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High-Definition (HD) maps can provide precise geometric and semanticinformation of static traffic environments for autonomous driving.Road-boundary is one of the most important information contained in HD mapssince it distinguishes between road areas and off-road areas, which can guidevehicles to drive within road areas. But it is labor-intensive to annotate roadboundaries for HD maps at the city scale. To enable automatic HD mapannotation, current work uses semantic segmentation or iterative graph growingfor road-boundary detection. However, the former could not ensure topologicalcorrectness since it works at the pixel level, while the latter suffers frominefficiency and drifting issues. To provide a solution to the aforementionedproblems, in this letter, we propose a novel system termed csBoundary toautomatically detect road boundaries at the city scale for HD map annotation.Our network takes as input an aerial image patch, and directly infers thecontinuous road-boundary graph (i.e., vertices and edges) from this image. Togenerate the city-scale road-boundary graph, we stitch the obtained graphs fromall the image patches. Our csBoundary is evaluated and compared on a publicbenchmark dataset. The results demonstrate our superiority. The accompanieddemonstration video is available at our project page\url{https://sites.google.com/view/csboundary/}.

csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps