融合三维地形特征的图Transformer河网模式识别方法

doi:10.11947/j.AGCS.2026.20260139

测绘学报 ›› 2026, Vol. 55 ›› Issue (9): 1695-1705.doi: 10.11947/j.AGCS.2026.20260139

融合三维地形特征的图Transformer河网模式识别方法

柴润泽¹^,²^,³(), 李精忠¹^,²^,³(), 闫浩文¹^,²^,³, 王小龙¹^,²^,³, 刘振跃¹^,²^,³, 王灵¹^,²^,³

^1.兰州交通大学测绘与地理信息学院，甘肃　兰州　730070
^2.地理国情监测技术应用国家地方联合工程研究中心，甘肃　兰州　730070
^3.甘肃省测绘科学与技术重点实验室，甘肃　兰州　730070

收稿日期:2026-04-13 修回日期:2026-08-14 出版日期:2026-10-28 发布日期:2026-10-28
通讯作者: 李精忠 E-mail:12242158@stu.lzjtu.edu.cn;lilideyx@126.com
作者简介:柴润泽（2002—），男，硕士生，主要研究方向为地图综合、智能制图。　E-mail：12242158@stu.lzjtu.edu.cn
基金资助:
国家自然科学基金(42394063; 42271454);甘肃省联合科研基金重大项目(24JRRA848);甘肃省委组织部重点人才项目(2025RCXM012);兰州市科技计划项目(2024-3-68);甘肃省优秀博士生项目(25JRRA214)

A graph Transformer method for river network pattern recognition incorporating three-dimensional terrain features

Runze Chai¹^,²^,³(), Jingzhong Li¹^,²^,³(), Haowen Yan¹^,²^,³, Xiaolong Wang¹^,²^,³, Zhenyue Liu¹^,²^,³, Ling Wang¹^,²^,³

^1.Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
^2.National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
^3.Key Laboratory of Science and Technology in Surveying & Mapping, Gansu Province, Lanzhou 730070, China

Received:2026-04-13 Revised:2026-08-14 Online:2026-10-28 Published:2026-10-28
Contact: Jingzhong Li E-mail:12242158@stu.lzjtu.edu.cn;lilideyx@126.com
About author:Chai Runze (2002—), male, postgraduate, majors in map generalization and intelligent cartography. E-mail: 12242158@stu.lzjtu.edu.cn
Supported by:
The National Natural Science Foundation of China(42394063; 42271454);Major Project of Gansu Provincial Joint Research Fund(24JRRA848);Key Talent Project of Gansu Provincial Organization Department(2025RCXM012);Lanzhou City Science and Technology Plan Project(2024-3-68);Gansu Province Excellent Doctoral Student Project(25JRRA214)

摘要/Abstract

摘要：

本文提出一种融合三维地形特征的图Transformer河网模式识别方法。首先，基于河网矢量数据构建河段对偶图，将河段表示为图节点，河段间拓扑连接关系表示为图边；其次，在提取二维拓扑、几何形态特征的基础上引入高程差、坡度、比降三维地形特征，增强河段节点对地形背景的表达能力；然后，利用GraphSAGE对河段邻域进行采样与聚合，学习局部汇流关系及相邻河段间的形态与地形差异，并引入Transformer自注意力机制建模河网图中的全局结构依赖关系，实现局部邻域关系与整体空间结构模式的协同表征；最后，通过图分类实现树状、扇状、平行状、骨架状和格网状5类河网模式识别。试验结果表明，本文方法在5类河网模式识别上准确率达到95.36%，优于GCN、GraphSAGE和Transformer等方法。该方法可为河网空间结构表达、流域分类与水文过程模拟提供研究思路。

关键词: Transformer, GraphSAGE, 河网模式识别, 地形特征

Abstract:

This study proposes a graph Transformer method for river network pattern recognition incorporating three-dimensional terrain features. First, a dual graph is constructed from vector drainage network data, in which river segments are represented as graph nodes and the topological connections between river segments are represented as graph edges. Second, in addition to two-dimensional topological and geometric morphological features, three-dimensional terrain features, including elevation difference, slope, and channel gradient, are introduced to enhance the representation of the terrain context associated with river-segment nodes. Third, GraphSAGE is employed to sample and aggregate neighborhood information, thereby learning local confluence relationships as well as morphological and terrain differences between adjacent river segments. A Transformer-based self-attention mechanism is then introduced to model global structural dependencies within the drainage network graph, enabling the collaborative representation of local neighborhood relationships and overall spatial structural patterns. Finally, graph classification is performed to identify five drainage patterns: dendritic, fan-shaped, parallel, skeletal, and grid-like patterns. Experimental results show that the proposed method achieves an accuracy of 95.36% in recognizing the five drainage patterns, outperforming GCN, GraphSAGE, and the Transformer methods. The proposed method provides an approach to drainage network spatial structure representation, watershed classification, and hydrological process simulation.

