Road graph extraction from remote sensing images aims at extracting topological maps composed of road vertices and edges, which has broad prospects in urban planning, traffic management, and other applications. However, existing methods are easily affected by complex remote sensing scenes, and also have shortcomings such as poor continuity and slow processing speed. In this letter, we propose a novel end-to-end road extraction method named “Road2Graph”, which encodes road graphs into topological representations for prediction. We proposed a transformer-based model to encode the deep convolutional features, and then fuse them with the output of the feature extractor to make the network pay more attention to the global multiscale road topology context. We also design an efficient topological representation that encodes attributes such as road segmentation, midpoint map, vertex map, and connection relationships with few parameters and low redundancy. The obtained topological representation can be decoded to obtain the road extraction result in graph format. We conduct experiments on two public datasets—CityScale dataset and SpaceNet dataset. The results show that our method achieves the state-of-art and improves both accuracy (TOPO-F1 +1.55% on CityScale dataset and +2.23% on SpaceNet dataset) and continuity (APLS +7.03% on CityScale dataset and +3.05% on SpaceNet dataset) compared to the other methods.

中文翻译：

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通过 Transformer 和拓扑表示提取道路图

遥感图像道路图提取旨在提取由道路顶点和边缘组成的拓扑图，在城市规划、交通管理等应用中具有广阔的前景。但现有方法容易受到复杂遥感场景的影响，也存在连续性差、处理速度慢等缺点。在这封信中，我们提出了一种名为“Road2Graph”的新型端到端道路提取方法，它将道路图编码为拓扑表示以进行预测。我们提出了一种基于变压器的模型来编码深度卷积特征，然后将它们与特征提取器的输出融合，使网络更加关注全局多尺度道路拓扑背景。我们还设计了一种高效的拓扑表示，以较少的参数和低冗余度对道路分段、中点图、顶点图和连接关系等属性进行编码。可以对获得的拓扑表示进行解码以获得图格式的道路提取结果。我们在两个公共数据集——CityScale 数据集和 SpaceNet 数据集上进行了实验。结果表明，我们的方法达到了最先进的水平，并提高了准确性（TOPO-F1 在 CityScale 数据集上 +1.55%，在 SpaceNet 数据集上 +2.23%）和连续性（APLS 在 CityScale 数据集上 +7.03%，在 SpaceNet 数据集上 +3.05%） SpaceNet 数据集）与其他方法相比。