TY - JOUR N2 - 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. L1 - http://www.czasopisma.pan.pl/Content/98379/PDF/Art3_%20Kogut_inni.pdf L2 - http://www.czasopisma.pan.pl/Content/98379 PY - 2016 IS - No 1 DO - 10.1515/geocart-2016-0007 KW - Airborne Lidar Bathymetry KW - interpolation KW - neural networks KW - inverse distance weighting KW - Delaunay triangulation A1 - Kogut, Tomasz A1 - Niemeyer, Joachim A1 - Bujakiewicz, Aleksandra PB - Commitee on Geodesy PAS VL - vol. 65 DA - 2016 T1 - Neural networks for the generation of sea bed models using airborne lidar bathymetry data UR - http://www.czasopisma.pan.pl/dlibra/publication/edition/98379 T2 - Geodesy and Cartography ER -