Key words: Transformer, GraphSAGE, river network pattern recognition, terrain features

中图分类号:

P208

柴润泽, 李精忠, 闫浩文, 王小龙, 刘振跃, 王灵. 融合三维地形特征的图Transformer河网模式识别方法[J]. 测绘学报, 2026, 55(9): 1695-1705.

Runze Chai, Jingzhong Li, Haowen Yan, Xiaolong Wang, Zhenyue Liu, Ling Wang. A graph Transformer method for river network pattern recognition incorporating three-dimensional terrain features[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(9): 1695-1705.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20260139

http://xb.chinasmp.com/CN/Y2026/V55/I9/1695

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方法	准确率/（%）	召回率/（%）	F₁值/（%）	训练时间/min
GCN	85.52	84.24	84.61	4.32
GraphSAGE	89.57	88.21	89.36	5.18
Transformer	92.25	91.82	92.03	7.64
本文方法	95.36	95.08	95.24	8.52

编号	类别	方法	模式概率					预测类别
编号	类别	方法	树状	扇状	平行状	骨架状	格网状	预测类别
（a）	扇状	GCN	0.142	0.386	0.421	0.039	0.012	平行状
		GraphSAGE	0.118	0.514	0.319	0.036	0.013	扇状
		Transformer	0.071	0.761	0.126	0.030	0.012	扇状
		本文方法	0.048	0.806	0.116	0.019	0.011	扇状
（b）	骨架状	GCN	0.248	0.054	0.038	0.577	0.083	骨架状
		GraphSAGE	0.621	0.046	0.034	0.209	0.090	树状
		Transformer	0.184	0.037	0.026	0.701	0.052	骨架状
		本文方法	0.096	0.026	0.018	0.833	0.027	骨架状
（c）	树状	GCN	0.316	0.109	0.026	0.538	0.011	骨架状
		GraphSAGE	0.672	0.092	0.023	0.201	0.012	树状
		Transformer	0.748	0.061	0.017	0.164	0.010	树状
		本文方法	0.879	0.038	0.011	0.062	0.010	树状
（d）	平行状	GCN	0.071	0.392	0.384	0.083	0.070	扇状
		GraphSAGE	0.053	0.315	0.507	0.060	0.065	平行状
		Transformer	0.031	0.216	0.719	0.018	0.016	平行状
		本文方法	0.018	0.126	0.835	0.010	0.011	平行状
（e）	格网状	GCN	0.028	0.034	0.122	0.052	0.764	格网状
		GraphSAGE	0.020	0.025	0.091	0.040	0.824	格网状
		Transformer	0.012	0.014	0.053	0.025	0.896	格网状
		本文方法	0.008	0.010	0.031	0.014	0.937	格网状

试验设置	GraphSAGE局部学习模块	Transformer全局学习模块	三维地形特征	准确率/（%）	F₁值/（%）
去除Transformer全局学习模块	√	×	√	89.57	89.36
去除GraphSAGE局部学习模块	×	√	√	92.25	92.03
去除三维地形特征	√	√	×	93.13	92.86
本文方法	√	√	√	95.36	95.24

特征组合	准确率	召回率	F₁值
无地形特征	93.13	92.75	92.86
高程差	93.78	93.52	93.71
坡度	93.85	93.65	93.73
比降	94.08	93.95	93.84
高程差-坡度	94.47	94.28	94.03
高程差-比降	94.16	93.98	93.72
坡度-比降	94.92	94.76	94.48
高程差-坡度-比降	95.36	95.08	95.24

区域	准确率	召回率	F₁值
原始数据集	95.36	95.08	95.24
长江流域	95.19	94.75	94.88
澜沧江流域	95.27	94.84	95.20

融合三维地形特征的图Transformer河网模式识别方法

A graph Transformer method for river network pattern recognition incorporating three-dimensional terrain features

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