An Adaptive Generalization Method for Complex Shoreline Segments in Nautical Charts Based on Bend Sequence Attention Learning

To address the challenges posed by significant variations in bend morphology and spatial relationships within complex shoreline segments—where single generalization operators and uniform parameters struggle to balance node compression with morphological preservation—this paper proposes an adaptive shoreline generalization method based on bend sequence attention learning. Treating bend units as local decision objects, the method integrates geometric morphology, proximity distance, and shoreline type features. It employs bidirectional long short-term memory (BiLSTM) and self-attention mechanisms to learn contextual relationships among bends within each shoreline and predict local generalization operators and their parameters. Proximity links between shorelines support spatial consistency checks during constraint-based correction. After operator execution, candidate geometries that violate cartographic constraints undergo parameter rollback or operator reselection; unresolved bends are restored to their original geometry before output. Under the evaluated datasets, scoring procedure, and compression settings, the final generalized shorelines achieved a vertex-reduction ratio of 86.43% and a bend-retention ratio of 94.84%. These results support the feasibility of bend-level adaptive operator selection at the evaluated compression–preservation operating point. Performance across other compression settings and chart series remains to be established.