Title: Visualizing Massive Pristine LIDAR Amplitude Responses
Authors: Benger, Werner
Leimer, Wolfgang
Baran, Ramona
Citation: Journal of WSCG. 2020, vol. 28, no. 1-2, p.177-186.
Issue Date: 2020
Publisher: Václav Skala - UNION Agency
Document type: článek
URI: http://wscg.zcu.cz/WSCG2020/2020-J_WSCG-1-2.pdf
ISSN: 1213-6972 (print)
1213-6980 (CD-ROM)
1213-6964 (on-line)
Keywords: LIDAR;vizuální analýza;zpracování dat;geověda;batymetrie;vědecká vizualizace;velká data;GPU shadery
Keywords in different language: LIDAR;visual analysis;data processing;geoscience;bathymetry;scientific visualization;big data;GPU shaders
Abstract in different language: Airborne light detection and ranging (LIDAR)- based bathymetry is a highly specialized field within the widely known and used geoscientific surveying technology based on green spectrum lasers. Green light can penetrate shallow water bodies such that river and lake beds can be surveyed. The result of such observations are point clouds, from which geometries are extracted, such as digital elevation maps for lake, river or sea floors. The quality of those maps is crucially dependent on the amount of reliable information that can be extracted from the noisy LIDAR signals. The primary LIDAR data consists of amplitude response curves for each emitted laser signal. A direct visualization of these “raw”, pristine data is crucial to verify, assess and optimize subsequent data processing and reduction methods. In this article we present a method for scientific visualization of these amplitude response curves en mass, i.e. for millions and tens of millions thereof simultaneously. As part of this direct visualization also preliminary analysis and data reduction operations can be performed interactively. This primary and direct inspection allows studying and evaluating the full potential of acquired data sets such that data processing methods can be fine-tuned to squeeze out all needed information of interest. Ideally such improved data processing avoids subsequent surveying when output from already measured data sets is improved, resulting in reduced economical and environmental costs.
Rights: © Václav Skala - UNION Agency
Appears in Collections:Volume 28, Number 1-2 (2020)

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/38440

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