Toward a new approach for massive LiDAR data processing

TL;DR

This paper compares software libraries and algorithms for processing massive LiDAR data, proposes improvements, and explores parallel computing solutions to enhance efficiency in handling large-scale ALS datasets.

Contribution

It provides a comprehensive comparison of existing tools, introduces a new method for LiDAR data processing, and discusses parallel computing integration for scalability.

Findings

Improved processing speed with the proposed method.

Parallel computing enhances scalability for large datasets.

Comparative analysis identifies optimal libraries and algorithms.

Abstract

Laser scanning (also known as Light Detection And Ranging) has been widely applied in various application. As part of that, aerial laser scanning (ALS) has been used to collect topographic data points for a large area, which triggers to million points to be acquired. Furthermore, today, with integrating full wareform (FWF) technology during ALS data acquisition, all return information of laser pulse is stored. Thus, ALS data are to be massive and complexity since the FWF of each laser pulse can be stored up to 256 samples and density of ALS data is also increasing significantly. Processing LiDAR data demands heavy operations and the traditional approaches require significant hardware and running time. On the other hand, researchers have recently proposed parallel approaches for analysing LiDAR data. These approaches are normally based on parallel architecture of target systems such as multi-core processors, GPU, etc. However, there is still missing efficient approaches/tools supporting the analysis of LiDAR data due to the lack of a deep study on both library tools and algorithms used in processing this data. In this paper, we present a comparative study of software libraries and algorithms to optimise the processing of LiDAR data. We also propose new method to improve this process with experiments on large LiDAR data. Finally, we discuss on a parallel solution of our approach where we integrate parallel computing in processing LiDAR data.