一种基于三分支注意力网络的面状地理实体自监督匹配方法

doi:10.11947/j.AGCS.2026.20250296

测绘学报 ›› 2026, Vol. 55 ›› Issue (1): 169-180.doi: 10.11947/j.AGCS.2026.20250296

• 地图学与地理信息 • 上一篇

一种基于三分支注意力网络的面状地理实体自监督匹配方法

孙壮¹^,²(), 刘坡¹^,², 翟亮¹^,²(), 何宇³, 张祖涛¹

^1.中国测绘科学研究院，北京　100036
^2.空间基准全国重点实验室，北京　100036
^3.中国地质大学(武汉)未来技术学院，湖北　武汉　430074

收稿日期:2025-08-12 修回日期:2026-01-05 发布日期:2026-02-13
通讯作者: 翟亮 E-mail:13236856636@163.com;zhailiang@casm.ac.cn
作者简介:孙壮（2000—），男，硕士生，研究方向为地理实体数据关联。E-mail：13236856636@163.com
基金资助:
中国测绘科学研究院基本科研业务费(AR2414);实景三维中国建设专项(A2505)

A self-supervised matching method for polygonal geographic entity based on a three-branch attention network

Zhuang SUN¹^,²(), Po LIU¹^,², Liang ZHAI¹^,²(), Yu HE³, Zutao ZHANG¹

^1.Chinese Academy of Surveying and Mapping, Beijing 100036, China
^2.State Key Laboratory of Spatial Datum, Beijing 100036, China
^3.School of Future Technology, China University of Geosciences, Wuhan 430074, China

Received:2025-08-12 Revised:2026-01-05 Published:2026-02-13
Contact: Liang ZHAI E-mail:13236856636@163.com;zhailiang@casm.ac.cn
About author:SUN Zhuang (2000—), male, postgraduate, majors in data association of geographic entity. E-mail: 13236856636@163.com
Supported by:
Fundamental Research Funds of Chinese Academy of Surveying and Mapping(AR2414);3D Real Scene China Construction Project(A2505)

摘要/Abstract

摘要：

地理实体是实景三维的重要数据成果，由于同名面状实体在不同场景数据集的空间表达存在尺度差异，因此需要地理实体匹配技术支撑数据的融合与更新。针对现有面状实体匹配方法在摆脱人工依赖、实现1∶1、1∶M、M∶N多种匹配关系精细化、差异化建模方面仍存优化空间的现状，本文提出一种基于三分支注意力网络的面状地理实体自监督匹配方法。首先，计算大小、距离、形状、方向4类特征的相似度，通过各类特征在不同阈值下的匹配实体对数量计算标准差，基于该标准差构造损失函数，训练模型获得决策阈值，相似度达到决策阈值的实体对转化为伪标签。然后，构建一个三分支架构的匹配网络，分别处理1∶1、1∶M、M∶N匹配关系，融合注意力机制和梯度加权类激活映射，自适应分配各特征的权重。最后，选取安徽省黄山市建筑与工程项目用地两类数据作为试验数据，对伪标签、特征权重分配及三分支网络框架分别进行验证。结果表明，与现有方法比较，本文方法在无须人工标注的情况下，能够自适应实现1∶1、1∶M、M∶N多种匹配关系的匹配，精确率（P）、召回率（R）、F₁值分别达到94.98%、94.22%、94.60%。该方法的有效性得到验证，可为面状地理实体数据的融合与更新工作提供有力支撑。

关键词: 面状地理实体匹配, 伪标签, 自监督学习, 注意力机制

Abstract:

