构建全球最大航拍道路数据集，提出新型交互式道路提取方法
RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction
- 支持点击和涂鸦的拓扑感知交互提示，更贴合道路网络结构
- 在36.7万张图像上实现最高精度与结构一致性，参数仅370万
- 适合遥感、城市规划等领域研究者，尤其关注交互式分割的场景
从航拍影像中精确分割道路是众多地理空间应用的基础。然而，现有数据集普遍存在场景多样性不足、语义粒度低、结构连续性差等问题，限制了模型跨环境泛化能力。为此，我们提出了目前最大最多样化的道路分割数据集WorldRoadSeg-360K，包含来自38个国家、223个城市的366,947张高分辨率图像，覆盖多种地形与大陆。该数据集揭示了处理复杂多变场景的关键挑战：自动化方法常破坏道路连通性，现有交互方法缺乏高效且拓扑敏感的编辑工具。为此，我们提出RoadGIE，建立遥感领域新型交互式道路提取范式。不同于传统的点或框提示，RoadGIE支持连通性感知提示（如点击与涂鸦），天然契合道路网络拓扑。为提升结构一致性并缓解迭代交互中的性能退化，引入专家引导提示策略，并将基于骨架的召回损失适配至交互场景。RoadGIE在WorldRoadSeg-360K及其他基准上均达到最优的分割精度与拓扑一致性，同时仅需370万参数，运行高效。代码已开源：https://github.com/chaineypung/RoadGIE
原文摘要 · Abstract (English)
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
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