Neural networks for the generation of sea bed models using airborne lidar bathymetry data

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Kogut, T.; Niemeyer, J.; Bujakiewicz, A.: Neural networks for the generation of sea bed models using airborne lidar bathymetry data. In: Geodesy and Cartography 65 (2016), Nr. 1, S. 41-53. DOI: https://doi.org/10.1515/geocart-2016-0007

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Sum total of downloads: 251




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Abstract: 
Various sectors of the economy such as transport and renewable energy have shown great interest in sea bed models. The required measurements are usually carried out by ship-based echo sounding, but this method is quite expensive. A relatively new alternative is data obtained by airborne lidar bathymetry. This study investigates the accuracy of these data, which was obtained in the context of the project ‘Investigation on the use of airborne laser bathymetry in hydrographic surveying’. A comparison to multi-beam echo sounding data shows only small differences in the depths values of the data sets. The IHO requirements of the total horizontal and vertical uncertainty for laser data are met. The second goal of this paper is to compare three spatial interpolation methods, namely Inverse Distance Weighting (IDW), Delaunay Triangulation (TIN), and supervised Artificial Neural Networks (ANN), for the generation of sea bed models. The focus of our investigation is on the amount of required sampling points. This is analyzed by manually reducing the data sets. We found that the three techniques have a similar performance almost independently of the amount of sampling data in our test area. However, ANN are more stable when using a very small subset of points.
License of this version: CC BY-NC-ND 3.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2016
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 148 58.96%
2 image of flag of United States United States 21 8.37%
3 image of flag of China China 21 8.37%
4 image of flag of Canada Canada 7 2.79%
5 image of flag of Brazil Brazil 6 2.39%
6 image of flag of Taiwan Taiwan 4 1.59%
7 image of flag of Russian Federation Russian Federation 4 1.59%
8 image of flag of Netherlands Netherlands 3 1.20%
9 image of flag of France France 3 1.20%
10 image of flag of Australia Australia 3 1.20%
    other countries 31 12.35%

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