Beyond Endpoints: Path-Centric Reasoning for Vectorized Off-Road Network Extraction

2025-12-11

AI Summary

To address topological errors (e.g., structural disconnections due to occlusion or ambiguous junctions) in vectorized off-road road extraction caused by domain shift, this paper introduces a path-centric inference paradigm, overcoming the robustness limitations of conventional node-centric approaches. Methodologically, we propose MaGRoad—a mask-aware geodesic road extraction framework integrating multi-scale visual evidence aggregation, geodesic path modeling, and lightweight vector decoding. Our contributions are threefold: (1) We release WildRoad, the first global off-road road dataset, accompanied by an interactive annotation tool; (2) We design MaGRoad to explicitly model continuous road centerlines via geodesic distance priors and mask-guided feature fusion; (3) MaGRoad achieves state-of-the-art performance on WildRoad, demonstrates strong cross-domain generalization to urban road datasets (e.g., DeepGlobe, Cowc), and operates 2.5× faster than prior methods.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Deep learning has advanced vectorized road extraction in urban settings, yet off-road environments remain underexplored and challenging. A significant domain gap causes advanced models to fail in wild terrains due to two key issues: lack of large-scale vectorized datasets and structural weakness in prevailing methods. Models such as SAM-Road employ a node-centric paradigm that reasons at sparse endpoints, making them fragile to occlusions and ambiguous junctions in off-road scenes, leading to topological errors.This work addresses these limitations in two complementary ways. First, we release WildRoad, a gloabal off-road road network dataset constructed efficiently with a dedicated interactive annotation tool tailored for road-network labeling. Second, we introduce MaGRoad (Mask-aware Geodesic Road network extractor), a path-centric framework that aggregates multi-scale visual evidence along candidate paths to infer connectivity robustly.Extensive experiments show that MaGRoad achieves state-of-the-art performance on our challenging WildRoad benchmark while generalizing well to urban datasets. A streamlined pipeline also yields roughly 2.5x faster inference, improving practical applicability. Together, the dataset and path-centric paradigm provide a stronger foundation for mapping roads in the wild.

Problem

• Extracts vectorized off-road networks from challenging terrains
• Addresses topological errors from occlusions and ambiguous junctions
• Overcomes lack of large-scale datasets and structural model weaknesses

Innovation

• Introduces a path-centric framework for robust connectivity inference
• Releases a global off-road dataset with specialized annotation tool
• Achieves faster inference with streamlined processing pipeline