一种稀疏标签优化的异构数据道路提取方法

doi:10.11947/j.AGCS.2026.20250230

测绘学报 ›› 2026, Vol. 55 ›› Issue (5): 881-893.doi: 10.11947/j.AGCS.2026.20250230

一种稀疏标签优化的异构数据道路提取方法

林雨准¹(), 王淑香¹, 芮杰¹, 金飞¹(), 姜建芳¹, 左溪冰², 刘潇¹, 邹毓杰¹

^1.信息工程大学，河南　郑州　450001
^2.31016部队，北京　100080

收稿日期:2025-06-05 修回日期:2026-04-26 出版日期:2026-06-23 发布日期:2026-06-23
通讯作者: 金飞 E-mail:lyz120218@163.com;jf371@sina.com
作者简介:林雨准（1993—），男，博士，副教授，研究方向为遥感数据智能处理。　E-mail：lyz120218@163.com
基金资助:
国家自然科学基金(42201443);河南省自然科学基金青年学生科学基金(252300423931)

Road extraction method for heterogeneous data using sparse labels

Yuzhun LIN¹(), Shuxiang WANG¹, Jie RUI¹, Fei JIN¹(), Jianfang JIANG¹, Xibing ZUO², Xiao LIU¹, Yujie ZOU¹

^1.Information Engineering University, Zhengzhou 450001, China
^2.Troops31016, Beijing 100080, China

Received:2025-06-05 Revised:2026-04-26 Online:2026-06-23 Published:2026-06-23
Contact: Fei JIN E-mail:lyz120218@163.com;jf371@sina.com
About author:LIN Yuzhun (1993—), male, phD, associate professor, majors in intelligent processing of remote sensing data.　E-mail: lyz120218@163.com
Supported by:
The National Natural Science Foundation of China(42201443);The Natural Science Foundation for Young Students of Henan Province(252300423931)

摘要/Abstract

摘要：

标签数据作为数据驱动法的核心要素，是影响可见光影像道路提取效果的重要因素。当前，高质量的标签数据往往需要耗费大量的时间与精力，且常规的网络模型在地域、传感器和成像时间上的迁移能力有限，导致其泛化推广存在客观局限性，故提升高质量标签数据的获取效率已成为当前的研究热点。基于此，本文高效整合开源和本地计算资源，形成“标签制作—模型训练—目标测试”的全流程自动化处理，提出一种稀疏标签优化的异构数据道路提取方法，创新实现了“历史矢量数据”到“精细标签”的转化。首先，立足开放街道地图数据，通过栅格处理、尺寸对齐等操作获取道路在可见光影像的稀疏标签；然后，联合分割一切模型和简单线性迭代聚类提取影像的多层级特征，从而基于对象级处理单元实现稀疏标签的信息传播，完成粗优化；最后，利用网络模型对可见光影像和粗优化结果进行训练，从而借助影像与标签的关联映射完善道路标签的位置信息，并结合OSM数据的缓冲区完成精优化。利用RoadNet数据集和Oklahoma数据集进行试验，联合UNet、D-LinkNet、MANet和UNetFormer 4种典型语义分割网络证明了本文方法的有效性，在定量精度和定性结果上均优于对比方法，尤其在道路提取的重难点区域优势显著。

关键词: OSM数据, 可见光影像, 弱监督, 稀疏样本, 道路提取

Abstract:

Labelled data are essential for road extraction from optical images; however, creating high-quality labels is labor-and time-intensive. Moreover, the transferability of network models across different regions, sensors, and imaging times is limited, restricting their broader application in spatio-temporal contexts. To address this issue, we propose a road extraction method for heterogeneous data using sparse labels that combines optical imagery with OpenStreetMap (OSM) data. Sparse road labels are generated through raster processing and coordinate alignment with OSM vector data. Then, the segment anything model (SAM) and simple linear iterative clustering (SLIC) are integrated to extract multi-level image features, thereby facilitating label dissemination through object-level processing for initial optimization. Finally, a network model was trained using both optical images and rough optimization results, and it refined the label accuracy via image-label association mapping and was further optimized with OSM data as a buffer. Experimental validation using the RoadNet and Oklahoma datasets in conjunction with the four semantic segmentation networks UNet, D-LinkNet, MANet and UNetFormer demonstrated that our proposed method outperforms existing methods in terms of both quantitative accuracy and performance, especially in challenging areas of road extraction.

Key words: OSM data, optical image, weak supervision, sparse sample, road extraction

中图分类号:

P237

林雨准, 王淑香, 芮杰, 金飞, 姜建芳, 左溪冰, 刘潇, 邹毓杰. 一种稀疏标签优化的异构数据道路提取方法[J]. 测绘学报, 2026, 55(5): 881-893.

Yuzhun LIN, Shuxiang WANG, Jie RUI, Fei JIN, Jianfang JIANG, Xibing ZUO, Xiao LIU, Yujie ZOU. Road extraction method for heterogeneous data using sparse labels[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(5): 881-893.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20250230

http://xb.chinasmp.com/CN/Y2026/V55/I5/881

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参考文献 35

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网络模型	标签优化方法	P↑	R↑	F₁值↑	IoU↑	Com↑	Eor↓
UNet	ScRoadExtract	77.96	91.55	84.22	72.74	85.37	8.24
	WeaklyOSM	81.13	89.92	85.30	74.37	84.91	5.61
	本文方法	86.57	90.10	88.30	79.05	82.99	4.57
D-LinkNet	ScRoadExtract	78.85	92.75	85.24	74.28	86.87	7.73
	WeaklyOSM	81.32	90.66	85.74	75.04	85.66	5.99
	本文方法	96.31	80.74	87.84	78.32	81.28	1.94
MANet	ScRoadExtract	80.98	92.09	86.18	75.72	86.14	6.16
	WeaklyOSM	82.44	90.52	86.29	75.89	85.16	4.36
	本文方法	97.46	80.08	87.91	78.43	81.31	1.13
UNetFormer	ScRoadExtract	80.13	92.59	85.91	75.30	87.08	6.15
	WeaklyOSM	82.02	91.02	86.28	75.88	85.59	4.31
	本文方法	96.88	81.67	88.62	79.57	81.15	2.40

网络模型	标签优化方法	P↑	R↑	F₁值↑	IoU↑	Com↑	Eor↓
UNet	ScRoadExtract	25.59	72.96	37.89	23.37	70.40	42.42
	WeaklyOSM	24.67	66.98	36.06	22.00	65.60	46.51
	本文方法	64.72	58.83	61.64	44.55	54.57	29.03
D-LinkNet	ScRoadExtract	26.66	80.12	40.00	25.00	77.62	40.32
	WeaklyOSM	24.08	73.88	36.32	22.19	72.13	42.51
	本文方法	62.76	61.23	61.98	44.91	54.32	19.68
MANet	ScRoadExtract	28.21	89.15	42.86	27.27	83.38	37.22
	WeaklyOSM	24.25	90.92	38.29	23.68	85.86	42.89
	本文方法	59.65	72.26	65.35	48.53	65.38	21.47
UNetFormer	ScRoadExtract	28.09	83.94	42.09	26.65	78.90	35.98
	WeaklyOSM	25.81	84.98	39.59	24.68	79.55	38.72
	本文方法	60.73	69.70	64.90	48.04	62.36	23.32

一种稀疏标签优化的异构数据道路提取方法

Road extraction method for heterogeneous data using sparse labels

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