Search Results (58)

With the increasing digitalization of maritime transportation, the demand for structured and interoperable data has grown. While the S-100 framework developed by the International Hydrographic Organization (IHO) provides a foundation for standardizing maritime information, a data model for representing marine casualties has not yet been developed. As a result, past incident data—such as collisions or groundings—remain fragmented in unstructured formats and are excluded from electronic navigational systems, limiting their use in safety analysis and route planning. To address this gap, this paper proposes a data model for structuring and visualizing marine casualty information within the S-100 standard. The model was designed by defining an application schema, constructing a machine-readable feature catalogue, and developing a portrayal catalogue and custom symbology for integration into Electronic Navigational Charts (ENCs). A case study using actual casualty records was conducted to examine whether the model satisfies the structural and portrayal requirements of the S-100 framework. The proposed model enables previously unstructured casualty data to be standardized and spatially integrated into digital chart systems. This approach allows accident information to be used alongside other S-100-based data models, contributing to risk-aware route planning and future applications in smart ship operations and maritime safety services. Full article

Deep Reinforcement Learning (DRL)-based algorithms have demonstrated substantial effectiveness in tackling complex control problems for autonomous underwater vehicles (AUVs). This paper attempts to evaluate reinforcement learning (RL)-based methods for AUV trajectory planning by incorporating a model of a vehicle’s full motion. In this study, the agent (AUV) is assumed to have no prior knowledge of the environment in which it navigates. Instead, it only receives inputs from navigation sensors and a simulated sonar. Additionally, in the article, a reward function is proposed and described, along with its optimization process, to elicit the desired behaviors in the underwater vehicle. The models are trained and tested on simplified electronic navigation chart (ENC) maps, followed by a comparative analysis against five effective classical methods for trajectory planning. The proposed solution enables efficient, collision-free route planning for the autonomous underwater vehicle, taking its motion dynamics into account to reach the designated target successfully. Full article

Digitalization has revolutionized the maritime industry, particularly in navigation systems. The use of advanced tools such as the Electronic Chart Display and Information System (ECDIS) has increased the need for information processing. However, the complexity of these systems can be overwhelming for navigators. To address the concern of usability of these complex navigation systems, training with simulator data allows the crew to familiarize themselves with these systems, handle complex navigation scenarios effectively, support the transition from paper-based systems to digital systems, and help in improving their situational awareness (SA) at sea. We propose a tool that provides optimal conditions for assessing situational awareness and informing the development of intuitive systems and user interfaces. In the maritime safety domain, there is an inverse correlation between situational awareness and scenario/system complexity, highlighting the importance of effective training and assessments to improve SA. The proposed tool utilizes the Situational Awareness Global Assessment Technique (SAGAT) method, widely used in other domains, to calculate an individual’s SA score. It evaluates participants’ situational awareness in different navigational scenarios on Ship Handling Simulators, using dynamic questionnaires and contextual maps. Additionally, it integrates a rule-based system to assess participants’ performance and calculate a situational awareness score in real time, offering possibilities for assessing the SA of navigators. Full article

Automatic Identification System (AIS) data offer essential insights into maritime traffic patterns; however, effective visualization tools for decision-making remain limited. This study presents an integrated visualization processing method to support ship operators by identifying maritime traffic behavior information, such as traffic density, direction, and flow in specific sea navigational areas. We analyzed AIS dynamic data from a specific sea area, calculated ship density distributions across a grid lattice, and obtained visualizations of traffic-dense areas as heat maps. Using the density-based spatial clustering of applications with a noise algorithm, we detected traffic direction at each grid point, which was visualized in the form of directional arrows, and clustered ship trajectories to identify representative traffic flows. The visualizations were integrated and overlaid onto an S-57-based electronic nautical map for Mokpo’s entry and exit routes, revealing primary shipping lanes and critical inflection points within the target area. This integrated visualization method simultaneously displays traffic density, flow, and customary routes. It is adapted for the electronic nautical chart (S-101) under the next-generation hydrographic information standard (S-100), which can be used as a tool to support decision-making for ship operators. Full article

