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arXiv · 2602.23290

LineGraph2Road: Structural Graph Reasoning on Line Graphs for Road Network Extraction

Zhengyang Wei·Renzhi Jing·Yiyi He·Jenny Suckale
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Abstract

Extracting routable road networks from satellite imagery requires accurate topology recovery beyond pixel-level segmentation. Recent methods decompose the task into keypoint detection and connectivity prediction, but reliably inferring road connectivity under structural ambiguity remains challenging. Overpasses introduce non-planar crossings that can create false shortcuts, and occlusions break visual continuity, requiring long-range structural reasoning. We formulate road extraction as fully unobserved connectivity inference and construct a global but sparse Euclidean graph from detected keypoints to better leverage the long-term context. To improve link prediction, we transform this graph into its line graph and perform reasoning using a Graph Transformer. We propose an end-to-end pipeline that integrates vision-based segmentation, sparse graph construction, and structured inference. Our method explicitly models overpasses and uses topology-preserving vertex extraction to mitigate routing-critical errors. This approach achieves state-of-the-art performance on City-scale, SpaceNet, and Global-scale benchmarks in topology metrics including TOPO-F1 and APLS. In addition to quantitative gains, our method improves reconstruction of multi-level road structures crucial for real-world routing reliability. The code is available at: https://github.com/wzzzzzzy/LineGraph2Road.

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Zhengyang Wei, Renzhi Jing, Yiyi He, Jenny Suckale. 2026-02-26. LineGraph2Road: Structural Graph Reasoning on Line Graphs for Road Network Extraction. https://arxiv.org/abs/2602.23290

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