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.

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.

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.