WHU-RRoad: A high-resolution remote sensing benchmark dataset for rural road extraction

Rapid and accurate extraction of road infrastructure from high-resolution remote sensing satellite imagery is important for traffic planning, construction, and management. In recent years, road extraction methods have developed rapidly, benefiting from the application of data-driven deep learning based models and the various urban road datasets. However, there are still application bottlenecks when directly transferring the current research from urban to rural areas. Specifically, most road datasets are designed for urban areas, and only a small number of rural scenes are included, without complex rural scenes. Due to the huge style differences between urban and rural roads in different geographical areas, it is difficult to apply the current datasets to rural road extraction. In this article, a large-scale high-resolution remote sensing road dataset, termed WHU-RRoad, is introduced for rural road extraction, which contains 27770 pairs of 1024 × 1024 satellite images with resolution of 0.3m and corresponding road annotations, covering a 2620.71 km2 rural area in central China. In addition, a comprehensive analysis of the performance of the current state-of-the-art deep learning based road extraction methods on the WHU-RRoad dataset is provided, where the experimental results illustrate that the proposed WHU-RRoad dataset is a challenging dataset for large-scale rural road extraction. At the same time, the WHU-RRoad dataset can meet the application requirements of rural road construction and has great application potential.

从高分辨率遥感卫星影像中快速且精准地提取道路基础设施，对于交通规划、建设与管理而言具有重要意义。近年来，得益于数据驱动深度学习模型的应用以及各类城市道路数据集的涌现，道路提取方法得到了快速发展。然而，将现有研究直接从城市场景迁移至农村场景时，仍存在应用瓶颈。具体而言，当前多数道路数据集均针对城市场景设计，仅包含少量农村场景，且未覆盖复杂农村场景。由于不同地理区域的城乡道路风格差异显著，现有数据集难以直接应用于农村道路提取任务。本文提出一款面向农村道路提取的大规模高分辨率遥感道路数据集——WHU-RRoad。该数据集包含27770对分辨率为0.3米的1024×1024卫星影像及其对应的道路标注信息，覆盖中国中部2620.71平方公里的农村区域。此外，本文针对当前前沿的基于深度学习的道路提取方法在WHU-RRoad数据集上的性能开展了全面分析，实验结果表明，该数据集对于大规模农村道路提取任务而言具有较高挑战性。同时，WHU-RRoad数据集能够满足农村道路建设的应用需求，具备广阔的应用前景。

搜集汇总

背景与挑战

背景概述

WHU-RRoad是一个大规模高分辨率遥感农村道路数据集，包含27770对卫星图像和道路标注，覆盖2620.71平方公里农村区域，旨在支持农村道路建设和管理的应用需求。该数据集填补了农村道路提取领域的空白，并为深度学习模型提供了具有挑战性的基准测试平台。