Accurate and efficient mapping of road networks is crucial for evaluating urban development, transportation accessibility, and environmental impact. However, existing road extraction methods utilizing remote sensing images suffer from limited generalization ability and object occlusion, resulting in fragmented and discontinuous segmentation. Consequently, these limitations impede the practical applicability of these methods in multi-city and diverse-scenario road extraction applications. To address these challenges, we propose SWCARE, a road extraction method with SWitchable learning and Connectivity-Aware REfinement. We propose a flickering module with switchable learning which considers four types of auxiliary supervision information, namely road edge, road centerline, road corner, and road orientation, to improve the feature representativeness ability and enhance road extraction results. Furthermore, the proposed connectivity-aware refinement module aims to enhance the completeness and connectivity of road networks, thereby augmenting their practicality in real-world scenarios. We evaluate the performance of SWCARE on commonly used public road datasets and our constructed Large-And-Complex Road Dataset (LACRD). Our approach surpasses the state-of-the-art road extraction method in terms of both pixel-oriented and connectivity-oriented metrics, achieving a 4.41% higher Intersection over Union (IoU) and a 3.57% higher Average Path Length Similarity (APLS), respectively.

准确有效的道路网络测绘对于评估城市发展、交通可达性和环境影响至关重要。然而，现有的利用遥感图像的道路提取方法存在泛化能力有限和物体遮挡等问题，导致分割碎片化、不连续。因此，这些限制阻碍了这些方法在多城市和多种场景的道路提取应用中的实际适用性。为了应对这些挑战，我们提出了 SWCARE，一种具有可切换学习和连接感知细化功能的道路提取方法。我们提出了一种具有可切换学习功能的闪烁模块，该模块考虑了四种类型的辅助监督信息，即道路边缘、道路中心线、道路拐角和道路方向，以提高特征代表性能力并增强道路提取结果。此外，所提出的连接感知细化模块旨在增强道路网络的完整性和连接性，从而增强其在现实场景中的实用性。我们评估了 SWCARE 在常用公共道路数据集和我们构建的大型复杂道路数据集 (LACRD) 上的性能。我们的方法在面向像素和面向连接的指标方面都超越了最先进的道路提取方法，实现了 4.41% 的并集交集 (IoU) 和 3.57% 的平均路径长度相似度 (APLS) ， 分别。