Intelligent Processing and Analysis of LiDAR Point Clouds

Dear Colleagues,

(1) Introduction, including scientific background and highlighting the importance of this research area.

LiDAR (light detection and ranging) has emerged as a key sensing technology for capturing high-resolution three-dimensional representations of real-world environments. By emitting laser pulses and measuring their return times, LiDAR systems generate dense point clouds that encode precise spatial and geometric information. These point clouds have become foundational data sources in fields such as autonomous driving, urban planning, forestry, robotics, geosciences and digital twin modeling.

However, the raw data produced by LiDAR sensors are often massive, unstructured and noisy, posing significant challenges for efficient processing and analysis. Traditional geometric and statistical methods struggle to scale or generalize across diverse scenarios. In recent years, advances in machine learning—particularly deep learning on irregular data structures—have revolutionized point cloud understanding, enabling tasks such as segmentation, classification, object detection and scene reconstruction with unprecedented accuracy.

Despite these advances, several challenges remain, including handling sparsity and occlusion, achieving real-time processing, ensuring robustness under varying environmental conditions and integrating multimodal data sources. Therefore, the intelligent processing and analysis of LiDAR point clouds is a rapidly evolving and critically important research area, with strong implications for both scientific progress and real-world applications.

(2) Aim of the Special Issue and how the subject relates to the journal scope.

The aim of this Special Issue is to provide a comprehensive platform for researchers and practitioners to present the latest developments, methodologies and applications in the intelligent processing and analysis of LiDAR point clouds. It seeks to bring together interdisciplinary contributions that address both theoretical advancements and practical implementations.

This Special Issue aligns closely with the journal’s scope in areas such as computational intelligence, data analytics, computer vision, remote sensing and geospatial information science. By focusing on innovative algorithms, scalable frameworks and application-driven solutions, the issue aims to bridge the gap between methodological research and domain-specific deployment.

Furthermore, the Special Issue encourages contributions that explore emerging trends such as AI-driven point cloud processing, edge computing for LiDAR systems and the integration of LiDAR with other sensing modalities (e.g., RGB images, radar). Through this, it aims to advance the state of the art and foster collaboration across academia, industry and government sectors.

(3) Suggested themes and article types for submissions.

Suggested Themes:

• Deep-learning architectures for point cloud processing (e.g., point-based, voxel-based, graph-based methods)
• 3D object detection, segmentation and classification in LiDAR data
• Data fusion techniques combining LiDAR with images, radar or hyperspectral data
• Real-time and edge-based LiDAR data processing
• Noise reduction, outlier removal, and data completion in sparse point clouds
• Large-scale point cloud management, compression and visualization
• Semantic mapping and scene understanding
• Applications in autonomous vehicles, smart cities, environmental monitoring and infrastructure inspection
• Benchmark datasets, evaluation metrics and reproducibility in LiDAR research
• Explainable and trustworthy AI for 3D data analysis

Article Types:

• Original research articles presenting novel methods, models or systems
• Review and survey papers summarizing recent advances and identifying future directions
• Application-focused studies demonstrating real-world deployment and performance
• Technical notes on datasets, tools and frameworks
• Perspective or opinion papers discussing emerging challenges and opportunities

This Special Issue aims to capture both foundational innovations and applied research, contributing to the continued advancement of intelligent LiDAR data-processing technologies.

Dr. Jie Sun
Dr. Wenxia Dai
Prof. Dr. Wen Xiao
Guest Editors

Manuscript Submission Information

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• lidar point clouds
• 3D data processing
• deep learning for point clouds
• point cloud segmentation and classification
• 3D object detection
• multimodal data fusion
• autonomous systems and robotics
• geospatial analysis and remote sensing
• real-time and edge computing
• semantic scene understanding