Search Results (42)

The generalization of nautical charts remains crucial in geographic information science and cartography. Traditional geometry-based methods have contributed to the advancement of automated generalization to a certain extent, but they still exhibit significant limitations in handling complex marine spatial relationships. This paper proposes the Graph Neural Network (GNN) as a transformative solution. GNN excels at processing non-Euclidean geospatial data, addressing the following three critical problems in the generalization of submarine terrain data: geographic feature representation, data processing, and the generalization process. The review first systematically outlines the main operators and fundamental methods of chart generalization. It analyzes their specific performance in various elements such as soundings, depth contours, islands, and coastlines. Subsequently, the potential of GNN is explored in addressing the limitations of traditional generalization methods. Although GNN is not a panacea, it shows advantages through horizontal and vertical comparisons. Finally, the challenges encountered in applying GNN to cartographic generalization are discussed. Full article

The oceans remain one of Earth’s last great unknowns, with about 74% still unmapped to modern standards. Consequently, interpolation is employed to create seamless digital bathymetric models (DBMs) from incomplete hydrographic datasets, but this introduces unquantified depth uncertainties. This study aims to estimate and characterize uncertainties arising from set-line spacing hydrographic surveys, which are important for nautical charting, navigational safety, and many other applications. By sampling four distinct complete-coverage testbeds in United States waters that vary in slope and roughness at different line spacings, this study interpolates across entire testbed areas using Spline, Inverse Distance Weighting, and Linear interpolation. Uncertainty is calculated by comparing interpolated depths against the source depths for independent points. The resulting interpolation uncertainties are evaluated from both scientific and operational perspectives. Linear regression and machine learning techniques, specifically artificial neural networks and random forest, are used to model the relationship between these uncertainties and three ancillary predictors (distance to the nearest known measurement, slope, and roughness) for interpolation uncertainty quantification. The results show operational equivalence among the three interpolators, how line spacing and morphology impact uncertainty, and the statistical significance of the examined uncertainty predictors. However, the relationships between the combined ancillary predictors and interpolation uncertainty are weak. These findings suggest the potential presence of unaccounted-for factors influencing uncertainty yet provide a foundational understanding for improving uncertainty estimates in DBMs within operational settings. Full article

The precise acquisition of water depth data in nearshore shallow waters bears considerable strategic significance for marine environmental monitoring, resource stewardship, navigational infrastructure development, and military security. Conventional bathymetric survey methodologies are constrained by their spatial and temporal limitations, thus failing to satisfy the requirements of large-scale, real-time surveillance. While satellite remote sensing technologies present a novel approach to water depth inversion in shallow waters, attaining high-precision inversion in nearshore areas characterized by elevated levels of suspended sediments and diminished transparency remains a formidable challenge. To tackle this issue, this study introduces an enhanced XGBoost model grounded in the Newton–Raphson optimizer (NRBO–XGBoost) and successfully applies it to water depth inversion investigations in the nearshore shallow waters of the Beibu Gulf. The research amalgamates Sentinel-2B multispectral imagery, nautical chart data, and in situ water depth measurements. By ingeniously integrating the Newton–Raphson optimizer with the XGBoost framework, the study realizes the automatic configuration of model training parameters, markedly elevating inversion accuracy. The findings reveal that the NRBO–XGBoost model attains a coefficient of determination (R²) of 0.85 when compared to nautical chart water depth data, alongside a scatter index (SI) of 21%, substantially surpassing conventional models. Additional validation analyses indicate that the model achieves a coefficient of determination (R²) of 0.86 with field-measured data, a mean absolute error (MAE) of 1.60 m, a root mean square error (RMSE) of 2.13 m, and a scatter index (SI) of 13%. Moreover, the model exhibits exceptional performance in extended applications within the waters of Zhanjiang Port (R² = 0.90), unequivocally affirming its dependability and practicality in intricate nearshore water environments. This study not only provides a fresh solution for remotely sensing water depth in complex nearshore water settings but also imparts valuable technical insights into the associated underwater surveys and marine resource exploitation. 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

