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 data-driven vision technology. However, their performance is limited to fully automating road map extraction in real-world services. Hence, many services employ the human-in-the-loop approaches on the extracted road maps: semi-automatic detection and repairing faulty road maps. Our paper exclusively focuses on the latter, introducing a novel data-driven approach for fixing road maps. We incorporate image inpainting approaches to tackle complex road geometries without custom-made algorithms for each road shape, yielding a method that is readily applicable to any road map segmentation 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：由于道路网络维护劳动强度大，在大规模高分辨率卫星影像的丰富数据以及数据驱动视觉技术进步的推动下，许多自动道路提取方法已被引入以解决这一实际问题。然而，这些方法在真实服务中完全自动化的道路地图提取性能仍有限。因此，许多服务在提取的道路地图上采用人在回路方法：半自动检测与修复有缺陷的道路地图。本文专门聚焦于后者，提出了一种新颖的数据驱动道路地图修复方法。我们通过引入图像修复技术处理复杂的道路几何结构，无需针对每种道路形状定制算法，从而使得该方法可便捷地应用于任意道路地图分割模型。我们在多种真实道路几何场景（如直道、弯道、T型路口及交叉路口）上验证了该方法的有效性。