DOGE: Differentiable Bézier Graph Optimization for Road Network Extraction

Automatic extraction of road networks from aerial imageryis a fundamental task, yet prevailing methods rely on discrete poly-lines that inherently lack the high-order geometric continuity required bydownstream applications. While parametric curve representations, suchas our Bézier Graph, offer analytic smoothness and topological compact-ness, their extraction is bottlenecked by the absence of curve-level vectorground-truth (GT) and the fragility of heuristic vectorization pipelines.To overcome these challenges, we introduce DOGE, a novel differen-tiable rendering-driven framework for robust, curve GT-free road net-work extraction. DOGE elegantly decouples the extraction process viaa discrete-continuous coupling mechanism: a DiffAlign module contin-uously optimizes the graph’s geometric alignment against target masksusing differentiable rendering, while a TopoAdapt module applies dis-crete operators to evolve the network’s topology. This coupling preventstopological bottlenecks from paralyzing the gradient-based geometric op-timization. Extensive experiments demonstrate that our method sets anew state-of-the-art on the large-scale SpaceNet and City-Scale bench-marks. Driven purely by mask guidance, DOGE successfully reconstructshighly accurate, geometrically smooth, and topologically compact vectormaps, advancing a new paradigm for structured road network reconstruc-tion.