Balancing Multiscale Similarity and Cartographic Constraints: A Similarity-Driven Optimization Framework for Line Generalization

This paper formulates cartographic line generalization as a constrained multiscale similarity optimization problem. Its framework combines geometric, structural, and learning-based similarity measures with constraints for readability, smoothness, and geometric validity, then searches for scale-dependent parameter settings across different line simplification algorithms. The abstract reports experiments covering multiple algorithms, target scales, and similarity measures, and claims that jointly optimizing similarity and cartographic constraints produces more consistent and interpretable parameter control than using similarity evaluation alone. Specific datasets, metrics, ablations, and numerical gains are not included in the supplied abstract.