【摘要】 深度学习已成为基于时空数据实现路网自动提取的主流手段。然而，由于道路尺度差异显著、易被遮挡等原因，现有方法普遍存在道路断裂、漏提、边缘锯齿等问题。针对上述问题，本文提出了一种融合层级特征，具备多样化注意力机制的道路面与中心线协同提取网络（HFDA-Net）。该网络以单一图像或多源数据为输入，采用双路协同建模策略提取路网。首先，设计层级特征交互融合模块（HFIFM）,实现卷积网络与Transformer结构相耦合，有效融合不同层级特征中的局部细节与全局语义信息。其次，为了增强道路线性结构感知和特征判别性，提出状态空间全局扫描增强模块（SGSEM）和多样化注意力优化增强模块（DARM）。最后，构建基于图Transformer的双路解码器（DDGT）,显式建模道路面与中心线之间的空间-结构共生关系，实现解码阶段的信息互补与预测协同，提升路网提取的完整性。在BJRoad、Massachusetts和City-scale数据集上的试验结果表明，本文方法在IoU、F₁值和TOPO等关键指标上均优于现有对比方法，能够有效缓解道路漏提和中断等问题。本文方法可为大规模路网更新和智能驾驶提供技术支持。更多还原

【Abstract】 Deep learning has become the dominant approach for automatic road network extraction based on spatio-temporal data.However, due to significant variations in road scale and frequent occlusions, existing methods often suffer from road discontinuities, missing detections, and jagged boundaries.To address these challenges, this paper proposes a hierarchical feature-aware and diversified-attention-based collaborative road surface and centerline extraction network（HFDA-Net）.The proposed network takes single-source imagery or multi-source data as input and adopts a dual-branch collaborative modeling strategy for road network extraction.First, a hierarchical feature interaction and fusion module（HFIFM）is designed to couple convolutional neural networks with Transformer architectures, enabling effective fusion of local details and global semantic information across multiple feature levels.Second, to enhance the perception of linear road structures and improve feature discriminability, a state-space global scanning enhancement module（SGSEM）and a diversified attention refinement module（DARM）are introduced.Finally, a dual-branch decoder based on a graph transformer（DDGT）is constructed to explicitly model the spatial-structural co-existence between road surfaces and centerlines, achieving complementary information exchange and collaborative prediction during decoding, thereby improving the completeness of road network extraction.Experimental results on the BJRoad, Massachusetts, and City-scale datasets demonstrate that the proposed method outperforms state-of-the-art approaches in key metrics such as IoU,F₁-score, and TOPO,effectively alleviating road discontinuity and missing detection issues.The proposed method provides robust technical support for large-scale road network updating and intelligent driving applications.更多还原