Correcting Faulty Road Maps by Image Inpainting
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 27 references that are in the map. First posted on arXiv 12 Nov 2022.
Builds on
9 of 27 references.
References · 9
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Citing papers per year: 2024: 1.
1 paper.