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A road is the skeleton of a city and is a fundamental and importantgeographical component. Currently, many countries have built geo-informationdatabases and gathered large amounts of geographic data. However, with theextensive construction of infrastructure and rapid expansion of cities,automatic updating of road data is imperative to maintain the high quality ofcurrent basic geographic information. However, obtaining bi-phase images forthe same area is difficult, and complex post-processing methods are required toupdate the existing databases.To solve these problems, we proposed a roaddetection method based on semi-supervised learning (SRUNet) specifically forroad-updating applications; in this approach, historical road information wasfused with the latest images to directly obtain the latest state of theroad.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 ofboundary prediction, and the Residual Refinement Module (RRM) was used tooptimize the prediction results. Further, to fully utilize the limited amountof label information and to enhance the prediction accuracy on unlabeledimages, we utilized the mean teacher framework as the basic semi-supervisedlearning framework and introduced Regional Contrast (ReCo) in our work toimprove the model capacity for distinguishing between the characteristics ofroads and background elements.We applied our method to two datasets. Our modelcan effectively improve the performance of a model with fewer labels. Overall,the proposed SRUNet can provide stable, up-to-date, and reliable predictionresults for a wide range of road renewal tasks.

Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps