RoadGIE：面向通用交互式道路提取的全球尺度航拍基准
RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction
AI总结：
提出最大、最多样的道路分割数据集WorldRoadSeg-360K，并设计支持连通性感知提示的交互式方法RoadGIE，在分割精度和拓扑一致性上达到最优。
摘要：
从航拍图像中准确分割道路是许多地理空间应用的基础。然而，现有数据集通常面临场景多样性有限、语义粒度低和结构连续性差的问题，限制了它们在不同环境中的泛化能力。为了解决这些挑战，我们引入了WorldRoadSeg-360K，这是迄今为止最大、最多样的道路分割数据集，包含从38个国家223个城市收集的366,947张高分辨率图像，覆盖不同地形和大陆。WorldRoadSeg-360K作为一个全面的基准，揭示了处理多样化和结构复杂场景的关键挑战。自动化方法通常难以保持道路连通性，而当前的交互式方法缺乏高效、拓扑敏感的工具用于实际道路编辑。为此，我们提出了RoadGIE，建立了一种新的遥感道路提取交互范式。与先前的点或框提示策略不同，RoadGIE支持连通性感知提示，包括点击和涂鸦，这些提示与道路网络的拓扑结构天然对齐。为了提高结构一致性并减轻迭代交互中的性能下降，RoadGIE集成了专家引导的提示策略，并针对交互场景调整了基于骨架的召回损失。RoadGIE在WorldRoadSeg-360K和其他基准上，在分割精度和拓扑一致性方面均达到了最先进的性能，同时仅需3.7M参数即可高效运行。代码公开于：https://github.com/chaineypung/RoadGIE
英文摘要：
Accurate road segmentation from aerial imagery is fundamental to many geospatial applications. However, existing datasets often suffer from limited scene diversity, low semantic granularity, and poor structural continuity, restricting their generalization across environments. To address these challenges, we introduce WorldRoadSeg-360K, the largest and most diverse road segmentation dataset to date, comprising 366,947 high-resolution images collected from 38 countries and 223 cities across various terrains and continents. WorldRoadSeg-360K serves as a comprehensive benchmark and reveals key challenges in handling diverse and structurally complex scenes. Automated approaches often struggle to preserve road connectivity, while current interactive methods lack efficient, topology-sensitive tools for real-world road editing. To this end, we present RoadGIE, establishing a novel interactive paradigm for road extraction in remote sensing. Unlike prior point- or box-based prompting strategies, RoadGIE supports connectivity-aware prompts, including clicks and scribbles, which inherently align with the topology of road networks. To improve structural consistency and mitigate performance degradation during iterative interactions, RoadGIE integrates an expert-guided prompting strategy and adapts the skeleton-based recall loss for interactive scenarios. RoadGIE achieves state-of-the-art performance in both segmentation accuracy and topological consistency on WorldRoadSeg-360K and other benchmarks, while maintaining efficient operation with only 3.7M parameters. The code are publicly available at: https://github.com/chaineypung/RoadGIE
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
