DOGE: Differentiable Bezier Graph Optimization for Road Network Extraction

• URL: http://arxiv.org/abs/2511.19850v1
• Date: Tue, 25 Nov 2025 02:28:53 GMT
• Title: DOGE: Differentiable Bezier Graph Optimization for Road Network Extraction
• Authors: Jiahui Sun, Junran Lu, Jinhui Yin, Yishuo Xu, Yuanqi Li, Yanwen Guo,
• Abstract summary: We introduce the Bézier Graph, a differentiable parametric curve-based representation.<n>Our framework, DOGE, operationalizes this paradigm by learning a parametric Bézier Graph directly from segmentation masks.<n>Our method sets a new state-of-the-art on the large-scale SpaceNet and CityScale benchmarks.
• Score: 21.196497150995356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Bézier 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 Bézier Graph. Our framework, DOGE, operationalizes this paradigm by learning a parametric Bézier 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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