Correcting Faulty Road Maps by Image Inpainting
This work addresses the labor-intensive maintenance of road networks for mapping services by automating the mending step in human-in-the-loop systems, though it is incremental as it focuses only on one part of a two-step process.
The paper tackles the problem of automatically mending faulty road maps extracted from satellite imagery by introducing a novel image inpainting approach that handles complex road geometries without custom heuristics, making it applicable to any road geometry extraction model.
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.