Road extraction is a process of automatically generating road maps mainly from satellite images. Existing models all target to generate roads from the scratch despite that a large quantity of road maps, though incomplete, are publicly available (e.g. those from OpenStreetMap) and can help with road extraction. In this paper, we propose to conduct road extraction based on satellite images and partial road maps, which is new. We then propose a two-branch Partial to Complete Network (P2CNet) for the task, which has two prominent components: Gated Self-Attention Module (GSAM) and Missing Part (MP) loss. GSAM leverages a channel-wise self-attention module and a gate module to capture long-range semantics, filter out useless information, and better fuse the features from two branches. MP loss is derived from the partial road maps, trying to give more attention to the road pixels that do not exist in partial road maps. Extensive experiments are conducted to demonstrate the effectiveness of our model, e.g. P2CNet achieves state-of-the-art performance with the IoU scores of 70.71% and 75.52%, respectively, on the SpaceNet and OSM datasets.

翻译：道路提取是一种主要从卫星图像自动生成道路地图的过程。现有模型均致力于从零开始生成道路，然而大量虽不完整但公开可用的道路地图（如OpenStreetMap中的地图）实际上能够辅助道路提取任务。本文首次提出基于卫星图像与部分道路地图进行道路提取的新方法。我们针对该任务设计了双分支部分到完整网络（P2CNet），其包含两个核心组件：门控自注意力模块（GSAM）和缺失部分（MP）损失函数。GSAM通过通道自注意力模块与门控模块捕获长程语义信息，滤除无效特征，并实现双分支特征的更优融合。MP损失基于部分道路地图推导而来，旨在对部分道路地图中缺失的道路像素赋予更高关注权重。大量实验验证了模型的有效性，例如P2CNet在SpaceNet和OSM数据集上分别以70.71%和75.52%的交并比（IoU）得分达到了当前最优性能。