Identification System and Vessel Monitoring System) to isolate UUBs. A spatial autocorrelation analysis revealed a positive association between UUB locations and narco-trafficking activity indicators, such as drug-related media reports, court arrest records, and cocaine seizure data. High-high clusters of UUBs and trafficking indicators suggested that particular coastal districts may serve as primary landing zones for illicit shipments, a finding consistent with secondary data on cocaine trafficking in the region. By integrating geospatial intelligence with contextual data sources, this study advances methodology for identifying narco-trafficking drop-off zones and contributes a spatially explicit perspective to the broader understanding of cocaine and other illicit supply chains. Despite the limitations of cloud cover and restricted nighttime visibility, this framework offers a proof-of-concept approach for identifying UUB concentrations and cocaine drop-off shipment zones. Future work should consider expanding temporal coverage and multiple imagery to further enhance the identification of narco-trafficking zones.

Addressing unfamiliar ship type recognition in real-scenario vessel monitoring: a multi-angle metric networks framework

Intelligent ship monitoring technology, driven by its exceptional data fitting ability, has emerged as a crucial component within the field of intelligent maritime perception. However, existing deep learning-based ship monitoring studies primarily focus on minimizing the discrepancy between predicted and true labels during model training. This approach, unfortunately, restricts the model to learning only from labeled ship samples within the training set, limiting its capacity to recognize new and unseen ship categories. To address this challenge and enhance the model’s generalization ability and adaptability, a novel framework is presented, termed MultiAngle Metric Networks. The proposed framework incorporates ResNet as its foundation. By employing a novel multi-scale loss function and a new similarity measure, the framework effectively learns ship patterns by minimizing sample distances within the same category and maximizing distances between samples of different categories. The experimental results indicate that the proposed framework achieves the highest level of ship monitoring accuracy when evaluated on three distinct ship monitoring datasets. Even in the case of unfamiliar ships, where the detection performance of conventional models significantly deteriorates, the framework maintains stable and efficient detection capabilities. These experimental results highlight the framework’s ability to effectively generalize its understanding beyond the training samples and adapt to real-world scenarios.

A fishing vessel operational behaviour identification method based on 1D CNN-LSTM

Abstract The identification of fishing vessel operations holds significant importance in addressing fishing industry issues, such as overfishing and illegal, unreported and unregulated fishing (IUUF). Many countries utilise data from vessel monitoring systems (VMSs) or automatic identification systems (AISs) to monitor fishing activities. These data include vessel trajectories, headings and speeds, among others. We aimed to analyse the fishing behaviours of three types of fishing gear used by vessels (trawl, purse seine and gill net) and identify the types of gear employed by the vessels. Therefore, a 1D CNN-LSTM fishing vessel operational behaviour prediction model was proposed by combining a one-dimensional convolutional (1D CNN) neural network and a long short-term memory (LSTM) neural network. The model utilises 1D CNN to extract local features from fishing vessel trajectories and employs LSTM to capture the time series information in the data, eventually classifying fishing gears. The results show that the proposed model achieves a classification accuracy of 92% in categorising fishing vessel operational trajectories. This study significantly contributes to preventing IUUF, curtailing overfishing, and enhancing fisheries management strategies.

Technological innovations in marine engineering: advancing sustainable fleet management

This article provides a detailed analysis of modern trends in technological innovations within the field of marine engineering, aimed at transitioning to more sustainable fleet management methods. The focus is placed on the implementation of alternative fuels, digital solutions for navigation optimization and vessel system control, as well as the development of intelligent energy systems that reduce fuel consumption and greenhouse gas emissions. A significant component is the digitalization of the maritime sector, encompassing the use of big data, blockchain technologies, digital twins, and intelligent routing systems. This approach enables comprehensive vessel monitoring, increased transparency of supply chains, and more accurate operational risk forecasting. Particular attention is given to the role of port infrastructure, as its modernization is critical for the adoption of low-carbon fuels and the establishment of "green corridors." Additionally, the importance of training personnel capable of effectively utilizing innovative technologies is emphasized, along with the need for harmonizing legal frameworks that regulate digital data exchange. The comprehensive strategy proposed in the article integrates technical and organizational aspects and aims to strengthen the economic potential of maritime transport while adhering to principles of environmental responsibility. Thus, this study provides a thorough overview of the prospects and challenges associated with the development of technological solutions for shipping and proposes ways to address them through an interdisciplinary approach and collaboration among all stakeholders in the industry.

Research on Ship Trajectory under Multi source Spatiotemporal Data

To explore the application of multi-source spatiotemporal data fusion technology in ship trajectory analysis, this study develops a fusion algorithm that integrates Kalman filtering and Bayesian estimation. Through the fusion of multi-source data, the positional accuracy of the trajectories is significantly improved, with experimental results showing that the average error is reduced from 58 meters to 23 meters. The results demonstrate that the proposed algorithm effectively enhances the accuracy and reliability of vessel monitoring, which is of great significance for maritime traffic safety.

Strategi Pengelolaan Sumber Daya Perikanan Berkelanjutan di Era Modern

The sustainable management of fishery resources has become a significant challenge in the modern era due to overfishing, habitat degradation, and illegal fishing activities. This study aims to identify effective management strategies through modern technological approaches and community-based frameworks. The research methodology employs a mixed-methods approach, encompassing interviews, questionnaires, field observations, and secondary data analysis across several strategic coastal regions in Indonesia. The findings reveal that utilizing technologies such as the Vessel Monitoring System (VMS) and data-sharing applications enhances the efficiency of fisheries management. Community-based approaches have also proven effective in preserving ecosystems and improving fishers' welfare. Moreover, regulations based on fish stock data are critical elements in ensuring the sustainability of the sector. This study concludes that synergy between technology, inclusive policies, and community participation is the key to achieving sustainable fishery resource management in the modern era.

Review of modern technologies of vessel monitoring in the water areas of the inland waterways using video surveillance tools

At present, there is a problem on inland waterways located within the boundaries of large settlements to ensure monitoring of vessel traffic, including small vessels. This situation is due to limitations on the use of traditional means of monitoring of inland waterway vessels, caused primarily by restrictions on the use of radar systems and radiotechnical means operating in the ultrashortwave frequency range within the city limits. At the same time in large cities of the Russian Federation implemented and successfully used video surveillance systems. The system operating in the city of Moscow to monitor the water area within the city as part of the city traffic control system is considered. Most of the sections of inland waterways located within the limits of St. Petersburg fall within the range of the cameras. The article considers possible approaches to use the city video surveillance system for monitoring the water area within the city limits of St. Petersburg. The paper describes technologies based on artificial neural networks potentially suitable for identification of ships and determination of their exact location at a given moment of time. Advantages and disadvantages of the considered methods are analyzed, as well as a variant of solving the inverse problem of bearing of ships (from the shore) using video surveillance systems consisting of two cameras is proposed. The algorithm of the system operation for identification and determination of vessel movement parameters is proposed. Domestic and foreign experience of solving the problems of vessel identification using video surveillance systems, as well as determining the parameters of vessel movement is analyzed. Several neural networks (object detection, text recognition) are proposed as a solution as a basis for further study of the described problem.

Assessing the overlap between fishing and chondrichthyans exposes high‐risk areas for bycatch of threatened species

AbstractChondrichthyans (sharks, rays, skates, and chimaeras) make up one of the oldest and most ecologically diverse vertebrate groups, yet they face severe threats from fishing, necessitating improved management strategies. To effectively manage these species, we need to understand their spatial interactions with fisheries. However, this understanding is often challenged by limited data on chondrichthyan catches and species identification. In such cases, assessing potential risks from fishing activities can provide valuable insights into these spatial interactions. Here, we propose a method combining geostatistical models fitted to a fishery‐independent dataset with vessel monitoring system (VMS) data to estimate the spatial overlap between chondrichthyans and fishing. Our case study focuses on the western Adriatic Sea in the Mediterranean, examining the overlap between bottom trawling (including otter bottom trawling and beam trawling) and demersal chondrichthyans. We find that the northwestern part of the basin is a hotspot where threatened chondrichthyans (classified as Vulnerable, Endangered, or Critically Endangered by the International Union for Conservation of Nature Red List) greatly overlap with bottom trawling activities. Moreover, some areas, such as the northernmost part of the Adriatic and the “area dei fondi sporchi” in the north‐central offshore part, exhibit minimal overlap between threatened chondrichthyans and bottom trawling, potentially serving as refuges. We recommend prioritizing the management of otter bottom trawling in the northwestern basin to protect these threatened species, while also paying attention to the possible impacts of beam trawling on skates and chondrichthyan habitats. Despite certain limitations, our findings demonstrate that combining geostatistical models of species distributions with VMS data is a promising method for identifying areas of concern for species vulnerable to fishing. This approach can inform targeted management measures and cost‐effective onboard monitoring programs.

