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.

翻译：由于道路网络的维护工作劳动强度大，借助大规模高分辨率卫星影像的丰富资源以及计算机视觉领域的进步，学界已提出多种自动道路提取方法来解决这一现实问题。然而，在实际服务中，这些方法在完全实现道路地图自动提取方面的性能仍存在局限。因此，许多服务采用包含人工验证的两步式闭环系统对提取的道路地图进行后处理：错误定位与自动修复。本文专门聚焦于后一步，提出一种新颖的图像修复方法，无需定制化启发式规则即可修复具有复杂道路几何形态的错误地图，从而得到一种可直接应用于任何道路几何提取模型的方法。我们在多种真实道路几何形态（如直线道路、弯曲道路、T型交叉口和十字交叉口）上验证了该方法的有效性。