Point Cloud Data Analytics

Dear Colleagues,

The advancement of technologies that provide point cloud data has paved the way for the development of new ideas and opportunities. These technologies are being used in a myriad of applications: Land use/cover, forestry, agriculture, archeology, soil sciences, tectonics, mining, and autonomous vehicles, to name a few. Currently, there are two widely-used point cloud data technologies: One is generated from digital imagery (photogrammetry) and the other is from Light Detection and Ranging (LiDAR). Each technology (photogrammetry or LiDAR) can be one of three types: Airborne, terrestrial, or mobile, each with a set of specific characteristics that can meet the requirements of a group of applications. Applications utilize photogrammetry and LiDAR technologies for surveying, 3D mapping, object/feature classification, distance calculation, and object/feature detection, among others.

Specific features of photogrammetric imaging technology include raster data acquisition and RGB, or other relevant, pixel values. Specific features of LiDAR technology include high point density, high spatial resolution, and high spatial accuracy. It is mainly due to these features, along with the reduced cost of these technologies, that both photogrammetric and LiDAR are being widely used in many applications. While photogrammetric and LiDAR technologies have much advanced and their use is increasing, there are still challenges in working with photogrammetrically- and LiDAR-derived data point clouds and developing image-based and LiDAR-based applications. One common challenge in working with point cloud data is preparation for applications since the datasets are typically very large. Another challenge is how to accurately and in real time recognize and detect 3D objects/features in the datasets.

In this Special Issue, we are particularly interested in original papers that address common techniques for handling and analyzing point cloud data, challenges in dealing with point cloud data in applications, and developing new applications where point cloud data plays an important role. Sample topics include:

Techniques for point cloud data collection;

Techniques for point cloud data compression;

Models and techniques for point cloud data analysis;

Techniques for handling real-time point cloud data computation;

Models and algorithms for object detection from point cloud data;

New applications by using point cloud data.

Prof. Dr. Hassan Karimi
Dr. Bobak Karimi
Guest Editors

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