A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds

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Spatial as Deep: Spatial CNN for Traffic Scene Understanding

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Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

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A graph convolutional neural network for classification of building patterns using spatial vector data

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Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

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Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring

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Multi-Scale Point-Wise Convolutional Neural Networks for 3D Object Segmentation From LiDAR Point Clouds in Large-Scale Environments

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A review of high-definition map creation methods for autonomous driving

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A point-based deep learning network for semantic segmentation of MLS point clouds

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Deep Learning for LiDAR Point Cloud Classification in Remote Sensing

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Scan-to-BIM for the infrastructure domain: Generation of IFC-compliant models of road infrastructure assets and semantics using 3D point cloud data

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GAMSNet: Globally aware road detection network with multi-scale residual learning

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Deep embedded hybrid CNN–LSTM network for lane detection on NVIDIA Jetson Xavier NX

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Generation of Horizontally Curved Driving Lines in HD Maps Using Mobile Laser Scanning Point Clouds

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Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds

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Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference

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Capsule-Based Networks for Road Marking Extraction and Classification From Mobile LiDAR Point Clouds

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3D Point Cloud to BIM: Semi-Automated Framework to Define IFC Alignment Entities from MLS-Acquired LiDAR Data of Highway Roads

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Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques

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GeoAI: a review of artificial intelligence approaches for the interpretation of complex geomatics data

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A two-stage approach for road marking extraction and modeling using MLS point clouds

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Occlusion-Aware Road Extraction Network for High-Resolution Remote Sensing Imagery

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A Dense Feature Pyramid Network-Based Deep Learning Model for Road Marking Instance Segmentation Using MLS Point Clouds

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Point Cloud Completion: A Survey

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Roads Detection and Parametrization in Integrated BIM-GIS Using LiDAR

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Point cloud segmentation based on Euclidean clustering and multi-plane extraction in rugged field

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3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation

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Robust Lane Extraction From MLS Point Clouds Towards HD Maps Especially in Curve Road

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Road marking degradation analysis using 3D point cloud data acquired with a low-cost Mobile Mapping System

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Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey

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Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

Lin, Y.-C., Manish, R., Bullock, D. M., & Habib, A. F. (2021) · Remote Sensing · doi:10.3390/rs13132485 · Cited by 36

Accurate Road Marking Detection from Noisy Point Clouds Acquired by Low-Cost Mobile LiDAR Systems

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Adaptive neighborhood size and effective geometric features selection for 3D scattered point cloud classification

Günen, M. A. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108196 · Cited by 33

Automated classification of piping components from 3D LiDAR point clouds using SE-PseudoGrid

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Lane Width Estimation in Work Zones Using LiDAR-Based Mobile Mapping Systems

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POINTNET FOR THE AUTOMATIC CLASSIFICATION OF AERIAL POINT CLOUDS

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Yolov5 Series Algorithm for Road Marking Sign Identification

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Road object detection for HD map: Full-element survey, analysis and perspectives

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Conditional Generative Adversarial Network-Based Training Sample Set Improvement Model for the Semantic Segmentation of High-Resolution Remote Sensing Images

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A DEEP LEARNING FRAMEWORK FOR ROADS NETWORK DAMAGE ASSESSMENT USING POST-EARTHQUAKE LIDAR DATA

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GRNet: Geometric relation network for 3D object detection from point clouds

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An Over-Segmentation-Based Uphill Clustering Method for Individual Trees Extraction in Urban Street Areas From MLS Data

Li, J., Cheng, X., Wu, Z., & Guo, W. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3051653 · Cited by 25

Road marking extraction in UAV imagery using attentive capsule feature pyramid network

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A graph attention network for road marking classification from mobile LiDAR point clouds

Fang, L., Sun, T., Wang, S., Fan, H., & Li, J. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102735 · Cited by 23

Automated Semantics and Topology Representation of Residential-Building Space Using Floor-Plan Raster Maps

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Pole-Like Street Furniture Segmentation and Classification in Mobile LiDAR Data by Integrating Multiple Shape-Descriptor Constraints

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Characterizing zebra crossing zones using LiDAR data

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Automated pixel-level pavement marking detection based on a convolutional transformer

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Generalized LiDAR Intensity Normalization and Its Positive Impact on Geometric and Learning-Based Lane Marking Detection

Cheng, Y.-T., Lin, Y.-C., & Habib, A. F. (2022) · Remote Sensing · doi:10.3390/rs14174393 · Cited by 19

Robust and accurate sub-pixel extraction method of laser stripes in complex circumstances

Wan, M., Wang, S., Zhao, H., Jia, H., & Yu, L. (2021) · Applied Optics · doi:10.1364/ao.444730 · Cited by 18

