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arXiv · 2511.19850

DOGE: Differentiable Bezier Graph Optimization for Road Network Extraction

Jiahui Sun·Junran Lu·Jinhui Yin·Yishuo Xu·Yuanqi Li·Yanwen Guo
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Abstract

Automatic extraction of road networks from aerial imagery is a fundamental task, yet prevailing methods rely on polylines that struggle to model curvilinear geometry. We maintain that road geometry is inherently curve-based and introduce the B\'ezier Graph, a differentiable parametric curve-based representation. The primary obstacle to this representation is to obtain the difficult-to-construct vector ground-truth (GT). We sidestep this bottleneck by reframing the task as a global optimization problem over the B\'ezier Graph. Our framework, DOGE, operationalizes this paradigm by learning a parametric B\'ezier Graph directly from segmentation masks, eliminating the need for curve GT. DOGE holistically optimizes the graph by alternating between two complementary modules: DiffAlign continuously optimizes geometry via differentiable rendering, while TopoAdapt uses discrete operators to refine its topology. Our method sets a new state-of-the-art on the large-scale SpaceNet and CityScale benchmarks, presenting a new paradigm for generating high-fidelity vector maps of road networks. We will release our code and related data.

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Jiahui Sun, Junran Lu, Jinhui Yin, Yishuo Xu, Yuanqi Li, Yanwen Guo. 2025-11-25. DOGE: Differentiable Bezier Graph Optimization for Road Network Extraction. https://arxiv.org/abs/2511.19850

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