Leveraging IoT Technology for Transformative Impact in the Maritime Sector

The maritime sector is undergoing a transformative shift driven by the adoption of the Internet of Things (IoT) era. IoT permits real-time data collection, monitoring, and evaluation of maritime assets, leading to improved operational efficiency, stepped forward protection, and value financial savings. By integrating IoT sensors into ships, ports, and cargo, stakeholders can optimize gas intake, control vessel maintenance, tune shipment movements, and ensure compliance with environmental regulations. Furthermore, IoT enables predictive protection, decreasing downtime and extending gadget lifespan. The usage of IoT in maritime logistics also promotes transparency within the supply chain, assisting in more specific direction-making plans and reducing delays. This summary explores the key packages of IoT inside the maritime region, addressing its ability to revolutionize transport operations, enhance sustainability, and power innovation across the enterprise. Via manner of its very nature, the delivery and logistics organization desires an in-depth and powerful statistical alternative for fulfillment management and choice-making. A specific technique is needed for the marine enterprise due to the complicated and various environments. The naval enterprise's short adoption of IoT generation will make it an awful lot less tough to administer essential services like vessel monitoring, emissions control, preventative protection, protection, and welfare. The IoT answers defined in this article are tailor-made to the business enterprise's present-day needs. The hints are normal with the venture, which intends to offer an interactive remark route on maritime and survey or IoT systems to fill the market need due to the speedy growth of the smart Maritime & Surveying business enterprise. By its very nature, the delivery and logistics company wishes for an in-depth and powerful facts alternative for fulfillment control and selection-making. A specific method is wanted for the marine company due to the complex and diverse environment. The naval enterprise's short adoption of IoT generation will make it much less tough to administer essential offerings like vessel tracking, emissions control, preventative protection, protection, and welfare. The IoT solutions defined in this newsletter are tailored to the organization's current wishes. The tips are regular with the undertaking, which intends to provide an interactive observation course on maritime and survey or IoT structures to fill the market want because of the speedy boom of the smart Maritime & Surveying commercial corporation

Characterization of maritime traffic noise in two areas of the Mediterranean

One of the objectives of the European JPI Oceans DeuteroNoise project is to investigate the soundscapes within different sea basins. Measurement campaigns, including the recording of raw noise data (wave files), have been carried out in the Venice Lagoon, in the North Adriatic and on Barcelona shore. In this work we will present the first results. The data acquired were processed to obtain the characteristic spectrum and noise levels; subsequently the velocity of the particles will be acquired and analysed. The analysis of the recorded data involves the design of a taxonomy of anthropogenic noise sources, e.g. large ships, small boats or any other existing noise source. The recorded data were labelled with these sources, describing their main audio characteristics (spectral distribution, temporal evolution, etc.) for their characterization and future blind identification. Throughout the measurement period, ship and route data were acquired using AIS (Automatic Identification System) and Vessel Monitoring System (VMS) in the measurement sites. The preliminary analysis and labelling, together with ship and route data, will allow the reproduction of acoustic environments of anthropogenic origin in laboratories and the creation of acoustic maps of the basins investigated using commercial software.

