The accurate and automatic extraction of roads from satellite imagery is critical for applications in navigation and urban planning, significantly reducing the need for manual annotation. Many existing methods decompose this task into keypoint extraction and connectedness prediction, but often struggle to capture long-range dependencies and complex topologies. Here, we propose LineGraph2Road, a framework that improves connectedness prediction by formulating it as binary classification over edges in a constructed global but sparse Euclidean graph, where nodes are keypoints extracted from segmentation masks and edges connect node pairs within a predefined distance threshold, representing potential road segments. To better learn structural link representation, we transform the original graph into its corresponding line graph and apply a Graph Transformer on it for connectedness prediction. This formulation overcomes the limitations of endpoint-embedding fusion on set-isomorphic links, enabling rich link representations and effective relational reasoning over the global structure. Additionally, we introduce an overpass/underpass head to resolve multi-level crossings and a coupled NMS strategy to preserve critical connections. We evaluate LineGraph2Road on three benchmarks: City-scale, SpaceNet, and Global-scale, and show that it achieves state-of-the-art results on two key metrics, TOPO-F1 and APLS. It also captures fine visual details critical for real-world deployment. We will make our code publicly available.

翻译：从卫星图像中准确、自动地提取道路对于导航和城市规划应用至关重要，可显著减少人工标注需求。现有方法多将此任务分解为关键点提取和连通性预测，但往往难以捕捉长程依赖和复杂拓扑结构。本文提出LineGraph2Road框架，通过将连通性预测构建为在全局稀疏欧几里得图上进行边二分类的任务来改进预测效果：其中节点为从分割掩码提取的关键点，边连接预设距离阈值内的节点对，代表潜在道路段。为更好地学习结构链接表示，我们将原始图转换为对应的线图，并应用图Transformer进行连通性预测。该形式克服了端点嵌入融合在集合同构链接上的局限性，实现了丰富的链接表示和全局结构的有效关系推理。此外，我们引入立交桥/下穿通道预测头以解析多层交叉路口，并采用耦合非极大值抑制策略以保留关键连接。我们在三个基准数据集（City-scale、SpaceNet和Global-scale）上评估LineGraph2Road，结果表明其在TOPO-F1和APLS两项关键指标上达到最先进水平，同时能捕捉实际部署所需的重要视觉细节。我们将公开代码。