DOGE: Differentiable Bezier Graph Optimization for Road Network Extraction

2025-11-24

AI Summary

Existing aerial image road extraction methods suffer from geometric distortion inherent in polyline representations or rely heavily on scarce curve-level ground-truth annotations. To address these limitations, we propose the Bézier Graph—a fully differentiable, parametric vector representation—formulating road network generation as an alternating geometric and topological optimization problem. Geometric refinement of Bézier control points is performed via differentiable rendering (DiffAlign), while discrete topological operations (TopoAdapt) enforce structural connectivity. The framework jointly trains with segmentation priors, eliminating the need for curve annotations and enabling direct learning of high-fidelity vector maps from binary masks. Evaluated on SpaceNet and CityScale benchmarks, our method achieves state-of-the-art performance, significantly improving both geometric accuracy and topological completeness.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Extracting road networks from aerial imagery using curve-based representation
• Eliminating the need for difficult-to-construct vector ground-truth data
• Optimizing both geometry and topology of road networks holistically

Innovation

• Differentiable Bezier Graph optimization for road extraction
• Learning parametric Bezier Graph from segmentation masks
• Alternating geometry optimization and topology refinement modules