融合全局效率与局部精度，高效精准提取遥感图像道路网
GLD-Road:A global-local decoding road network extraction model for remote sensing images
- 分两阶段：先全局检测节点并连接，再局部迭代修复断路
- 相比顶尖方法，APLS提升1.9%（City-Scale）和0.67%（SpaceNet3）
- 检索时间比主流方法快40%至92%，适合大规模遥感应用
道路网络在地图绘制、自动驾驶和灾害响应中至关重要。人工标注成本高，深度学习提供高效提取方案。现有方法包括后处理（易出错）、全局并行（快但漏节点）和局部迭代（准但慢）。本文提出GLD-Road，一种结合全局效率与局部精度的两阶段模型。首先通过连接模块检测道路节点并构建初步网络；随后采用局部搜索迭代修复断裂路段，显著降低计算开销。实验表明，GLD-Road优于当前最优方法，在City-Scale上提升APLS 1.9%，SpaceNet3上提升0.67%。相比Sat2Graph（全局）检索时间减少40%，相比RNGDet++（局部）减少92%。实验结果详见https://github.com/ucas-dlg/GLD-Road。
原文摘要 · Abstract (English)
Road networks are crucial for mapping, autonomous driving, and disaster response. While manual annotation is costly, deep learning offers efficient extraction. Current methods include postprocessing (prone to errors), global parallel (fast but misses nodes), and local iterative (accurate but slow). We propose GLD-Road, a two-stage model combining global efficiency and local precision. First, it detects road nodes and connects them via a Connect Module. Then, it iteratively refines broken roads using local searches, drastically reducing computation. Experiments show GLD-Road outperforms state-of-the-art methods, improving APLS by 1.9% (City-Scale) and 0.67% (SpaceNet3). It also reduces retrieval time by 40% vs. Sat2Graph (global) and 92% vs. RNGDet++ (local). The experimental results are available at https://github.com/ucas-dlg/GLD-Road.
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