Pixel-level detection of multiple pavement distresses and surface design features with ShuttleNetV2

Zhang, H., Zhang, A. A., He, A., Dong, Z., & Liu, Y. (2023) · Structural Health Monitoring · doi:10.1177/14759217231183656 · Cited by 18

Processing Strategy and Comparative Performance of Different Mobile LiDAR System Grades for Bridge Monitoring: A Case Study

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Focal Combo Loss for Improved Road Marking Extraction of Sparse Mobile LiDAR Scanning Point Cloud-Derived Images Using Convolutional Neural Networks

Lagahit, M. L. R., & Matsuoka, M. (2023) · Remote Sensing · doi:10.3390/rs15030597 · Cited by 16

An AI-driven object segmentation and speed control scheme for autonomous moving platforms

Talati, S., Vekaria, D., Kumari, A., & Tanwar, S. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107783 · Cited by 15

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

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Image-Translation-Based Road Marking Extraction From Mobile Laser Point Clouds

Liu, L., Ma, H., Chen, S., Tang, X., Xie, J., Huang, G., & Mo, F. (2020) · IEEE Access · doi:10.1109/access.2020.2985413 · Cited by 14

Bibliometric and visualized analysis of deep learning in remote sensing

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Deep Learning Applied to Vegetation Identification and Removal Using Multidimensional Aerial Data

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An intensity-enhanced method for handling mobile laser scanning point clouds

Fang, L., Chen, H., Luo, H., Guo, Y., & Li, J. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102684 · Cited by 13

Bending the Curve of HD Maps Production for Autonomous Vehicle Applications in Taiwan

Chiang, K., Zeng, J.-C., Tsai, M.-L., Darweesh, H., Chen, P.-X., & Wang, C. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3204306 · Cited by 13

UTILITY POLES EXTRACTION FROM MOBILE LIDAR DATA IN URBAN AREA BASED ON DENSITY INFORMATION

Shokri, D., Rastiveis, H., Shams, A., & Sarasua, W. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-4-w18-1001-2019 · Cited by 13

YOLOTree-Individual Tree Spatial Positioning and Crown Volume Calculation Using UAV-RGB Imagery and LiDAR Data

Luo, T., Rao, S., Ma, W., Song, Q., Cao, Z., Zhang, H., Xie, J., Wen, X., Gao, W., Chen, Q., et al. (2024) · Forests · doi:10.3390/f15081375 · Cited by 12

Raster-Based Point Cloud Mapping of Defective Road Marking: Toward Automated Road Inspection via Airborne LiDAR

Zhu, J., Bu, T., Ma, T., Huang, X., & Chen, F. (2024) · Journal of Transportation Engineering Part B Pavements · doi:10.1061/jpeodx.pveng-1410 · Cited by 11

Construction and Verification of a High-Precision Base Map for an Autonomous Vehicle Monitoring System

Ham, S., Im, J.-H., Kim, M., & Cho, K. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8110501 · Cited by 11

Automated Annotation of Lane Markings Using LIDAR and Odometry

Barandiarán, J., Nieto, M., Cortés, A., Otaegui, O., Esnal, J. F., & Graña, M. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3031921 · Cited by 11

Optimal Vehicle Pose Estimation Network Based on Time Series and Spatial Tightness with 3D LiDARs

Wang, H., Wang, Z., Lin, L., Xu, F., Yu, J., & Liang, H. (2021) · Remote Sensing · doi:10.3390/rs13204123 · Cited by 13

On the applicability of a scan-based mobile mapping system for monitoring the planarity and subsidence of road surfaces – Pilot study on the A44n motorway in Germany

Heinz, E., Eling, C., Klingbeil, L., & Kuhlmann, H. (2019) · Journal of Applied Geodesy · doi:10.1515/jag-2019-0016 · Cited by 11

A Click-Based Interactive Segmentation Network for Point Clouds

Sun, W., Luo, Z., Chen, Y., Li, H., Marcato, J., Gonçalves, W. N., & Li, J. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3323735 · Cited by 10

A case study: Making decisions for sustainable university campus planning using GeoAI

KUMAŞ, E., & Aslan, D. (2024) · International Journal of Engineering and Geosciences · doi:10.26833/ijeg.1506265 · Cited by 10

Coarse-to-Fine Classification of Road Infrastructure Elements from Mobile Point Clouds Using Symmetric Ensemble Point Network and Euclidean Cluster Extraction

Wang, D., Wang, J., Scaioni, M., & Si, Q. (2019) · Sensors · doi:10.3390/s20010225 · Cited by 12

Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Hu, J., Abubakar, S., Liu, S., Dai, X., Yang, G., & Sha, H. (2019) · Journal of Sensors · doi:10.1155/2019/7174602 · Cited by 10

RdmkNet & Toronto-RDMK: Large-Scale Datasets for Road Marking Classification and Segmentation

Du, J., Ma, L., Li, J., Qin, N., Zelek, J., Guan, H., & Li, J. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3394481 · Cited by 9

Three-Dimensional Reconstruction of Zebra Crossings in Vehicle-Mounted LiDAR Point Clouds

Zhao, Z., Gan, S., Xiao, B., Wang, X., & Liu, C. (2024) · Remote Sensing · doi:10.3390/rs16193722 · Cited by 9

Towards a standardized workflow for creating high-definition maps for highly automated shuttles

Rehrl, K., Gröchenig, S., Piribauer, T., Spielhofer, R., & Weissensteiner, P. (2022) · Journal of Location Based Services · doi:10.1080/17489725.2022.2059587 · Cited by 9

Transfer Learning for LiDAR-Based Lane Marking Detection and Intensity Profile Generation

Patel, A., Cheng, Y.-T., Ravi, R., Lin, Y.-C., Bullock, D. M., & Habib, A. F. (2021) · Geomatics · doi:10.3390/geomatics1020016 · Cited by 8

A Review of Indoor Automation Modeling Based on Light Detection and Ranging Point Clouds

Cui, Y., Yang, B., Liu, P., & Kong, L. (2023) · Sensors and Materials · doi:10.18494/sam4211 · Cited by 8

Proximity‐based cloud resource provisioning for deep learning applications in smart healthcare

Sivan, D., Sellappa, M., & Dinesh Peter, J. (2020) · Expert Systems · doi:10.1111/exsy.12524 · Cited by 8

Automating intersection marking data collection and condition assessment at scale with an artificial intelligence-powered system

Xie, K., Sun, H., Dong, X., Yang, H. J., & Yu, H. (2023) · Computational Urban Science · doi:10.1007/s43762-023-00098-7 · Cited by 7

Learnable Resized and Laplacian-Filtered U-Net: Better Road Marking Extraction and Classification on Sparse-Point-Cloud-Derived Imagery

Lagahit, M. L. R., Liu, X., Xiu, H., Kim, T., Kim, K.-S., & Matsuoka, M. (2024) · Remote Sensing · doi:10.3390/rs16234592 · Cited by 7

Model-Driven Precise Degradation Analysis Method of Highway Marking Using Mobile Laser Scanning Point Clouds

Ma, R., Ge, X., Zhu, Q., Jia, X., Jiang, H., Chen, M., & Liu, T. (2023) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.22-00119r2 · Cited by 7

Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from a Mobile Mapping System Point Cloud Using Oriented Bounding Box Collision-Detection Techniques

Kang, S., Lee, J.-W., & Lee, J. (2023) · Remote Sensing · doi:10.3390/rs15194656 · Cited by 7

MarkCapsNet: Road Marking Extraction From Aerial Images Using Self-Attention-Guided Capsule Network

Yu, Y., Li, Y., Liu, C., Wang, J., Yu, C., Jiang, X., Wang, L., Liu, Z., & Zhang, Y. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3124575 · Cited by 7

Context-aware and boundary-optimized model for road marking instance segmentation using MLS point cloud intensity images

Li, D., Liu, T., Du, P., Ma, T., & Liu, S. (2025) · International Journal of Digital Earth · doi:10.1080/17538947.2025.2531842 · Cited by 6

Instance-Based Clustering of Road Markings with Wear and Occlusion from Mobile Lidar Data

Jung, J., Che, E., Olsen, M. J., Parrish, C., Türkan, Y., & Yoo, S. (2024) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5728 · Cited by 6

A benchmark approach and dataset for large-scale lane mapping from MLS point clouds

Mi, X., Dong, Z., Cao, Z., Yang, B., Cao, Z. X., Zheng, C., Stoter, J., & Nan, L. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104139 · Cited by 6

A Deep Learning-Based Robust Change Detection Approach for Very High Resolution Remotely Sensed Images with Multiple Features

Huang, L., An, R., Zhao, S., Jiang, T., & Hu, H. (2020) · Remote Sensing · doi:10.3390/rs12091441 · Cited by 6

Lane-Level Road Network Construction Based on Street-View Images

Shi, J., Li, G., Zhou, L., & Lü, G. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3181464 · Cited by 7

Exploring FSCNN + Focal Loss: A Faster Alternative for Road Marking Classification on Mobile LIDAR Sparse Point Cloud Derived Images

