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

2026-02-26

AI Summary

This work proposes an end-to-end road extraction framework that addresses the limitations of existing methods in modeling long-range dependencies and complex topologies in road networks. By constructing a sparse Euclidean graph and transforming it into a line graph, the approach applies a Graph Transformer on the line graph to predict connectivity, thereby integrating line graph representations with Graph Transformers for the first time. This combination overcomes the representational limitations of conventional endpoint embeddings regarding isomorphic links. To further enhance structural integrity, the framework incorporates an overpass identification head and a coupled non-maximum suppression strategy. Evaluated on three benchmarks—City-scale, SpaceNet, and Global-scale—the method achieves state-of-the-art performance, significantly outperforming prior approaches in TOPO-F1 and APLS metrics while preserving fine visual details suitable for real-world deployment.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Planning with Language Models
• Computer Vision: Representation Learning for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The accurate and automatic extraction of roads from satellite imagery is critical for applications in navigation and urban planning, significantly reducing the need for manual annotation. Many existing methods decompose this task into keypoint extraction and connectedness prediction, but often struggle to capture long-range dependencies and complex topologies. Here, we propose LineGraph2Road, a framework that improves connectedness prediction by formulating it as binary classification over edges in a constructed global but sparse Euclidean graph, where nodes are keypoints extracted from segmentation masks and edges connect node pairs within a predefined distance threshold, representing potential road segments. To better learn structural link representation, we transform the original graph into its corresponding line graph and apply a Graph Transformer on it for connectedness prediction. This formulation overcomes the limitations of endpoint-embedding fusion on set-isomorphic links, enabling rich link representations and effective relational reasoning over the global structure. Additionally, we introduce an overpass/underpass head to resolve multi-level crossings and a coupled NMS strategy to preserve critical connections. We evaluate LineGraph2Road on three benchmarks: City-scale, SpaceNet, and Global-scale, and show that it achieves state-of-the-art results on two key metrics, TOPO-F1 and APLS. It also captures fine visual details critical for real-world deployment. We will make our code publicly available.

Problem

• road network extraction
• satellite imagery
• connectedness prediction
• long-range dependencies
• complex topologies

Innovation

• Line Graph
• Graph Transformer
• Road Network Extraction
• Structural Reasoning
• Connectedness Prediction