TY  - JOUR
A1  - Paredes, E. G.
A1  - Amor, M.
A1  - Boo, M.
A1  - Bruguera, J. D.
A1  - Döllner, Jürgen Roland Friedrich
T1  - Hybrid terrain rendering based on the external edge primitive
JF  - International journal of geographical information science
N2  - Hybrid terrain models combine large regular data sets and high-resolution irregular meshes [triangulated irregular network (TIN)] for topographically and morphologically complex terrain features such as man-made microstructures or cliffs. In this paper, a new method to generate and visualize this kind of 3D hybrid terrain models is presented. This method can integrate geographic data sets from multiple sources without a remeshing process to combine the heterogeneous data of the different models. At the same time, the original data sets are preserved without modification, and, thus, TIN meshes can be easily edited and replaced, among other features. Specifically, our approach is based on the utilization of the external edges of convexified TINs as the fundamental primitive to tessellate the space between both types of meshes. Our proposal is eminently parallel, requires only a minimal preprocessing phase, and minimizes the storage requirements when compared with the previous proposals.
KW  - digital terrain model
KW  - Terrain rendering
KW  - TIN
KW  - multiresolution
KW  - hybrid terrain model
Y1  - 2016
U6  - https://doi.org/10.1080/13658816.2015.1105375
SN  - 1365-8816
SN  - 1362-3087
VL  - 30
SP  - 1095
EP  - 1116
PB  - American Chemical Society
CY  - Abingdon
ER  - 
TY  - GEN
A1  - Rheinwalt, Aljoscha
A1  - Bookhagen, Bodo
T1  - Network-based flow accumulation for point clouds
BT  - Facet-Flow Networks (FFN)
T2  - Remote Sensing for Agriculture, Ecosystems, and Hydrology XX
N2  - Point clouds provide high-resolution topographic data which is often classified into bare-earth, vegetation, and building points and then filtered and aggregated to gridded Digital Elevation Models (DEMs) or Digital Terrain Models (DTMs). Based on these equally-spaced grids flow-accumulation algorithms are applied to describe the hydrologic and geomorphologic mass transport on the surface. In this contribution, we propose a stochastic point-cloud filtering that, together with a spatial bootstrap sampling, allows for a flow accumulation directly on point clouds using Facet-Flow Networks (FFN). Additionally, this provides a framework for the quantification of uncertainties in point-cloud derived metrics such as Specific Catchment Area (SCA) even though the flow accumulation itself is deterministic.
KW  - lidar
KW  - point clouds
KW  - stochastic filtering
KW  - flow accumulation
KW  - drainage networks
KW  - uncertainty quantification
KW  - TIN
KW  - DEM
Y1  - 2018
SN  - 978-1-5106-2150-3
U6  - https://doi.org/10.1117/12.2318424
SN  - 0277-786X
SN  - 1996-756X
VL  - 10783
PB  - SPIE-INT  Society of Photo-Optical Instrumentation Engineers
CY  - Bellingham
ER  -