Correcting Faulty Road Maps by Image Inpainting

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...

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Bibliographic Details
Published in:arXiv.org 2024-01
Main Authors: Hong, Soojung, Choi, Kwanghee
Format: Article
Language:English
Subjects:
Algorithms
Developing countries
Image resolution
Image segmentation
LDCs
Roads
Satellite imagery
Online Access:Get full text
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Summary: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.
ISSN:2331-8422