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

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSE_z), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSE_z values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with ${\mathrm{R}}^2$ values reaching up to 0.97. 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

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 hydrographic survey is an important technology for improving maritime safety, especially in coastal waters. The accuracy of nautical charts and navigation publications is known to be directly related to hydrographic survey data. Therefore, this paper aims to investigate the status of a hydrographic survey by the International Hydrographic Organization (IHO) regions and identify the potentially risky IHO region for navigation safety. The fundamental step was to obtain the qualitative and quantitative data of the survey. Then, the presented analysis includes investigating the possible relationships between survey status and geographical characteristics by IHO regions. Considering that coastline length and sea surface data have not been calculated by regions, a quantum geographic information system was used to extract data. Using the presented methodology, the case study analyzes the data of stranded ships from 2010 to 2021 by IHO regions, estimates coastline length and sea surface by regions, and establishes the relationships between the coastline length, sea surface, and stranded ships. The results point out the need for improvement in the state of the hydrographic survey in almost all IHO regions and show a correlation between the sea surface and an adequate survey, as well as the coastline length and stranded ships. Hence, this research indicates the possibility of rationalizing the distribution of the IHO region concerning the sea surface and coastline length. Full article

Spectrally derived bathymetry (SDB) algorithms are rapidly gaining in acceptance and widespread use for nearshore bathymetric mapping. In the past, refraction correction could generally be ignored in SDB, due to the relatively small fields of view (FOVs) of satellite sensors, and the fact that such corrections were typically small in relation to the uncertainties in the output bathymetry. However, the validity of ignoring refraction correction in SDB is now called into question, due to the ever-improving accuracies of SDB, the desire to use the data in nautical charting workflows, and the application of SDB algorithms to airborne cameras with wide FOVs. This study tests the hypothesis that refraction correction leads to a statistically significant improvement in the accuracy of SDB using uncrewed aircraft system (UAS) imagery. A straightforward procedure for SDB refraction correction, implemented as a modification to the well-known Stumpf algorithm, is presented and applied to imagery collected from a commercially available UAS in two study sites in the Florida Keys, U.S.A. The results show that the refraction correction produces a statistically significant improvement in accuracy, with a reduction in bias of 46–75%, a reduction in RMSE of 3–11 cm, and error distributions closer to Gaussian. Full article

The presence of wave coherence, which contributes to the inhomogeneity of wave characteristics and significantly affects wave processes over nearshore regions of the Yellow River Delta (YRD), was simulated and analyzed in this study. A phase-resolving Boussinesq-type wave model, FUNWAVE-TVD, was used to simulate waves with desirable coherency effects. Bathymetry and topography data were obtained from the Chinese nautical chart and E.U. Copernicus Marine Service Information. After the model configuration, spatial distributions of the root mean square and significant wave heights, and the maximum cross-shore current velocity and vorticity over the domain with respect to different degrees of wave coherence and energy spectrum discretization were investigated. The results indicate that the complexity of the spatial distribution and magnitude of longshore variations in wave statistics are proportional to the degree of coherence. Waves with higher coherency exhibit more complex variabilities and stronger fluctuations along the longshore direction. The influence of morphological changes on wave height in the YRD was discernible by comparing the results with and without coherency effects. The cross-shore current velocity decreased as the waves moved toward the surf zone, while the vorticity accelerated, indicating a higher shear wave magnitude. The simulated wave dissipates more than 60% (80%) of its energy when it reaches water depths of less than 5 m (2 m) and completely dissipates when it breaks at the shore. Full article

