The automated extraction of rural roads is pivotal for rural development and transportation planning, serving as a cornerstone for socio-economic progress. Current research primarily focuses on road extraction in urban areas. However, rural roads present unique challenges due to their narrow and irregular nature, posing significant difficulties for road extraction. In this article, a reverse refinement network (R2-Net) is proposed to extract narrow rural roads, enhancing their connectivity and distinctiveness from the background. Specifically, to preserve the fine details of roads within high-resolution feature maps, R2-Net utilizes an axis context aware module (ACAM) to capture the long-distance spatial context information in various layers. Subsequently, the multi-level features are aggregated through a global aggregation module (GAM). Moreover, in the decoder stage, R2-Net employs a reverse-aware module (RAM) to direct the attention of the network to the complex background, thus amplifying its separability. In experiments, we compare R2-Net with several state-of-the-art methods using the DeepGlobe road extraction dataset and the WHU-RuR+ global large-scale rural road dataset. R2-Net achieved superior performance and especially excelled in accurately detecting narrow roads. Furthermore, we explored the applicability of R2-Net for large-scale rural road mapping. The results show that the proposed R2-Net has significant performance advantages for large-scale rural road mapping applications.

翻译：乡村道路的自动提取对于乡村发展与交通规划至关重要，是社会经济进步的基石。当前研究主要集中于城市道路提取。然而，乡村道路因其狭窄且不规则的特性带来了独特挑战，给道路提取带来了显著困难。本文提出了一种反向细化网络（R2-Net）用于提取狭窄乡村道路，以增强其连通性及与背景的区分度。具体而言，为保留高分辨率特征图中道路的精细细节，R2-Net采用轴向上下文感知模块（ACAM）来捕获各层中的长距离空间上下文信息。随后，通过全局聚合模块（GAM）对多层级特征进行聚合。此外，在解码器阶段，R2-Net采用反向感知模块（RAM）引导网络关注复杂背景，从而增强其可分离性。实验中，我们使用DeepGlobe道路提取数据集和WHU-RuR+全球大规模乡村道路数据集，将R2-Net与多种先进方法进行比较。R2-Net取得了优越的性能，尤其在狭窄道路的精确检测方面表现突出。此外，我们探索了R2-Net在大规模乡村道路制图中的应用潜力。结果表明，所提出的R2-Net在大规模乡村道路制图应用中具有显著的性能优势。