A road is the skeleton of a city and is a fundamental and important geographical component. Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive construction of infrastructure and rapid expansion of cities, automatic updating of road data is imperative to maintain the high quality of current basic geographic information. However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases.To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road.Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results. Further, to fully utilize the limited amount of label information and to enhance the prediction accuracy on unlabeled images, we utilized the mean teacher framework as the basic semi-supervised learning framework and introduced Regional Contrast (ReCo) in our work to improve the model capacity for distinguishing between the characteristics of roads and background elements.We applied our method to two datasets. Our model can effectively improve the performance of a model with fewer labels. Overall, the proposed SRUNet can provide stable, up-to-date, and reliable prediction results for a wide range of road renewal tasks.

翻译：道路是城市的骨架，是最基本且重要的地理要素。当前，许多国家已建立地理信息数据库并积累了大量地理数据。然而，随着基础设施的大规模建设和城市的快速扩张，为保持基础地理信息的现势性，道路数据的自动更新势在必行。但获取同一区域的双时相影像较为困难，且更新现有数据库需要复杂的后处理方法。针对这些问题，我们提出了一种基于半监督学习的道路检测方法（SRUNet），专门用于道路更新应用；该方法将历史道路信息与最新影像相融合，直接获取道路的最新状态。考虑到道路纹理复杂，我们提出名为地图编码分支（MEB）的多分支网络进行表征学习，其中采用边界增强模块（BEM）提升边界预测精度，并利用残差精化模块（RRM）优化预测结果。此外，为充分利用有限的标签信息并增强未标记图像的预测准确性，我们采用均值教师框架作为基础半监督学习框架，并引入区域对比（ReCo）机制以提升模型区分道路与背景要素特征的能力。我们将该方法应用于两个数据集，实验表明该模型能在标签较少的情况下有效提升性能。总体而言，所提出的SRUNet可为大范围道路更新任务提供稳定、现势且可靠的预测结果。