LLM驱动的免训练位置-属性协同融合：双源加密POI与LULC制图的闭环范式

中文摘要 AI 辅助

双源加密兴趣点（DSEP），即来自两个加密坐标系的POI，存在相互交织的位置和属性不确定性，包括非线性系统错位和命名不一致，阻碍了土地利用/土地覆盖（LULC）制图。据我们所知，本文首次提出了一种LLM驱动的、免训练的位置-属性协同闭环优化范式，用于DSEP融合。该范式通过迭代反馈联合优化位置变换和属性对应关系。属性协同位置融合使用LLM驱动的属性匹配方法建立DSEP对应关系，将匹配复杂度从O(N^2)降低到O(N)，并在ISODATA聚类局部子区域内使用改进的粒子群优化算法细化变换系数。位置协同属性融合随后通过LLM-模糊方法根据更新的几何残差重新评估属性置信度。细化后的对应关系反馈到位置优化中，形成双向闭环。样本纯化和自适应半径收缩使得算法在基本两次迭代内收敛。我们进一步提出了一种免训练的LULC制图方法，通过位置融合从加密地图继承土地利用类别，生成矢量-栅格集成的LULC地图。在中国大陆31个省会城市和直辖市应用了一种无参考的POI融合评估方法。实验表明，我们的方法实现了平均DSEP位置融合残差4.58米和属性融合精度95.12%，分别比开源基线和最先进方法提高了1.77米和14.87%。总体而言，该方法为DSEP融合提供了免训练解决方案，并能够在无需实地测量的地面控制点的情况下，将加密矢量数据地理配准到WGS-84。

英文摘要

Dual-source encrypted points of interest (DSEP), POIs from two encrypted coordinate systems, suffer from intertwined location and attribute uncertainties, including nonlinear systematic misalignment and naming inconsistency, hindering land-use/land-cover (LULC) mapping. To the best of our knowledge, this paper is the first to propose an LLM-driven, training-free location-attribute synergic closed-loop optimization paradigm for DSEP fusion. The paradigm jointly refines location transformation and attribute correspondences through iterative feedback. Attribute-synergic location fusion uses an LLM-driven attribute matching method to establish DSEP correspondences, reducing matching complexity from O(N^2) to O(N), and refines transformation coefficients using an improved particle swarm optimization algorithm within ISODATA-clustered local subregions. Location-synergic attribute fusion then reassesses attribute confidence from updated geometric residuals through an LLM-fuzzy method. The refined correspondences feed back into location optimization, forming a bidirectional closed loop. Sample purification and adaptive radius contraction enable convergence in essentially two iterations. We further propose a training-free LULC mapping method that inherits land-use classes from encrypted maps through location fusion, producing vector-raster integrated LULC maps. A reference-free POI fusion evaluation method is applied across 31 provincial capitals and municipalities in mainland China. Experiments show that our method achieves an average DSEP location fusion residual of 4.58 m and attribute fusion accuracy of 95.12%, improving upon the open-source baseline and state-of-the-art method by 1.77 m and 14.87%, respectively. Overall, the method provides a training-free solution for DSEP fusion and enables georeferencing of encrypted vector data to WGS-84 without field-surveyed ground control points.

发表机构

• Central China Normal University（华中师范大学）
• Wuhan University（武汉大学）

机构由 AI 辅助整理，请以论文原文为准。