史前人类迁徙中高效路径重建：基于地形数据小波压缩的自适应迪杰斯特拉算法
Efficient Path Reconstruction in Prehistoric Human Migration: An Adaptive Dijkstra's Algorithm Based on Wavelet Compression for Topographic Data
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中文总结 AI 辅助
研究史前人类迁徙路径重建问题，提出基于自适应小波方法压缩地形数据，加速迪杰斯特拉算法，在不丢失关键拓扑的情况下显著提升计算效率，通过迁徙场景验证了该方法的有效性。
中文摘要 AI 辅助
史前迁徙路线的重建需要准确评估跨越复杂地形的有效距离。传统使用高分辨率高程模型的最小成本路径分析（LCPA）常导致密集计算网格，使迪杰斯特拉等算法在大规模考古建模中运行极慢。本文提出一种利用自适应小波方法动态压缩地形数据的新方法。通过仅在地形复杂区域保留高分辨率并平滑均匀区域，显著加速迪杰斯特拉算法且不丢失关键路由拓扑。利用迁徙场景证明了该方法的有效性。
英文摘要
The reconstruction of prehistoric migration routes requires the accurate evaluation of effective distances across complex topographies. Traditional Least-Cost Path Analyses (LCPA) using high-resolution elevation models often lead to dense computational grids, making algorithms like Dijkstra's prohibitively slow for large-scale archaeological modelling. In this paper, we present a novel approach using adaptive wavelet methods to dynamically compress topographic data. By retaining high resolution only in areas of high topographic complexity (e.g., mountain passes) and smoothing homogeneous regions, we significantly accelerate the Dijkstra algorithm without losing essential routing topology. We demonstrate the efficacy of this method using migration scenarios.
