Trajectory-Driven Road Network Extraction via Coupled Multi-Level Grid Semantics
Bibliographic Data
Road network extraction and updating are crucial for urban development, map updating, and mobility applications. Existing trajectory-based methods often underutilize grid-level semantic information and neighborhood context, thereby limiting their robustness to noisy, heterogeneous, and cross-city trajectory conditions. This study proposes a supervised framework for trajectory-driven road network extraction by coupling intra-grid movement semantics with inter-grid neighborhood context. Multi-level features, including convex-hull shape descriptors, directional clustering, DTW-based (Dynamic Time Warping) heterogeneity, and neighborhood density differences, are used to train a Random Forest classifier for key-grid detection. The detected key grids are further processed through morphology-aware thinning and Kalman smoothing to generate a topology-preserving and vectorization-ready road skeleton. The model is trained on pedestrian trajectories from Shenzhen and directly transferred to vehicle trajectories in Wuhan and Changsha under a zero-shot setting. Experimental results show that the proposed method achieves longer correctly extracted road length and competitive length-based precision compared with raster-based reference methods, while feature-importance and ablation analyses confirm the complementary role of neighborhood context. The proposed pipeline is scalable, interpretable, and transferable, supporting trajectory-based road map updating and urban network analysis
Feature extraction · Grid · Kalman filter · Limiting · Segmentation · Smoothing · Automated Road and Building Extraction · Data Management and Algorithms · Remote Sensing and LiDAR Applications
Trajectory Data Mining
A Hybrid Method to Incrementally Extract Road Networks Using Spatio-Temporal Trajectory Data
Automatic Extraction of Road Interchange Networks from Crowdsourced Trajectory Data
Three-Dimensional Outdoor Pedestrian Road Network Map Construction Based on Crowdsourced Trajectory Data
| Citation velocity | historical |
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| Highly cited | No |