AUTOMATIC ROAD MARKINGS EXTRACTION, CLASSIFICATION AND VECTORIZATION FROM MOBILE LASER SCANNING DATA

8 citations as recorded by crossref.

1. Lane-Level Road Network Construction Based on Street-View Images J. Shi et al. https://doi.org/10.1109/JSTARS.2022.3181464
2. Point-Based Visual Status Evaluation of Worn Pavement Markings Based on a Feature-Binary-PointNet Network and Shape Descriptors Using LiDAR Point Clouds: A Case Study of an Expressway J. Wang et al. https://doi.org/10.1177/03611981231185139
3. Toward Automated Parking Lot As-Built Measurement: All-Factor Extraction and Coarse-to-Fine Reliable Vectorization via LiDAR-Visual Fusion T. Yao et al. https://doi.org/10.1109/JSEN.2026.3734493
4. Focal Combo Loss for Improved Road Marking Extraction of Sparse Mobile LiDAR Scanning Point Cloud-Derived Images Using Convolutional Neural Networks M. Lagahit & M. Matsuoka https://doi.org/10.3390/rs15030597
5. Instance-Based Clustering of Road Markings with Wear and Occlusion from Mobile Lidar Data J. Jung et al. https://doi.org/10.1061/JCCEE5.CPENG-5728
6. Enhancing the Effective Receptive Field of a U-Net-Based CNN for Road Marking Segmentation From Mechanical Spinning Mobile LiDAR-Derived Imagery M. Lagahit et al. https://doi.org/10.1109/JSTARS.2026.3716325
7. Learnable Resized and Laplacian-Filtered U-Net: Better Road Marking Extraction and Classification on Sparse-Point-Cloud-Derived Imagery M. Lagahit et al. https://doi.org/10.3390/rs16234592
8. Precision Inverse Modeling of Highway Pavements Based on Standardized Alignment R. Ma et al. https://doi.org/10.1109/JSTARS.2024.3404458