RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction

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. Meanwhile, to alleviate user intent ambiguity, RoadGIE introduces a topo-semantic instantiation during training to enhance interaction stability and consistency. $RoadGIE$ 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 3.7 million parameters and real-time processing capabilities.