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This study addresses the challenges of automated cartographic generalization by formulating it as a constrained multiscale similarity optimization problem, integrating spatial similarity and cartographic constraints into a unified framework. The proposed similarity-driven optimization framework allows for adaptive control over generalization parameters, enhancing the balance between information preservation and readability across different scales. Experimental results demonstrate that this approach yields more consistent and interpretable parameter configurations compared to traditional methods, significantly improving the effectiveness of line simplification algorithms in cartographic applications.
Combining multiscale similarity optimization with cartographic constraints revolutionizes how we achieve adaptive and interpretable map generalization.
Cartographic generalization is essential for generating multiscale map representations by balancing information preservation and cartographic readability. However, automated generalization remains challenging because existing approaches often treat spatial similarity evaluation, cartographic constraints, and parameter optimization as separate processes, limiting adaptive and interpretable control across scales. This study formulates cartographic generalization as a constrained multiscale similarity optimization problem and proposes a similarity-driven framework for adaptive generalization control. The framework integrates multiscale spatial similarity as an optimization objective to quantify representation consistency between original and generalized data, while incorporating cartographic constraints to regulate readability, smoothness, and geometric validity. A unified objective function is optimized to automatically identify scale-dependent parameter configurations for different generalization algorithms. Experiments using multiple line simplification algorithms, target scales, and similarity measures, including geometric, structural, and learning-based metrics, demonstrate that the proposed framework achieves an effective balance between similarity preservation and cartographic abstraction. The results further show that combining similarity optimization with cartographic constraints provides more consistent and interpretable parameter control than relying on similarity evaluation alone. This study provides a unified optimization perspective that connects similarity assessment, constraint modeling, and algorithm control, contributing to adaptive and automated cartographic generalization.