Previously, paired spits have been described at the mouths of bays, estuaries, and deltas. This study analyzed the worldwide distribution and morphodynamic patterns of paired spits located at the mouths of interdistributary bays of deltas (three systems) and within coastal channels (24 systems). The methodology was based on the detailed analysis of satellite images, nautical charts, and tidal-range databases. The paired spits found were mainly located on microtidal coasts at high or mid latitudes. Waves were the main factor controlling convergent progradation and breaching of the spits, while the hydraulic blockage for the development of these paired spits was mainly due to tide-induced currents, as well as minor fluvial outlets in the interdistributary bays. Three morphodynamic patterns were identified: (i) stable, with low progradation rates, generally without breaching or degradation of any of the spits; (ii) stationary, with high progradation rates, alternating degradation or breaching of any of the spits with the formation of new spits or closure of the breaches; and (iii) instable or ephemeral, which included three subtypes, the severe erosion of one or both spits, the joining of the head of the two spits forming a single barrier, and the merging of each with its channel margin. Full article

Maritime transportation is vital to the global economy. With the increased operating and labor costs of maritime transportation, autonomous shipping has attracted much attention in both industry and academia. Autonomous shipping can not only reduce the marine accidents caused by human factors but also save labor costs. Path planning is one of the key technologies to enable the autonomy of ships. However, mainstream ship path planning focuses on searching for the shortest path and controlling the vehicle in order to track it. Such path planning methods may lead to a dynamically infeasible trajectory that fails to avoid obstacles or reduces fuel efficiency. This paper presents a data-driven, efficient, and safe path planning (ESP) method that considers ship dynamics to provide a real-time optimal trajectory generation. The optimization objectives include fuel consumption and trajectory smoothness. Furthermore, ESP is capable of fast replanning when encountering obstacles. ESP consists of three components: (1) A path search method that finds an optimal search path with the minimum number of sharp turns from the geographic data collected by the geographic information system (GIS); (2) a minimum-snap trajectory optimization formulation with dynamic ship constraints to provide a smooth and collision-free trajectory with minimal fuel consumption; (3) a local trajectory replanner based on B-spline to avoid unexpected obstacles in real time. We evaluate the performance of ESP by data-driven simulations. The geographical data have been collected and updated from GIS. The results show that ESP can plan a global trajectory with safety, minimal turning points, and minimal fuel consumption based on the maritime information provided by nautical charts. With the long-range perception of onboard radars, the ship can avoid unexpected obstacles in real time on the planned global course. Full article

Electronic Navigational Chart (ENC) data are essential for safe maritime navigation and have multiple other uses in a wide range of enterprises. Charts are relied upon to be as accurate and as up-to-date as possible by the vessels moving vast amounts of products to global ports each year. However, cartographic generalization processes for updating and creating ENCs are complex and time-consuming. Increasing the efficiency of the chart production workflow has been long sought by the nautical charting community. Toward this effort, approaches must consider intended scale, data quality, various chart features, and perform consistently in different scenarios. Additionally, supporting open-science initiatives through standardized open-source workflows will increase marine data accessibility for other disciplines. Therefore, this paper reviews, improves, and integrates available open-source software, and develops new custom generalization tools, for the semi-automated processing of land and hydrographic features per nautical charting specifications. The robustness of this approach is demonstrated in two areas of very different geographic configurations and the effectiveness for use in nautical charting was confirmed by winning the first prize in an international competition. The presented rapid data processing combined with the ENC portrayal of results as a web-service provides new opportunities for applications such as the development of base-maps for marine spatial data infrastructures. Full article

This research is a pilot study to develop a maritime traffic control system that supports the decision-making process of control officers, and to evaluate the usability of a prototype tool developed in this study. The study analyzed the movements of multiple vessels through automatic identification system (AIS) data using one of the existing methodologies in GIScience, the RElative MOtion (REMO) approach. The REMO approach in this study measured the relative speed, delta-speed, and the azimuth of each vessel per time unit. The study visualized the results on electronic navigational charts in the prototype tool developed, V-REMO. In addition, the study conducted a user evaluation to assess the user interface (UI) of V-REMO and to future enhance the usability. The general usability of V-REMO, the data visualization, and the readability of information in the UI were tested through in-depth interviews. The results of the user evaluation showed that the users needed changes in the size, position, colors, and transparency of the trajectory symbols in the digital chartmap view of V-REMO for better readability and easier manipulation. The users also indicated a need for multiple color schemes for the spatial data and more landmark information about the study area in the chartmap view. Full article