Point Cloud Data Processing and Applications

Dear Colleagues,

3D laser scanners are instruments that capture an enormous number of observations using Light Detection And Ranging (LiDAR). LiDAR is an active sensor that sends laser pulses that hit surfaces in the environment and reflect (back-scatter) to the sensor to measure the coordinate position of the surface in 3D space relative to the sensor, measuring a cloud of observations usually known as a point cloud. Automatic LiDAR data classification, feature extraction, and data modeling are still a hot research area thanks to their many applications. Rule-based and machine learning approaches are the two main research axes for data labeling and local and global feature extraction. The use of laser scanning covers urban, forest, and rural areas, and it can be performed indoors or outdoors. Laser scanning can be performed in the air by unmanned aerial vehicles (UAVs), more commonly called drones, by planes, and by satellites. Terrestrial laser scanning can be static or mobile, the latter of which involves such equipment as a Simultaneous Localization and Mapping (SLAM) scanner or a mobile vehicle. Nevertheless, the obtained point cloud may consist of several classes such as terrain, buildings, vegetation, and manmade objects.  Also, LiDAR data are the main source for 2D and 3D mapping and modeling, forest modeling and management, digital twin (DT) construction, digital terrain models (DTMs), digital surface models (DSMs), geographic information systems (GISs), city information modeling (CIM), building information modeling (BIM), land information modeling (LIM), and tree information modeling (TIM). These systems adopt the real-time monitoring and management of spatial objects to realize sustainable development in a fast-changing world. Such systems are developed with the use of new technologies that interchange data with DTs. Data extraction systems and automated systems for constructing digital models in real time are needed to develop and update DTs. In recent years, the growing demand for geospatial monitoring systems and data measurement and handling tools that take advantage of machine learning and deep learning approaches has spurred significant technological advances.

Finally, accuracy assessment may use data classification and geometric feature extraction. However, automatic feature extraction is widely implemented for point clouds to obtain more accurate positions of features in a point cloud dataset.

Dr. Fayez Tarsha Kurdi
Dr. Mohammad Awrangjeb
Guest Editors

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• LiDAR
• classification
• 3D modeling
• machine learning
• feature extraction
• monitoring
• digital twin (DT)
• point cloud
• mapping
• digital terrain model (DTM)
• digital surface model (DSM)
• geographic information system (GIS)
• city information modeling (CIM)
• building information modeling (BIM)
• land information modeling (LIM)
• tree information modeling (TIM)
• rule-based
• accuracy assessment
• sensor
• unmanned aerial vehicles (UAVs)