Submarine Terrain Generalization in Nautical Charts: A Survey of Traditional Methods and Graph Neural Network Solutions

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.