Accurate road networks are of great importance to online food delivery (OFD) services. In recent years, various data sources have been used to extract road information. Remote sensing images and Global Positioning System (GPS) trajectories can provide complementary information about roads, and the fusion of these two data sources allows to enhance the accuracy of automatic road extraction. To make full use of the information available from these two data sources, we developed a decoder fusion model based on the dilated Res-U-Net (DF-DRUNet) which fuses the remote sensing images and GPS trajectories in an efficient way to extract the road network. The DF-DRUNet model is built on two components: First, two independent dilated Res-U-Net models are used, where one model uses remote sensing images as input whilst the other model takes GPS trajectories as input. Second, we fused the decoders from the modalities based on a gated fusion module, which can help to learn the selection from these two input modalities. Based on the road extraction from the DF-DRUNet model, we also developed various refinement strategies, i.e., noise removal, skeleton extraction, topology construction, and vectorization. Numerical experiments were conducted using the DF-DRUNet model and baseline models from the real dataset of remote sensing images and GPS trajectories. The quantitative evaluation shows that the DF-DRUNet model can integrate remote sensing images and GPS trajectories effectively and achieve the highest performance of F1-score (0.857) and IoU (0.746) among all baseline fusion models. Moreover, the proposed DF-DRUNet model needs relatively fewer parameters and takes shorter training time.

准确的路网对于在线食品配送（OFD）服务非常重要。近年来，各种数据源已被用于提取道路信息。遥感图像和全球定位系统（GPS）轨迹可以提供道路的互补信息，这两种数据源的融合可以提高自动道路提取的准确性。为了充分利用这两个数据源提供的信息，我们开发了一种基于扩张 Res-U-Net (DF-DRUNet) 的解码器融合模型，该模型以有效的方式融合遥感图像和 GPS 轨迹，以提取公路网。DF-DRUNet 模型基于两个组件构建：首先，使用两个独立的扩张 Res-U-Net 模型，其中一个模型使用遥感图像作为输入，而另一个模型使用 GPS 轨迹作为输入。其次，我们基于门控融合模块融合了来自模态的解码器，这可以帮助学习这两种输入模态的选择。基于DF-DRUNet模型的道路提取，我们还开发了各种细化策略，即噪声去除、骨架提取、拓扑构建和矢量化。使用 DF-DRUNet 模型和来自遥感图像和 GPS 轨迹的真实数据集的基线模型进行了数值实验。定量评价表明，DF-DRUNet模型能够有效地融合遥感图像和GPS轨迹，并在所有基线融合模型中取得了最高的F1-score（0.857）和IoU（0.746）性能。此外，所提出的 DF-DRUNet 模型需要相对较少的参数，并且需要较短的训练时间。