In recent years, unmanned surface vehicles (USVs) have gained increasing attention in industry due to their efficiency and versatility in marine operations. Artificial potential field (APF) methods, with their strong adaptability and simplicity of implementation, are widely used in USV path planning tasks. However, the naive APF method struggles in static complex environments, due to the local minima problem. Not to mention that actual navigations may involve other dynamic traffic participants. In this work, an improved APF algorithm integrating the boundary potential field method and the International Regulations for Preventing Collisions at Sea (COLREGs) is proposed. By incorporating the boundary potential field method, this novel approach effectively reduces the computational burden caused by clusters of land obstacles in complex environments, significantly improving computational efficiency. Furthermore, the APF method is refined to ensure the algorithm strictly adheres to COLREGs in head-on, overtaking, and crossing encounters, generating smooth and safe collision avoidance paths. The proposed method was tested in numerous complex scenarios derived from electronic navigational charts. The simulation results demonstrated the robustness and efficiency of the proposed algorithm for collision avoidance within complex maritime environments, providing reliable technical support for autonomous obstacle avoidance in dynamic ocean conditions. Full article

Maritime navigation safety relies on preventing accidents, such as collisions and groundings. However, several factors can exacerbate these risks, including inexistent or inadequate buoyage systems and nautical charts with outdated bathymetry. The International Hydrographic Organization (IHO) highlights high costs and traditional methods as obstacles to updating bathymetric information, impacting both safety and socio-economic factors. Navigation in inland and coastal waters is particularly complex due to the presence of shallow intertidal zones that are not signaled, where navigation depends on tidal height, vessel draw, and local knowledge. To address this, recreational vessels can use electronic maritime sensors to share critical data with nearby vessels. This article introduces a low-cost maritime data sharing system using IoT technologies for both inland (e.g., Ria de Aveiro) and coastal waters. The system enables the collection and sharing of meteorological and oceanographic data, including depth, tide height, wind direction, and speed. Using a case study in the Ria de Aveiro lagoon, known for its navigational difficulties, the system was developed with a Contextual Design approach focusing on sailors’ needs. It allows for the real-time sharing of data, helping vessels to anticipate maneuvers for safer navigation. The results demonstrate the system’s potential to improve maritime safety in both inland and coastal areas. Full article

The gravity–geologic method (GGM) is widely used for bathymetric predictions. However, the conventional GGM cannot be applied in regions without actual bathymetric data. The modified gravity–geologic method (MGGM) enhances the accuracy of bathymetric models by supplementing short-wavelength gravity anomalies with an a priori bathymetric model, but it overlooks the significance of actual bathymetric data in the prediction process. In this study, we used the BP neural network (BPNN), incorporating shipborne depth soundings and coastline data as zero-depth estimates combined with the MGGM to produce a bathymetric model (BPGGM_BAT) for the South China Sea (105°E–122°E, 0°N–26°N). The results indicate that the BPGGM_BAT model decreases the root-mean-square (RMS) of bathymetry differences from 154.33 m to approximately 140.43 m relative to multibeam depth data. Additionally, the RMS differences between the BPGGM_BAT model and multibeam depth data show further improvements of 19.63%, 20.10%, and 19.54% when compared with the recently released SRTM15_V2.6, GEBCO_2022, and topo_V27.1 models, respectively. The precision of the BPGGM_BAT model is comparable to that of the SDUST2023BCO model, as verified using multibeam depth data in open sea regions. The BPGGM_BAT model outperforms existing models with RMS differences of 8.54% to 32.66%, as verified using Electronic Navigational Chart (ENC) bathymetric data in the regions around the Zhongsha and Nansha Islands. A power density analysis suggests that the BPGGM_BAT model is superior to the MGGM_BAT model for predicting seafloor topography within wavelengths shorter than 15 km, and its performance is closely consistent with that of the topo_V27.1 and SDUST2023BCO models. Overall, this integrated method demonstrates significant potential for improving the accuracy of bathymetric predictions. Full article

