融合轨迹和遥感影像的立交桥精细结构提取方法

doi:10.11947/j.AGCS.2026.20260159

测绘学报 ›› 2026, Vol. 55 ›› Issue (8): 1482-1495.doi: 10.11947/j.AGCS.2026.20260159

• 地图学与地理信息 • 上一篇

融合轨迹和遥感影像的立交桥精细结构提取方法

李雅丽¹(), 郝悦竹¹, 向隆刚²(), 张彩丽³, 刘茂华¹

^1.沈阳建筑大学交通与测绘工程学院，辽宁　沈阳　110000
^2.武汉大学测绘遥感信息工程全国重点实验室，湖北　武汉　430079
^3.河南城建学院测绘与城市空间信息学院，河南　平顶山　467000

收稿日期:2026-04-27 修回日期:2026-06-17 发布日期:2026-09-09
通讯作者: 向隆刚 E-mail:lyllhjgbx@163.com;geoxlg@whu.edu.cn
作者简介:李雅丽（1992—），女，博士，讲师，研究方向为多源时空数据融合与分析、人工智能。E-mail：lyllhjgbx@163.com
基金资助:
辽宁省教育厅高校基本科研项目(LJ212410153040);辽宁省住房和城乡建设厅科学技术计划(LNSJSKJ-2026-041);国家自然科学基金(42471460)

Fine structure extraction method of overpass based on trajectory and remote sensing image fusion

Yali Li¹(), Yuezhu Hao¹, Longgang Xiang²(), Caili Zhang³, Maohua Liu¹

^1.School of Transportation and Surveying Engineering, Shenyang Jianzhu University, Shenyang 110000, China
^2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
^3.School of Geomatics and Urban Spatial Information, Henan University of Urban Construction, Pingdingshan 467000, China

Received:2026-04-27 Revised:2026-06-17 Published:2026-09-09
Contact: Longgang Xiang E-mail:lyllhjgbx@163.com;geoxlg@whu.edu.cn
About author:Li Yali (1992—), female, PhD, lecturer, majors in multi-source spatio-temporal data fusion and analysis, and artificial intelligence. E-mail: lyllhjgbx@163.com
Supported by:
Liaoning Provincial Department of Education Basic Research Project for Universities(LJ212410153040);Science and Technology Planning Project of the Liaoning Provincial Department of Housing and Urban-Rural Development(LNSJSKJ-2026-041);The National Natural Science Foundation of China(42471460)

摘要/Abstract

摘要：

立交桥是城市立体交通网络的关键节点，其精细结构提取对高精度地图构建与导航路径规划具有重要意义。现有轨迹建网方法易受轨迹稀疏与上下层投影重叠影响，产生道路断裂和层间误连；遥感影像建网方法受二维正射投影和遮挡影响，难以准确表达立交桥层级拓扑关系。为此，本文提出了一种融合众源轨迹与遥感影像的立交桥精细结构提取方法。首先，对众源轨迹数据进行预处理，经密度栅格生成、骨架提取与矢量化建立初始道路中心线，并对其进行等间距离散化采样以构建初始几何节点。然后，针对轨迹稀疏导致的路网断裂问题，以轨迹几何节点为几何先验，融合遥感影像提取的候选节点构建候选节点集，进而利用SAM-Road遥感影像特征进行局部连通关系推理，实现断裂修复。最后，针对垂直重叠区产生的伪平面交叉问题，提出了基于几何连续性的拓扑纠正方法，通过余弦相似度判别对向延伸向量对以识别伪节点，经伪节点剔除与路径重构实现上下层路网的拓扑解耦。基于北京市众源轨迹与遥感影像进行试验，结果表明本文方法能有效提取立交桥精细结构，几何精度指标（GEO-F₁值）和拓扑正确性指标（TOPO-F₁值）分别达到0.922 0和0.933 1，整体提取质量优于单一数据源对比方法。

关键词: 立交桥, 众源轨迹数据, 遥感影像, 拓扑纠正, 路网构建

Abstract:

Interchanges are critical components of urban grade-separated transportation networks, and the extraction of their fine-grained structures is essential for high-precision map construction and navigation route planning. Existing trajectory-based road network construction methods are susceptible to trajectory sparsity and projection overlap between upper-and lower-level roads, often resulting in road discontinuities and erroneous inter-level connections. Remote-sensing-image-based methods, constrained by two-dimensional orthographic projection and occlusion, have difficulty accurately representing the hierarchical topology of interchanges. To address these issues, this paper proposes a fine-grained interchange structure extraction method that integrates crowdsourced trajectories and remote sensing imagery. First, the crowdsourced trajectory data are preprocessed, and an initial road centerline is generated through density raster construction, skeleton extraction, and vectorization. The centerline is then sampled at equal intervals to construct initial geometric nodes. Next, to restore road network discontinuities caused by sparse trajectories, trajectory-derived geometric nodes are used as geometric priors and fused with candidate nodes extracted from remote sensing imagery to form a unified candidate node set. SAM-Road image features are subsequently employed to infer local connectivity and restore missing road connections. Finally, a topology correction method based on geometric continuity is developed to address pseudo-planar intersections in vertically overlapping areas. Pseudo-nodes are identified by evaluating pairs of oppositely directed extension vectors using cosine similarity, and the upper-and lower-level road networks are topologically decoupled through pseudo-node removal and path reconstruction. Experiments conducted using crowdsourced trajectories and remote sensing imagery from Beijing demonstrate that the proposed method effectively extracts fine-grained interchange structures. The geometric accuracy (GEO-F₁ score) and topological correctness (TOPO-F₁ score) reach 0.922 0 and 0.933 1, respectively, and the overall extraction performance surpasses that of the compared single-source methods.

