  As maintaining road networks is labor-intensive, many automatic road
extraction approaches have been introduced to solve this real-world problem,
fueled by the abundance of large-scale high-resolution satellite imagery and
advances in computer vision. However, their performance is limited for fully
automating the road map extraction in real-world services. Hence, many services
employ the two-step human-in-the-loop system to post-process the extracted road
maps: error localization and automatic mending for faulty road maps. Our paper
exclusively focuses on the latter step, introducing a novel image inpainting
approach for fixing road maps with complex road geometries without custom-made
heuristics, yielding a method that is readily applicable to any road geometry
extraction model. We demonstrate the effectiveness of our method on various
real-world road geometries, such as straight and curvy roads, T-junctions, and
intersections.
