Data-driven Approach for Automatically Correcting Faulty Road Maps
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Maintaining road networks is labor-intensive, especially in actively developing countries where the road frequently changes. 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 repairment of 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 compare our method with the baselines on various road geometries, such as straight and curvy roads, T-junctions, and intersections, to demonstrate the effectiveness of our approach.
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