LiDAR data reduction using vertex decimation and processing with GPGPU and multicore CPU technology

Oryspayev, Dossay; Sugumaran, Ramanathan; DeGroote, John; Gray, Paul A.

doi:10.1016/j.cageo.2011.09.013

Cited by 32 publications

(18 citation statements)

References 18 publications

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“…However, the huge amount of data retrieved by a high-density survey gives several issues in its storage and usage (e.g. Oryspayev et al 2012). Besides data management concerns, the economic aspect is equally relevant with prices reaching values of hundreds Euro per square kilometer (Lovell et al 2005;Johansen et al 2010;Jakubowski et al 2013).…”

Section: Discussionmentioning

confidence: 99%

Rapid mapping application of vegetated terraces based on high resolution airborne LiDAR

Godone

Giordan

2018

Geomatics, Natural Hazards and Risk

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The aim of this work is to define a methodology for terraced areas survey and rapid mapping in a complex environment, like Ligurian (Northwestern Italy) one, where a remarkable percentage of its surface is estimated as terraced and where the canopy coverage makes their recognition very hard. Methodology steps are the definition of LiDAR survey parameters, morphometric filtering and GIS processing for final mapping. Each phase is oriented to provide a reliable terrace mapping, also practicable in canopy-covered areas due to a particular attention to land cover influence. The work considers a case study (Rupinaro basin) close to Cinque Terre, with a mixed land cover (terraces, forest and urbanized areas). The methodology provided encouraging results detecting 448 ha of terraces, 95% of them located under canopy cover. This finding pointed out that terraces mapping cannot rely only on photo-interpretation, as canopies will hamper their detection. Mapping of these areas, frequently characterized by abandonment, is crucial while identifying potential trigger factors for slope instabilities. This case study highlighted the importance of a carefully planned production chain, that should start from LiDAR survey parameter choice, providing the best input for analysis algorithm and providing the correct identification of terraces. ARTICLE HISTORY

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Section: Discussionmentioning

confidence: 99%

Rapid mapping application of vegetated terraces based on high resolution airborne LiDAR

Godone

Giordan

2018

Geomatics, Natural Hazards and Risk

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“…The bulky size of LiDAR data point cloud and complex file structure (especially for the foreseeable multi/ hyperspectral LiDAR waveform data) would impose certain computational burden. Recently, initialization toward data compression (Lipuš & Žalik, 2012;Mongus & Žalik, 2011), data structure and file handling (Elseberg, Borrmann, & Nüchter, 2013), high performance computing framework (Han, Heo, Sohn, & Yu, 2009;Lee, Gasster, Plaza, Chang, & Huang, 2011) and GPU-based processing Oryspayev, Sugumaran, DeGroote, & Gray, 2012) have been addressed and researched. Some other attempts have also been found to use compressed LiDAR data for land cover classification Toth, Laky, Zaletnyik, & Grejner-Brzezinska, 2010) and digital 3D modeling (Jang, Choi, & Cho, 2011).…”

Section: Further Development Of Lidar Data Processing Algorithmsmentioning

confidence: 99%

Urban land cover classification using airborne LiDAR data: A review

Yan

Shaker

El-Ashmawy

2015

Remote Sensing of Environment

462

270

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“…Although this method performs very well, it is extremely time consuming (Lee, 1991). Thus, it is impractical to reduce LiDAR data with huge data volumes, especially under the common computation environment (Oryspayev et al, 2012).…”

Section: Introductionmentioning

confidence: 99%