@misc{RheinwaltBookhagen2018,
  author    = {Rheinwalt, Aljoscha and Bookhagen, Bodo},
  title     = {Network-based flow accumulation for point clouds},
  series = {Remote Sensing for Agriculture, Ecosystems, and Hydrology XX},
  volume    = {10783},
  journal   = {Remote Sensing for Agriculture, Ecosystems, and Hydrology XX},
  publisher = {SPIE-INT Society of Photo-Optical Instrumentation Engineers},
  address   = {Bellingham},
  isbn      = {978-1-5106-2150-3},
  issn      = {0277-786X},
  doi       = {10.1117/12.2318424},
  pages     = {12},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{ParedesAmorBooetal.2016,
  author    = {Paredes, E. G. and Amor, M. and Boo, M. and Bruguera, J. D. and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Hybrid terrain rendering based on the external edge primitive},
  series = {International journal of geographical information science},
  volume    = {30},
  journal   = {International journal of geographical information science},
  publisher = {American Chemical Society},
  address   = {Abingdon},
  issn      = {1365-8816},
  doi       = {10.1080/13658816.2015.1105375},
  pages     = {1095 -- 1116},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}