Addressing the limitations of current multi-scale seabed terrain construction methods for a Digital Depth Model (DDM) and the low computational efficiency of automatic generalization algorithms, this paper draws on the concept of curvature simplification from 3D point cloud data processing and proposes a block-based DDM automatic generalization method that leverages surface curvature features. Initially, a clustering blocking model is established using an improved K-means algorithm for partitioning DDM data. Subsequently, a fitting surface is constructed based on the neighboring depth points within the blocked DDM to obtain the surface curvature characteristics of each depth point, which serve as the criterion for the DDM automatic generalization process. By integrating a multi-threaded parallel computation model, an efficient automated generalization workflow that encompasses data partitioning, fitting, computation, processing, and integration of the DDM is ultimately constructed. Furthermore, this paper designs validity and comparative experiments to analyze the proposed algorithm through experimental analysis. The results demonstrate that the algorithm can be applied to the multi-scale construction of DDM seabed terrain, while maintaining the integrity of both flat and complex seabed landforms, and significantly enhancing the computational efficiency of the DDM automatic generalization process.

Shallow water bathymetry data underpins maritime shipping and marine resource survey/protection, but its accuracy is constrained by water heterogeneity and spectral interference. To address this, this study proposes a Spatio-Temporal Coupling and Correlation Adaptive Spectral (STCCAS) inversion method, integrating four machine learning models: Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Experiments were conducted in Tampa Bay’s nearshore waters, using Sentinel-2 imagery and Airborne LiDAR Bathymetry (ALB) data. Core to STCCAS, the Temporal Stability Index (TSI) quantifies spectral temporal consistency, while the Normalized Difference Turbidity Index (NDTI) characterizes water turbidity, and the two indices synergistically form a dual-scale “spectral reliability-turbidity stability” evaluation system for pixel-level feature quality assessment—coupled with spectral fusion features and spatial location, they jointly realize pixel-level feature reliability weighting and dynamic filtering to build a water condition-adaptive input set. Comparative analysis of inversion performance under the original spectral features (OSFs) inversion method vs. STCCAS inversion method confirms STCCAS significantly boosts accuracy. XGBoost outperforms others, achieving a coefficient of determination (R²) of 0.93, root mean square error (RMSE) of 0.16 m, and mean absolute error (MAE) of 0.12 m. STCCAS breaks the limitations of traditional fixed feature combinations, effectively adapting to nearshore water heterogeneity. It provides a novel method for high-frequency, high-precision shallow water bathymetry inversion, with important practical value for marine environmental monitoring and resource management.

Aiming at the problem that the current double direction buffering algorithm is easy to use to seal the “bottleneck” area when simplifying coastlines, an adaptive simplification method for coastlines using a skeleton line “bridge” double direction buffering algorithm is proposed. Firstly, from the perspective of visual constraints, the relationship between the buffer distance and the coastline line width and the minimum recognition distance of the human eye is theoretically derived and determined. Then, based on the construction of the coastline skeleton binary tree, the “bridge” skeleton line is extracted using the “source tracing” algorithm. Finally, the shoreline adaptive simplification is realized by constructing a visual buffer of “bridge” skeleton lines to bridge the original resulting coastline and the local details. The experimental results show that the proposed method can effectively solve the problem that the current double direction buffering algorithm has, which can significantly improve the quality of simplification.

In current practices of determining continental shelf area, the measured sediment thickness data do not effectively reflect the distribution of sediments across the area due to its dispersed nature. This issue raises potential limitations in unknown optimal survey line layout strategies for maximizing the posterior determination area. This paper adopts the binary search algorithm, relies on existing sediment thickness grid data, and uses geodetic formulas to build an ellipsoidal surface grid distance calculation model. This model quickly screens potential areas for the 1% sediment thickness line candidate points set. By constraining the azimuth parameter values during the construction process of the ellipsoidal point feature buffer zones, efficiently select the candidate points set for the 1% sediment thickness line. Furthermore, by elucidating the essential meanings of points on the formula limit and combining the candidate points set of the foot of the continental slope (FOS)+60 n mile line, the polygon minimal convex hull construction technique and a baseline points optimization algorithm with a length threshold are used to efficiently select points on formula limit. Experimental results demonstrate that this method can effectively assist coastal states in optimizing the determination of continental shelf area to the fullest extent under the length threshold requirements of the United Nations Convention on the Law of the Sea. Experiments have proven that compared to the traditional intersection method, the method presented in this paper can help coastal countries delineate a larger continental shelf area. In typical application scenarios, the gain in area can reach 77,278,427 m² accounting for 0.51% of the total area.

With the increasing demand for high-precision and difficult-to-obtain geospatial point cloud data copyright protection in military, scientific research, and other fields, research on lossless watermarking is receiving more and more attention. However, most of the current geospatial point cloud data watermarking algorithms embed copyright information by modifying vertex coordinate values, which not only damages the data accuracy and quality but may also cause incalculable losses to data users. To maintain data fidelity and protect its copyright, in this paper, we propose a lossless embedded watermarking algorithm based on vertical stability. First, the watermark information is generated based on the binary encoding of the copyright information and the code of the traceability information. Second, the watermark index is calculated based on the length of the watermark information after compression and the vertical distribution characteristics of the data. Finally, watermark embedding is completed by modifying the relative storage order of the corresponding data according to the index and watermark value. The experimental results show that the proposed algorithm has good invisibility without damaging the data accuracy. In addition, compared with existing algorithms, this method has a higher robustness under operations such as projection transformation, precision perturbation, and vertex deletion of geospatial point cloud data.

This study investigates the demand for high-precision trajectory similarity assessment in intelligent maritime navigation. This is done by analyzing discrepancies between GPS-derived trajectories and actual vessel paths, while identifying critical limitations in existing evaluation methods. To address these challenges, we propose a robust framework that integrates three core innovations: firstly, a linear feature accuracy-constrained resampling method to ensure computational precision under diverse complexity conditions, validated through experimental verification; secondly, a shape feature extraction and transformation protocol designed to maintain consistency across multi-scale and heterogeneous operational scenarios; thirdly, a quantitative similarity evaluation criterion based on extracted shape characteristics, enabling systematic alignment between localized trajectory segments and historical navigation patterns. The experimental results confirm the method’s enhanced robustness and its capability to bridge local and global trajectory comparisons, demonstrating that shape-driven quantification significantly refines similarity analysis. This approach advances intelligent maritime systems by providing a technically rigorous solution for real-time decision support and actionable insights into next-generation navigation applications.