【摘要】 【目的】针对开放街道地图中立交桥层级结构缺失制约高精度地图与智能导航发展的问题，突破传统方法对高程或激光点云数据的强依赖，本文旨在构建一种无需高程信息、仅融合遥感影像与车辆轨迹数据的立交桥层级自动识别方法。【方法】本文提出一种融合遥感影像与车辆轨迹数据的OSM路网立交桥层级结构识别框架。首先，基于遥感影像与OSM路网的空间拓扑关系，检测道路交叠区域；通过霍夫变换提取线性特征并结合斜率比较策略，初步判别交叠道路的上下层空间关系。其次，利用车辆轨迹数据构建高斯混合模型，提取速度分布特征，采用随机森林分类器对平行重叠道路进行精细识别。最后，引入局部-全局推理算法，综合空间几何约束与轨迹行为模式，为OSM路网节点与边赋予层级属性，并实现结构可视化输出。【结果】实验在北京多个典型立交桥区域开展，结果表明：该方法在交叠道路层级判别任务中准确率达99%，召回率为89%, F1分数达94%；在重叠道路识别任务中准确率达100%，召回率为86.96%, F1分数为93.02%。相较于依赖机载LiDAR或GPS轨迹高程的现有方法，本文方法在完全不使用高程信息的前提下，不仅显著降低数据获取成本与门槛，且整体识别精度更高，展现出更强的实用性与可扩展性。【结论】本研究提出的多源数据融合框架有效实现了OSM立交桥层级结构的精细化识别，突破了对高程数据的依赖，为开源地图数据质量提升提供了可靠技术路径，可广泛应用于智能导航、自动驾驶高精地图构建及城市交通建模等领域。更多还原

【Abstract】 [Objectives] To address the issue of the missing hierarchical structure of overpasses in OpenStreetMap(OSM) that hinders the development of high-precision maps and intelligent navigation, this paper aims to break through the strong reliance of traditional methods on elevation or LiDAR point cloud data and proposes a method for automatically identifying the hierarchical structure of overpasses that only fuses remote sensing images and vehicle trajectory data without the need for elevation information. [Methods] A framework for identifying the hierarchical structure of overpasses in OSM road networks by fusing remote sensing images and vehicle trajectory data is proposed. Firstly, based on the spatial topological relationship between remote sensing images and OSM road networks, the overlapping areas of roads are detected; linear features are extracted through the Hough transform and combined with a slope comparison strategy to initially determine the spatial relationship of upper and lower layers of overlapping roads. Secondly, a Gaussian mixture model is constructed using vehicle trajectory data to extract speed distribution features, and a random forest classifier is used to accurately identify parallel overlapping roads. Finally, a local-global reasoning algorithm is introduced to assign hierarchical attributes to OSM road network nodes and edges based on spatial geometric constraints and trajectory behavior patterns, and to achieve structural visualization output. [Results] The experiments were carried out in multiple typical overpass areas in Beijing. The results show that the accuracy rate of this method in the task of discriminating overlapping road levels is 99%, the recall rate is 89%, and the F1 score is 94%; in the task of identifying overlapping roads, the accuracy rate is 100%, the recall rate is 86.96%, and the F1 score is 93.02%. Compared with the existing methods that rely on airborne LiDAR or GPS trajectory elevation, this method does not use elevation information at all, significantly reducing the cost and threshold of data acquisition, and has higher overall recognition accuracy, demonstrating stronger practicality and scalability. [Conclusions] The multi-source data fusion framework proposed in this study effectively realizes the fine-grained identification of the hierarchical structure of OSM overpasses, breaks the dependence on elevation data, and provides a reliable technical path for improving the quality of opensource map data. It can be widely applied in intelligent navigation, high-precision map construction for autonomous driving, and urban traffic modeling.更多还原