Wavelet denoising analysis on vacuum-process monitoring signals of aerospace vacuum vessel structures

Abstract The monitoring of micro-defects or external actions induced vacuum degradation in aerospace vacuum vessels is an important challenge. A vacuum-process monitoring method based on quasi-distributed fiber Bragg grating (FBG) sensing technology is proposed. Due to the influence of environmental noise and vacuum pump operation noise, the raw signals measured by FBG sensors contain a large amount of noises, which affects the measurement accuracy and data analysis. Therefore, the wavelet threshold (WT) denoising method is proposed to analyze the influence of noises on the monitoring signals measured by two different kinds of FBG sensors. The evaluation of the monitoring signals after denoising indicates that the proposed method can effectively remove the noise and significantly improve signal quality. The highest signal-to-noise ratio of the processed signals can reach 37.61 dB and the mean square error is 3.68 × 10−7, while retaining the key features of the original signal. The proposed WT denoising method demonstrates better performance and feasibility compared with moving average filtering and Kalman filtering methods. The study provides critical supports for improving the performance and reliability of the vacuum vessel monitoring system.

Identifying Fishing Trip Behavior from Vessel Monitoring System (VMS) Data Using Machine Learning Models

Illegal fishing in Indonesian waters poses a serious challenge that requires innovative solutions. This research offers an advanced technological approach by applying the Hidden Markov Model (HMM) in Machine Learning to address this issue. Data from the Vessel Monitoring System (VMS) is utilized to efficiently identify fishing vessel activities. By involving a dataset that encompasses various vessel activities, this model can detect suspicious fishing practices in real-time. The research findings demonstrate that this model consistently identifies fishing vessel activities with a high level of accuracy. This study makes a significant contribution to efforts in preventing Illegal, Unreported, and Unregulated (IUU) Fishing and supports marine resource sustainability initiatives.

Research on risk data analysis of hazardous chemical vessels based on stream computing

Abstract To effectively improve the safety supervision and information management level of vessels carrying hazardous chemicals, the maritime management department is promoting the construction of hazardous chemical vessel monitoring systems based on artificial intelligent technology for hazardous chemical terminals and shipping companies in their jurisdiction. It can identify specific risks and transmit the risk data in real-time to the data center. To analyze data in real-time, a risk data analysis platform for hazardous chemical vessels based on stream computing is designed and implemented. The platform acquires vessel automatic identification system (AIS), risk, basic information, and other data. Based on the stream computing of Flink, it evaluates the risk status of vessels in real time, counts the risk data, and displays the results in the form of charts. The analysis of risk data for hazardous chemical vessels helps maritime management departments to track the real-time navigation status of key vessels, timely identify potential safety hazards, and effectively improve the level and efficiency of safety supervision.

Speaking Deads: Sea Turtle Mortality Areas and Fisheries Overlaps Identified Through Backtracking of Stranded Carcasses in the Adriatic Sea

ABSTRACT Interaction with fishing gears represents the main anthropogenic threat at sea for sea turtles worldwide, and identifying the hotspots of turtle bycatch is a priority knowledge gap. Turtle stranding data represent a source of information about mortality areas at sea that are not fully exploited. This study aims to infer turtle mortality areas of turtles stranded along the Italian Adriatic Coast in the period 2019–2021 (1432 records), through backtrack modelling of carcasses. Specifically, the decomposition process of eight loggerhead carcasses was monitored, and the relationship between floating period (FP), turtle size and sea temperature was modelled through a generalized additive model. Oceanographic information was then used to track the routes of floating carcasses back, knowing their size and decomposition stage, and finally estimate the likely area of mortality. A complementary numerical experiment of connectivity between coastal and offshore areas gave indication that areas of potential mortality are relatively close to the coast, particularly in the northern Adriatic. Stranded turtles probably represent just a small fraction (17%–25%) of total at‐sea mortalities in the study area (Italian Adriatic waters), with decomposition rates, season and distance from shore influencing their stranding likelihood. Hence, strandings can inform only about spatio‐temporal variability of coastal mortality hotspots. Inferred areas of turtle mortality were most likely located in the North and Central Adriatic all over the year and overlap with heatmaps of fishing effort, obtained from vessel monitoring system (VMS) and automatic identification system (AIS) data, in the Gulf of Manfredonia and in the North‐West Adriatic in the cold (September–December) and warm (May–August) periods, respectively.

