DeH4R: A Decoupled and Hybrid Method for Road Network Graph Extraction

TL;DR

DeH4R is a hybrid model for road network graph extraction from remote sensing images that combines efficiency and topological accuracy, outperforming previous methods in speed and accuracy.

Contribution

The paper introduces DeH4R, a novel decoupled hybrid approach that enables dynamic vertex insertion and improves topology fidelity in road network extraction.

Findings

DeH4R outperforms RNGDet++ by 4.62 APLS and 10.18 IoU on CityScale.

DeH4R is approximately 10 times faster than prior state-of-the-art methods.

DeH4R achieves state-of-the-art results on CityScale and SpaceNet benchmarks.

Abstract

The automated extraction of complete and precise road network graphs from remote sensing imagery remains a critical challenge in geospatial computer vision. Segmentation-based approaches, while effective in pixel-level recognition, struggle to maintain topology fidelity after vectorization postprocessing. Graph-growing methods build more topologically faithful graphs but suffer from computationally prohibitive iterative ROI cropping. Graph-generating methods first predict global static candidate road network vertices, and then infer possible edges between vertices. They achieve fast topology-aware inference, but limits the dynamic insertion of vertices. To address these challenges, we propose DeH4R, a novel hybrid model that combines graph-generating efficiency and graph-growing dynamics. This is achieved by decoupling the task into candidate vertex detection, adjacent vertex prediction, initial graph contruction, and graph expansion. This architectural innovation enables dynamic vertex (edge) insertions while retaining fast inference speed and enhancing both topology fidelity and spatial consistency. Comprehensive evaluations on CityScale and SpaceNet benchmarks demonstrate state-of-the-art (SOTA) performance. DeH4R outperforms the prior SOTA graph-growing method RNGDet++ by 4.62 APLS and 10.18 IoU on CityScale, while being approximately 10 $\times$ faster. The code will be made publicly available at https://github.com/7777777FAN/DeH4R.