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

Abstract

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.

Keywords

• Road extraction
• Deep learning
• Rural road
• Road dataset