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Laser scanning (also known as Light Detection And Ranging) has been widelyapplied in various application. As part of that, aerial laser scanning (ALS)has been used to collect topographic data points for a large area, whichtriggers to million points to be acquired. Furthermore, today, with integratingfull wareform (FWF) technology during ALS data acquisition, all returninformation of laser pulse is stored. Thus, ALS data are to be massive andcomplexity since the FWF of each laser pulse can be stored up to 256 samplesand density of ALS data is also increasing significantly. Processing LiDAR datademands heavy operations and the traditional approaches require significanthardware and running time. On the other hand, researchers have recentlyproposed parallel approaches for analysing LiDAR data. These approaches arenormally based on parallel architecture of target systems such as multi-coreprocessors, GPU, etc. However, there is still missing efficientapproaches/tools supporting the analysis of LiDAR data due to the lack of adeep study on both library tools and algorithms used in processing this data.In this paper, we present a comparative study of software libraries andalgorithms to optimise the processing of LiDAR data. We also propose new methodto improve this process with experiments on large LiDAR data. Finally, wediscuss on a parallel solution of our approach where we integrate parallelcomputing in processing LiDAR data.

Toward a new approach for massive LiDAR data processing