Geographic entity are important data outputs of 3D realistic geospatial scenes. Due to scale differences in the spatial expression of polygonal entities with the same name across datasets from different scenarios, geographic entity matching technology is required to support data fusion and updating. Aiming at the current situation that existing polygonal entity matching methods still have room for optimization in breaking away from manual dependence and realizing refined and differentiated modeling of multiple matching relationships (1∶1, 1∶M, M∶N), this paper proposes a self-supervised matching method for polygonal geographic entity based on a three-branch attention network. First, it calculates the similarity of four types of features: size, distance, shape, and direction. For each type of feature, the standard deviation is computed based on the number of matched entity pairs under different thresholds. A loss function is constructed using this standard deviation to train the model and obtain decision thresholds, and entity pairs whose similarity meets the decision thresholds are converted into pseudo-labels. Second, a matching network with a three-branch structure is built to handle 1∶1, 1∶M, and M∶N matching relationships respectively. This network integrates the attention mechanism and gradient-weighted class activation mapping (Grad-CAM) to adaptively assign weights to each feature. Finally, two types of data—construction land and engineering project land in Huangshan city, Anhui province—are selected as experimental data to conduct verification on the pseudo-labels, feature weight assignment, and the three-branch network framework respectively. The experimental results show that compared with existing methods, the proposed method in this paper does not require manual annotation, can adaptively achieve matching for multiple matching relationships (1∶1, 1∶M, M∶N), and achieves a precision (P) of 94.98%, recall (R) of 94.22%, and F₁ score of 94.60%. Its effectiveness is verified, and it can provide strong support for the fusion and updating of polygonal geographic entity data.

Key words: polygonal geographic entity matching, pseudo-labels, self-supervised learning, attention mechanism

中图分类号:

P208

孙壮, 刘坡, 翟亮, 何宇, 张祖涛. 一种基于三分支注意力网络的面状地理实体自监督匹配方法[J]. 测绘学报, 2026, 55(1): 169-180.

Zhuang SUN, Po LIU, Liang ZHAI, Yu HE, Zutao ZHANG. A self-supervised matching method for polygonal geographic entity based on a three-branch attention network[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(1): 169-180.

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数据类别	训练数据	测试数据
自然幢实体数量	9094	1377
建筑用地实体数量	2343	612
1∶1数量	482	344
1∶M数量	1325	166
M∶N数量	24	17

方法	匹配关系	TP	FP	FN	P/（%）	R/（%）	F₁值/（%）
经验阈值	全部	450	40	37	91.84	92.40	92.12
	1∶1	302	29	13	91.24	95.87	93.50
	1∶M	136	8	22	94.44	86.08	90.07
	M∶N	12	3	2	80.00	85.71	82.76
CatBoost	全部	459	23	45	94.92	93.03	93.96
	1∶1	307	11	31	95.91	92.15	93.99
	1∶M	149	10	12	93.71	95.51	94.60
	M∶N	13	2	2	86.67	86.67	86.67
BPNN	全部	448	46	33	90.69	93.14	91.90
	1∶1	293	26	25	91.85	92.14	91.99
	1∶M	143	17	6	89.38	95.97	92.56
	M∶N	12	3	2	80.00	85.71	82.76
本文方法	全部	473	25	29	94.98	94.22	94.60
	1∶1	303	16	25	94.98	92.38	93.66
	1∶M	156	8	2	95.12	98.73	96.89
	M∶N	14	1	2	93.33	87.50	90.32

标签	匹配关系	TP	FP	FN	P/（%）	R/（%）	F₁值/（%）
真实标签	全部	488	22	17	95.69	96.63	96.16
	1∶1	313	16	15	95.14	95.43	95.28
	1∶M	160	5	1	96.97	99.38	98.16
	M∶N	15	1	1	93.75	93.75	93.75
伪标签	全部	473	25	29	94.98	94.22	94.60
	1∶1	303	16	25	94.98	92.38	93.66
	1∶M	156	8	2	95.12	98.73	96.89
	M∶N	14	1	2	93.33	87.50	90.32

一种基于三分支注意力网络的面状地理实体自监督匹配方法

A self-supervised matching method for polygonal geographic entity based on a three-branch attention network

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