The maritime sector is increasingly integrating Information and Communication Technology (ICT) and Artificial Intelligence (AI) technologies to enhance safety, environmental protection, and operational efficiency. With the introduction of the MASS Code by the International Maritime Organization (IMO), which regulates Maritime Autonomous Surface Ships (MASS), ensuring the safety of AI-integrated systems on these vessels has become critical. To achieve safe navigation, it is essential to identify potential risks during the system planning stage and design systems that can effectively address these risks. This paper proposes RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems. RA4MAIS employs a systematic approach to uncover potential risks by considering internal system failures, human interactions, environmental conditions, AI-specific characteristics, and data quality issues. The method provides structured guidance to identify unknown risk situations and supports the development of safety requirements that guide system design and implementation. A case study on an Electronic Chart Display and Information System (ECDIS) with an AI-integrated collision avoidance function demonstrates the applicability of RA4MAIS, highlighting its effectiveness in identifying specific risks related to AI performance and reliability. The proposed method offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships. Full article

Considerable technological progress has been made in ship handling and mooring in recent years, especially progress generated by the needs imposed by the introduction of ever larger ships. These advancements exploit the economic scale and environmental efficiency of larger vessels, but also present unique challenges, particularly in narrow waterways and harbour approaches. Precise navigation in these environments requires highly accurate hydrographic measurements, high-quality electronic charts, and advanced navigation systems, such as modern electronic chart display and information systems (ECDIS). Safe and efficient port operations also depend on the optimised allocation of port resources and comprehensive queuing strategies. Modern ships are increasingly susceptible to interference with Global Navigation Satellite Systems (GNSS) and Automatic Identification Systems (AIS), necessitating the development of resilient technologies and procedures to ensure navigational safety. In addition, climate change is exacerbating the challenges of ship handling in ports, as larger vessels are particularly vulnerable to sudden gusts of wind and have difficulty maintaining their position in the quay in strong crosswinds. Training and simulation are crucial to overcoming these challenges. Ship-handling simulators are invaluable for training purposes, but development is still needed to accurately simulate tilt and lean effects, especially when ships are sailing in narrow channels with following currents and changing winds. Improving the accuracy of these simulators will improve the preparation of seafarers for real-life conditions and ultimately contribute to safer and more efficient ship operations. Full article

Electronic navigational charts (ENCs) are geospatial databases compiled in strict accordance with the technical specifications of the International Hydrographic Organization (IHO). Electronic Chart Display and Information System (ECDIS) is a Geographic Information System (GIS) operated by ENCs for real-time navigation at sea, which is one of the key technologies for intelligent ships to realize autonomous navigation, intelligent decision-making, and other functions. Facing the urgent demand for high-precision and real-time nautical chart products for polar navigation under the new situation, the projection of ENCs for polar navigation is systematically analyzed in this paper. Based on the theory of complex functions, we derive direct transformations of Mercator projection, polar Gauss-Krüger projection, and polar stereographic projection. A rational set of dynamic projection options oriented towards polar navigation is proposed with reference to existing specifications for the compilation of the ENCs. From the perspective of nautical users, rather than the GIS expert or professional cartographer, an ENCs visualization idea based on multithread-double buffering is integrated into Polar Region Electronic Navigational Charts software, which effectively solves the problem of large projection distortion in polar navigation applications. Taking the CGCS2000 reference ellipsoid as an example, the numerical analysis shows that the length distortion of the Mercator projection is less than 10% in the region up to 74°, but it is more than 80% at very high latitudes. The maximum distortion of the polar Gauss-Krüger projection does not exceed 10%. The degree of distortion of the polar stereographic projection is less than 1% above 79°. In addition, the computational errors of the direct conversion formulas do not exceed $10^{-9}$ m throughout the Arctic range. From the point of view of the computational efficiency of the direct conversion model, it takes no more than 0.1 s to compute nearly 8 million points at $1^{\prime }\times 1^{\prime }$ resolution, which fully meets the demand for real-time nautical chart products under information technology conditions. Full article

