Correcting Faulty Road Maps by Image Inpainting

TL;DR

This paper presents a novel image inpainting method to automatically repair faulty road maps with complex geometries, improving post-processing in road extraction workflows without relying on custom heuristics.

Contribution

It introduces a new inpainting approach specifically designed for fixing complex road geometries in faulty maps, applicable across different extraction models.

Findings

Effective on various real-world road geometries

Improves automation in road map correction

No need for custom heuristics

Abstract

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.