Work

WHU-RuR+: A benchmark dataset for global high-resolution rural road extraction

Year: 2025

Type: article

Abstract: • A benchmark dataset for rural road extraction, WHU-RuR+, is proposed. • It contains 36,098 annotated images covering diverse rural areas around the world. • Four special challenges of rural road extraction are summarized. • A comprehensive analysis is conducted by using seven state-of-the-art methods. • The dataset provides support for the modernization of agriculture and rural areas. 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 km 2 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 .

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.104518

Authors: Ningjing Wang, Xinyu Wang, Yang Pan, Wanqiang Yao, Yanfei Zhong

Institutions: Xi'an University of Science and Technology, Wuhan University, State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Language: English

Topic: Automated Road and Building Extraction

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $3,550