Background: The pandemic has disproportionately impacted rural communities with a higher burden of chronic disease and COVID-19 infection. West Virginia is a rural state with a high rate of diabetes, hypertension, and COPD, which are known risk factors for severe COVID-19 and long COVID. Yet, there is a significant hesitancy regarding COVID-19 vaccination uptake in the state. The purpose of this study was to use an educational intervention to increase vaccine knowledge and vaccine acceptance in rural patients with chronic disease(s) in West Virginia. This project used an academic–community partnership comprised of researchers, practitioners, community organizations, community-engaged partners, and patient stakeholders to increase COVID-19 health literacy and increase vaccine acceptance among rural West Virginians with chronic conditions. Materials and Methods: A quasi-experimental study design was used to deliver an educational intervention by trained Health Navigators using short videos to increase COVID-19 health literacy and address participants’ vaccine concerns. Eligibility included adults (18 years and older) who have at least one chronic condition. A statewide community advisory board (CAB) guided the development of the educational training curriculum and implementation strategies. An adapted version of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework guided the development of the intervention. Health Navigators (n = 45) delivered the educational intervention in their local communities between November 2022 and October 2023 (project implementation is still ongoing). Intervention fidelity checks, an adaptable script, and a flow chart allowed tailoring of brief videos to address participants’ specific COVID-19 questions and vaccine concerns. A validated online survey, monitored by an online Research Electronic Data Capture (REDCap) database, assessed participants’ knowledge, perceived susceptibility, and vaccine intention. Results: Health Navigators delivered the intervention to 1368 West Virginians in 52 counties (59.2% women; 61.8% without a college degree). Participants reported living with an average of 2.1 ± 1.4 chronic conditions. The mean age was 43.5 ± 18.8 years. The majority of participants (81.2%) had received the primary vaccination series, and 63.1% had at least one booster. However, 18% were unvaccinated or did not complete the primary COVID-19 vaccine series. Discussions to improve vaccine literacy focused on how the vaccine was so quickly developed and protects against variants, addressing concerns related to the safety, short- and long-term side effects, and importance of vaccine uptake for immunocompromised individuals. Participants with higher concerns were more likely to be unvaccinated and to have not completed their primary series or boosters (p < 0.001). However, the educational intervention improved the willingness of individuals who were either unvaccinated or did not complete their primary vaccine series to get vaccinated (11.4%). Discussion: Our findings highlight the importance of vaccine literacy in increasing vaccination rates among rural patients with chronic diseases. Using the EPIS framework allowed us to reflect upon the challenges, ensure resilience during changing local contexts, and plan and implement a promising, cost-effective intervention in rural areas. Conclusions: This study provides insights into the need for tailored educational interventions based on disease status, which has implications for public health and patient care in rural and underserved communities. Academic–community partnerships can be useful for successful knowledge transfer for vaccine acceptance to reduce rural health disparities. Full article

In order to solve the problem of many constraints and a complex navigation environment in the path planning of unmanned surface vehicles (USV), an improved sparrow search algorithm combining cubic chaotic map and Gaussian random walk strategy was proposed to plan it. Firstly, in the population initialisation stage, cubic chaotic map was used to replace the random generation method of the traditional sparrow search algorithm to optimise the uneven initial distribution of the population and improve the global search ability of the population. Secondly, in the late iteration of the algorithm, the standard deviation of fitness is introduced to determine whether the population is trapped in the local optimum. If true, the Gaussian random walk strategy is used to perturb the optimal individual and assist the algorithm to escape the local optimum. Thirdly, the chosen water environment is modelled, and the navigation information of the original inland electronic navigation chart (ENC) is preprocessed, gridised, and the obstacle swelling is processed. Finally, the path planning experiments of USV are carried out in an inland ENC grid environment. The experimental results show that, compared with the traditional sparrow search algorithm, the average fitness value of the path planned by improved sparrow search algorithm is reduced by 14.8% and the variance is reduced by 49.9%. The path planned by the algorithm is of good quality and high stability and, combined with ENC, it can provide a reliable path for USV. Full article