Fishing Vessel Type Recognition Based on Semantic Feature Vector

Identifying fishing vessel types with artificial intelligence has become a key technology in marine resource management. However, classical feature modeling lacks the ability to express time series features, and the feature extraction is insufficient. Hence, this work focuses on the identification of trawlers, gillnetters, and purse seiners based on semantic feature vectors. First, we extract trajectories from massive and complex historical Vessel Monitoring System data that contain a large amount of dirty data and then extract the semantic features of fishing vessel trajectories. Finally, we input the semantic feature vectors into the LightGBM classification model for classification of fishing vessel types. In this experiment, the F1 measure of our proposed method on the East China Sea fishing vessel dataset reached 96.25, which was 6.82% higher than that of the classical feature-modeling method based on fishing vessel trajectories. Experiments show that this method is accurate and effective for the classification of fishing vessels.

Estimating emissions from fishing vessels: a big Beidou data analytical approach

Fishing vessels are important contributors to global emissions in terms of greenhouse gases and air pollutants. However, few studies have addressed the emissions from fishing vessels on fishing grounds. In this study, a framework for estimating fishing vessel emissions, using a bottom-up dynamic method based on the big data from the Beidou VMS (vessel monitoring system) of fishing vessels, is proposed and applied to a survey of fishing vessel emissions in the East China Sea. The results of the study established a one-year emission inventory of fishing vessels in the East China Sea. This study was the first to use VMS data to estimate fishing vessel emissions in a fishing area, and the results will help to support the management of their carbon emissions.

Global expansion of marine protected areas and the redistribution of fishing effort

The expansion of marine protected areas (MPAs) is a core focus of global conservation efforts, with the "30x30" initiative to protect 30% of the ocean by 2030 serving as a prominent example of this trend. We consider a series of proposed MPA network expansions of various sizes, and we forecast the impact this increase in protection would have on global patterns of fishing effort. We do so by building a predictive machine learning model trained on a global dataset of satellite-based fishing vessel monitoring data, current MPA locations, and spatiotemporal environmental, geographic, political, and economic features. We then use this model to predict future fishing effort under various MPA expansion scenarios compared to a business-as-usual counterfactual scenario that includes no new MPAs. The difference between these scenarios represents the predicted change in fishing effort associated with MPA expansion. We find that regardless of the MPA network objectives or size, fishing effort would decrease inside the MPAs, though by much less than 100%. Moreover, we find that the reduction in fishing effort inside MPAs does not simply redistribute outside-rather, fishing effort outside MPAs would also decline. The overall magnitude of the predicted decrease in global fishing effort principally depends on where networks are placed in relation to existing fishing effort. MPA expansion will lead to a global redistribution of fishing effort that should be accounted for in network design, implementation, and impact evaluation.

Vessel Monitoring System (VMS) Technology as a Management Strategy for Longline Tuna Capture Fisheries in Benoa

Benoa Port has fishing vessel activities that are the foundation of Bali's tuna swamp fisheries (longlines). Sustainable tuna capture fisheries will be impossible to attain if the number of infractions in the marine and fisheries sectors continues to rise. The government's efforts to reduce infractions in marine and fisheries are achieved through tightening the oversight system. The Vessel Monitoring System (VMS) surveillance system was created to reduce breaches in the marine and fishery sectors. The goal of this study is to determine the monitoring and suspected indication of violations of longlines using the VMS at the Benoa PSDKP as well as to learn about the capture fisheries management strategy using VMS data on longlines at Benoa Port using an analytic hierarchy process (AHP) approach. This study used a qualitative-quantitative descriptive technique with AHP analysis to determine the value of the management priority scale. Based on the findings of monitoring longlines with web tracks from January to December 2022, the most targeted fishing area was the Indian Ocean High Seas, and the most prominent suspected indication of violation was the inactivity of VMS when the vessel was operating. The main approach for managing capture fisheries utilizing VMS data on longlines in Benoa Port with the AHP analysis is for the government to offer subsidies for installing VMS on all fishing vessels, particularly those under 30 GT.