Key words: overpass, crowdsourced trajectory data, remote sensing image, topological correction, road network construction

中图分类号:

P208

李雅丽, 郝悦竹, 向隆刚, 张彩丽, 刘茂华. 融合轨迹和遥感影像的立交桥精细结构提取方法[J]. 测绘学报, 2026, 55(8): 1482-1495.

Yali Li, Yuezhu Hao, Longgang Xiang, Caili Zhang, Maohua Liu. Fine structure extraction method of overpass based on trajectory and remote sensing image fusion[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(8): 1482-1495.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20260159

http://xb.chinasmp.com/CN/Y2026/V55/I8/1482

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立交桥	精确率	召回率	GEO-F₁值	TOPO-F₁值
丽泽桥	0.939 8	0.905 1	0.922 1	0.934 7
健翔桥	0.971 9	0.922 5	0.946 5	0.952 5
建国门桥	0.901 3	0.847 5	0.873 6	0.896 6
四元桥	0.888 4	0.891 4	0.889 9	0.906 8
新兴桥	0.918 9	0.905 8	0.912 3	0.908 7
莲花桥	0.956 7	0.938 6	0.947 6	0.957 5
安慧桥	0.978 8	0.973 9	0.976 3	0.980 4
定慧桥	0.890 2	0.925 3	0.907 4	0.927 2

判别阈值τ	对应夹角l/（°）	GEO-F₁值	TOPO-F₁值
0.75	138.6	0.878 5	0.895 6
0.85	148.2	0.889 9	0.906 8
0.95	161.8	0.886 7	0.899 4

立交桥	无影像候选点补充		无拓扑纠正		无地图匹配修正
立交桥	GEO-F₁值	TOPO-F₁值	GEO-F₁值	TOPO-F₁值	GEO-F₁值	TOPO-F₁值
丽泽桥	0.866 3	0.881 3	0.886 0	0.897 9	0.913 4	0.922 2
健翔桥	0.935 5	0.940 0	0.925 3	0.931 5	0.944 8	0.948 5
四元桥	0.825 9	0.858 2	0.859 8	0.890 1	0.864 6	0.891 2
新兴桥	0.872 9	0.866 8	0.889 5	0.883 4	0.905 1	0.901 6
安慧桥	0.894 8	0.909 8	0.967 6	0.978 8	0.966 2	0.974 2

立交桥	方法	精确率	召回率	GEO-F₁值	TOPO-F₁值
丽泽桥	RoadRunner	0.898 2	0.509 7	0.650 4	0.681 7
	TrackFusion	0.977 6	0.878 3	0.925 3	0.925 8
	Sat2Graph	0.389 6	0.239 8	0.296 9	0.443 7
	OSM	0.969 1	0.903 4	0.935 1	0.946 7
	本文方法	0.939 8	0.905 1	0.922 1	0.934 7
健翔桥	RoadRunner	0.949 7	0.565 4	0.708 8	0.719 6
	TrackFusion	0.969 0	0.864 6	0.913 9	0.922 1
	Sat2Graph	0.681 2	0.278 2	0.395 1	0.446 2
	OSM	0.969 1	0.956 1	0.962 6	0.971 0
	本文方法	0.971 9	0.922 5	0.946 5	0.952 5
四元桥	RoadRunner	0.787 3	0.600 0	0.681 0	0.719 0
	TrackFusion	0.860 2	0.816 1	0.837 6	0.849 5
	Sat2Graph	0.358 9	0.311 4	0.333 5	0.458 3
	OSM	0.935 8	0.826 9	0.878 0	0.886 3
	本文方法	0.888 4	0.891 4	0.889 9	0.906 8
新兴桥	RoadRunner	0.981 1	0.745 5	0.847 2	0.859 1
	TrackFusion	0.971 2	0.742 2	0.841 4	0.853 5
	Sat2Graph	0.424 9	0.329 7	0.371 3	0.555 6
	OSM	0.977 7	0.880 8	0.926 7	0.891 2
	本文方法	0.918 9	0.905 8	0.912 3	0.908 7
安慧桥	RoadRunner	0.970 1	0.773 1	0.860 4	0.873 5
	TrackFusion	0.961 0	0.805 4	0.876 3	0.887 4
	Sat2Graph	0.527 7	0.150 4	0.234 1	0.326 3
	OSM	0.977 5	0.974 5	0.976 0	0.975 2
	本文方法	0.978 8	0.973 9	0.976 3	0.980 4

融合轨迹和遥感影像的立交桥精细结构提取方法

Fine structure extraction method of overpass based on trajectory and remote sensing image fusion

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