Nearshore water depth plays a crucial role in scientific research, navigation management, coastal zone protection, and coastal disaster mitigation. This study aims to address the challenge of insufficient feature extraction from remote sensing data in nearshore water depth inversion. To achieve this, a convolutional neural network with spatial location integration (CNN-SLI) is proposed. The CNN-SLI is designed to extract deep features from remote sensing data by considering the spatial dimension. In this approach, the spatial location information of pixels is utilized as two additional channels, which are concatenated with the input feature image. The resulting concatenated image data are then used as the input for the convolutional neural network. Using GF-6 remote sensing images and measured water depth data from electronic nautical charts, a nearshore water depth inversion experiment was conducted in the waters near Nanshan Port. The results of the proposed method were compared with those of the Lyzenga, MLP, and CNN models. The CNN-SLI model demonstrated outstanding performance in water depth inversion, with impressive metrics: an RMSE of 1.34 m, MAE of 0.94 m, and R² of 0.97. It outperformed all other models in terms of overall inversion accuracy and regression fit. Regardless of the water depth intervals, CNN-SLI consistently achieved the lowest RMSE and MAE values, indicating excellent performance in both shallow and deep waters. Comparative analysis with Kriging confirmed that the CNN-SLI model best matched the interpolated water depth, further establishing its superiority over the Lyzenga, MLP, and CNN models. Notably, in this study area, the CNN-SLI model exhibited significant performance advantages when trained with at least 250 samples, resulting in optimal inversion results. Accuracy evaluation on an independent dataset shows that the CNN-SLI model has better generalization ability than the Lyzenga, MLP, and CNN models under different conditions. These results demonstrate the superiority of CNN-SLI for nearshore water depth inversion and highlight the importance of integrating spatial location information into convolutional neural networks for improved performance. Full article

The aim of the research is to identify the optimal method for smoothing the surface of a hybrid digital bathymetric model (HDBM). The initiation of this research is justified by the fact that a model created from diverse types of data may have different surface textures and outliers. This diversity may cause problems in subsequent data processing stages, such as generating depth contours. As part of the adopted research methodology, fifteen filters were analysed. Filtering techniques were examined for filter type, the number of iterations, weights, and window size. The result is the adopted research methodology, which enabled the selection of the optimal filtering method. The research undertaken in this work is an extension of the methodology for developing an HDBM. An important aspect of the research is the approach to elaborating on such kinds of models in shallow and ultra-shallow waters adjacent to the land, as well as the use of data obtained by modern measurement platforms, such as unmanned surface vehicles (USV) and unmanned aerial vehicles (UAV). The studies fit into the general context of works related to the development of this type of model and undoubtedly provide a solid reference for further development or improvement of similar methods. Full article

In this study, an intelligent hybrid algorithm based on deep-reinforcement learning (DRL) is proposed to achieve autonomous navigation and intelligent collision avoidance for a smart autonomous marine surface vessel (SMASV). First, the kinematic model of the SMASV is used, and clauses 13 to 17 of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) are introduced. Then, the electronic chart is rasterized and used for path planning. Next, states, actions, and reward functions are designed, and collision avoidance strategies are formulated. In addition, a temperature factor and a constrained loss function are used to improve the soft actor–critic (SAC) algorithm. This improvement reduces the challenges of hyperparameter adjustment and improves sampling efficiency. By comparing the improved SAC algorithm with other deep-reinforcement learning (DRL) algorithms based on strategy learning, it is proved that the improved SAC algorithm converges faster than the other algorithms. During the experiment, some unknown obstacles are added to the simulation environment to verify the collision-avoidance ability of the trained SMASV. Moreover, eight sea areas are randomly selected to verify the generalization ability of the intelligent-navigation system. The results show that the proposed method can plan a path for the SMASV accurately and effectively, and the SMASV decision-making behavior in the collision-avoidance process conforms to the COLREGs in both unknown and dynamic environments. Full article