AI 中文总结
研究针对制图综合中自动综合的挑战，提出相似性驱动框架，将多尺度空间相似性作优化目标，纳入制图约束，通过优化统一目标函数为化简算法识别参数配置，实验表明该框架平衡了相似性保留与制图抽象，实现更优参数控制。
AI 中文摘要
制图综合对于通过平衡信息保留和制图可读性来生成多尺度地图表示至关重要。然而，自动综合仍然具有挑战性，因为现有方法通常将空间相似性评估、制图约束和参数优化视为单独的过程，限制了跨尺度的自适应和可解释控制。本研究将制图综合表述为一个受约束的多尺度相似性优化问题，并提出了一个用于自适应综合控制的相似性驱动框架。该框架将多尺度空间相似性作为优化目标，以量化原始数据和化简后数据之间的表示一致性，同时纳入制图约束以调节可读性、平滑性和几何有效性。通过优化一个统一的目标函数，自动为不同的化简算法识别依赖于尺度的参数配置。使用多种线化简算法、目标尺度和相似性度量（包括几何、结构和基于学习的度量）进行的实验表明，所提出的框架在相似性保留和制图抽象之间实现了有效平衡。结果还表明，将相似性优化与制图约束相结合比仅依赖相似性评估提供了更一致和可解释的参数控制。本研究提供了一个统一的优化视角，将相似性评估、约束建模和算法控制联系起来，有助于实现自适应和自动的制图综合。
英文摘要
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.
CommentsSubmitted to Cartography and Geographic Information Science
