An Automatic Generalization Method of a Block-Based Digital Depth Model Based on Surface Curvature Features

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.