PAB-Road: A Patch-wise Boundary for Road Network Extraction via Multitask UNet

Road network extraction from remote sensing images is a fundamental task for applications like autonomous driving and urban planning. Mainstream methods, however, face a critical trade-off: segmentation-based approaches provide high geometric detail but often yield fragmented roadmaps, while graph-based approaches ensure connectivity but can sacrifice fine-grained accuracy. While hybrid models have been explored to resolve this, effectively fusing pixel-level features with structural information remains a key challenge. To address this, we propose PAB-Road, a novel framework with a unique fusion mechanism. Its core novelty is a multitask UNet that learns a patch-wise boundary representation to explicitly model local connectivity. This learned information then guides the synthesis of a geometrically accurate and structurally coherent road network. Experimental results in the real dataset show that PAB-Road achieves a compelling F1 score of 79.29%, demonstrating the effectiveness of our proposed fusion strategy.