Balancing multiscale similarity and cartographic constraints: A similarity-driven optimization framework for line generalization

2026-07-28

AI Summary

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.

Abstract

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.

Problem

• cartographic generalization
• multiscale similarity
• cartographic constraints
• line generalization
• parameter optimization

Innovation

• similarity-driven optimization
• cartographic generalization
• multiscale similarity
• constraint integration
• adaptive parameter control