Balancing multiscale similarity and cartographic constraints: A similarity-driven optimization framework for line generalization
This study addresses the challenge that existing automated map generalization methods struggle to jointly preserve spatial similarity and cartographic legibility across multiple scales, often treating similarity assessment, constraint modeling, and parameter optimization in isolation. To overcome this limitation, the authors propose a unified similarity-driven framework that formulates map generalization as a constrained multi-scale similarity optimization problem. For the first time, geometric, structural, and learned similarity measures are integrated into the objective function, while cartographic constraints—including legibility, smoothness, and geometric validity—are incorporated through line simplification algorithms. Experimental results demonstrate that the approach adaptively and consistently optimizes parameter configurations across diverse algorithms and scales, achieving high-quality map abstraction that maintains spatial similarity while significantly improving the interpretability and generalizability of parameter control.