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.

翻译：从航空影像中自动提取道路网络是一项基础任务，但现有方法主要依赖折线表示，难以有效建模曲线几何结构。我们认为道路几何本质上是基于曲线的，因此引入了贝塞尔图——一种基于可微分参数化曲线的表示方法。该表示面临的主要障碍在于难以获取构造复杂的矢量标注真值。我们通过将任务重新定义为对贝塞尔图的全局优化问题，绕过了这一瓶颈。我们的框架DOGE通过直接从分割掩码中学习参数化贝塞尔图，实现了这一范式，无需曲线标注真值。DOGE通过交替运行两个互补模块对图进行整体优化：DiffAlign通过可微分渲染持续优化几何结构，而TopoAdapt则利用离散算子精修拓扑关系。本方法在大型SpaceNet和CityScale基准测试中取得了新的最优性能，为生成高保真矢量道路网络地图提供了新范式。我们将公开代码及相关数据。