Lagahit, M. L. R., & Matsuoka, M. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9884771 · Cited by 6

A Generalization Sample Learning Method of Deep Learning for Semantic Segmentation of Remote Sensing Images

Chen, Z., Li, J., Chen, Y., & Wang, L. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3298924 · Cited by 6

Bird's Eye View Semantic Segmentation based on Improved Transformer for Automatic Annotation

Liang, T., Pan, W., Bao, H., Fan, X., & Li, H. (2023) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2023.08.002 · Cited by 6

EXTRACTION OF ROAD MARKINGS FROM MLS DATA: A REVIEW

Barcon, E., Landes, T., Grussenmeyer, P., & Berson, G. (2022) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-2-w1-2022-7-2022 · Cited by 5

MFSCNN: APPENDING A MASKED BRANCH TO FAST-SCNN TO IMPROVE ROAD MARKING EXTRACTION ON SPARSE MLS POINT CLOUD-DERIVED IMAGES

Lagahit, M. L. R., & Matsuoka, M. (2023) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-1-w2-2023-829-2023 · Cited by 5

3D POINT CLOUD DATA PROCESSING AND INFRASTRUCTURE INFORMATION MODELS: METHODS AND FINDINGS FROM SAFEWAY PROJECT

Soilán, M., Justo, A., Sánchez‐Rodríguez, A., Lamas, D., & Riveiro, B. (2021) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2021-239-2021 · Cited by 5

BOOSTING U-NET WITH FOCAL LOSS FOR ROAD MARKING CLASSIFICATION ON SPARSE MOBILE LIDAR POINT CLOUD DERIVED IMAGES

Lagahit, M. L. R., & Matsuoka, M. (2022) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-5-2022-33-2022 · Cited by 5

IDA-Net: Intensity-distribution aware networks for semantic segmentation of 3D MLS point clouds in indoor corridor environments

Luo, Z., Chen, P., Shi, W., & Li, J. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102904 · Cited by 5

COMPARATIVE STUDY OF ROAD AND URBAN OBJECT CLASSIFICATION BASED ON MOBILE LASER SCANNERS

Rashdi, R., Balado, J., Martínez-Sánchez, J., & Arias, P. (2023) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-1-w1-2023-423-2023 · Cited by 5

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

Wang, J., Cao, M., Zhang, T., Liu, B., & Li, H. (2023) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981231185139 · Cited by 5

Precision Inverse Modeling of Highway Pavements Based on Standardized Alignment

Ma, R., Zhu, Q., Ge, X., Jia, X., Hu, H., & Liu, T. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3404458 · Cited by 4

A review on multiple facets of Indian Road safety-classification, challenges and future scope

Jindal, M., Bajal, E., Singh, P., Diwakar, M., Kumar, P., & Singh, S. (2021) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon52273.2021.9667590 · Cited by 4

3D POINT CLOUD TO BIM: AUTOMATED APPLICATION TO DEFINE IFC ALIGNMENT AND ROADWAY WIDTH ENTITIES FROM MLS-ACQUIRED LiDAR DATA OF MOUNTAIN ROADS

Lamas, D., Justo, A., Soilán, M., & Riveiro, B. (2022) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-x-4-w2-2022-169-2022 · Cited by 4

Reconstruction of 3D Zebra Crossings from Mobile Laser Scanning Point Clouds

Zeng, H., Chen, Y., Zhang, Z., Wang, C., & Li, J. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8899336 · Cited by 4

Capsule-Based Networks for Road Marking Extraction and Classification From Mobile LiDAR Point Clouds

Ma, L., Li, Y., Li, J., Yu, Y., Marcato, J., Gonçalves, W. N., & Chapman, M. A. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2990120 · Cited by 61

A two-stage approach for road marking extraction and modeling using MLS point clouds

Mi, X., Yang, B., Dong, Z., Liu, C., Zong, Z., & Yuan, Z. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.07.012 · Cited by 54

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

Pan, Y., Yang, B., Li, S., Yang, H., Dong, Z., & Yang, X. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w13-1089-2019 · Cited by 14

AUTOMATIC EXTRACTION OF ROAD MARKINGS FROM MOBILE LASER SCANNING DATA

Ma, H., Pei, Z., Wei, Z., & Zhong, R. (2017) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w7-825-2017 · Cited by 13

A method for extracting road markings based on vehicle-mounted LiDAR point clouds

He, Y., Zou, T., Yang, X. Y., Guo, M., Geng, X., & Lu, Y. (2025) · International Conference on Intelligent Traffic Systems and Smart City (ITSSC) · doi:10.1117/12.3050628