WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction
Efficient and accurate extraction of road networks from high-resolution satellite images is essential for urban planning, construction, and traffic management. Recently, various road datasets and advances in deep learning models have greatly enhanced road extraction techniques. However, challenges r...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225001657 |
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| author | Ningjing Wang Xinyu Wang Yang Pan Wanqiang Yao Yanfei Zhong |
| author_facet | Ningjing Wang Xinyu Wang Yang Pan Wanqiang Yao Yanfei Zhong |
| author_sort | Ningjing Wang |
| collection | DOAJ |
| description | Efficient and accurate extraction of road networks from high-resolution satellite images is essential for urban planning, construction, and traffic management. Recently, various road datasets and advances in deep learning models have greatly enhanced road extraction techniques. However, challenges remain when trying to apply existing research to rural areas. Specifically, most public road datasets focus on urban areas and only contain a small number of rural scenes with complex backgrounds. The application of current public datasets for rural road extraction is challenging due to significant stylistic differences between urban and rural roads. In this article, a large-scale high-resolution remote sensing road dataset, termed WHU-RuR+, is proposed for rural road extraction, which contains 36,098 pairs of 1024 × 1024 high-resolution satellite images with the corresponding road annotation, covering a 6866.35 km2 of rural areas in eight countries around the world. In addition, the article comprehensively summarizes the characteristics of this dataset and comprehensively evaluates advanced deep learning methods for road extraction on the WHU-RuR + dataset. Experimental results show that this dataset not only meets the application needs of rural road mapping but also has great practical application potential. At the same time, this article analyzes the challenges faced by rural road extraction and explores future research directions. The proposed WHU-RuR + rural road dataset will be available at the following URL: http://rsidea.whu.edu.cn/WHU_RuR+_dataset.htm. |
| format | Article |
| id | doaj-art-cef2bc20fcc640ef9f55e43b1f5598a3 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-cef2bc20fcc640ef9f55e43b1f5598a32025-08-20T03:49:42ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910451810.1016/j.jag.2025.104518WHU-RuR+: A benchmark dataset for global high-resolution rural road extractionNingjing Wang0Xinyu Wang1Yang Pan2Wanqiang Yao3Yanfei Zhong4College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China; School of Remote Sensing and Information Engineering, Wuhan University, 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, 430079, China; Corresponding author.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, 430079, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, 430079, ChinaEfficient and accurate extraction of road networks from high-resolution satellite images is essential for urban planning, construction, and traffic management. Recently, various road datasets and advances in deep learning models have greatly enhanced road extraction techniques. However, challenges remain when trying to apply existing research to rural areas. Specifically, most public road datasets focus on urban areas and only contain a small number of rural scenes with complex backgrounds. The application of current public datasets for rural road extraction is challenging due to significant stylistic differences between urban and rural roads. In this article, a large-scale high-resolution remote sensing road dataset, termed WHU-RuR+, is proposed for rural road extraction, which contains 36,098 pairs of 1024 × 1024 high-resolution satellite images with the corresponding road annotation, covering a 6866.35 km2 of rural areas in eight countries around the world. In addition, the article comprehensively summarizes the characteristics of this dataset and comprehensively evaluates advanced deep learning methods for road extraction on the WHU-RuR + dataset. Experimental results show that this dataset not only meets the application needs of rural road mapping but also has great practical application potential. At the same time, this article analyzes the challenges faced by rural road extraction and explores future research directions. The proposed WHU-RuR + rural road dataset will be available at the following URL: http://rsidea.whu.edu.cn/WHU_RuR+_dataset.htm.http://www.sciencedirect.com/science/article/pii/S1569843225001657Road extractionDeep learningRural roadRoad dataset |
| spellingShingle | Ningjing Wang Xinyu Wang Yang Pan Wanqiang Yao Yanfei Zhong WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction International Journal of Applied Earth Observations and Geoinformation Road extraction Deep learning Rural road Road dataset |
| title | WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction |
| title_full | WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction |
| title_fullStr | WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction |
| title_full_unstemmed | WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction |
| title_short | WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction |
| title_sort | whu rur a benchmark dataset for global high resolution rural road extraction |
| topic | Road extraction Deep learning Rural road Road dataset |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225001657 |
| work_keys_str_mv | AT ningjingwang whururabenchmarkdatasetforglobalhighresolutionruralroadextraction AT xinyuwang whururabenchmarkdatasetforglobalhighresolutionruralroadextraction AT yangpan whururabenchmarkdatasetforglobalhighresolutionruralroadextraction AT wanqiangyao whururabenchmarkdatasetforglobalhighresolutionruralroadextraction AT yanfeizhong whururabenchmarkdatasetforglobalhighresolutionruralroadextraction |