@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{Doellner2020,
  author    = {D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Geospatial artificial intelligence},
  series = {Journal of photogrammetry, remote sensing and geoinformation science : PFG : Photogrammetrie, Fernerkundung, Geoinformation},
  volume    = {88},
  journal   = {Journal of photogrammetry, remote sensing and geoinformation science : PFG : Photogrammetrie, Fernerkundung, Geoinformation},
  number    = {1},
  publisher = {Springer International Publishing},
  address   = {Cham},
  issn      = {2512-2789},
  doi       = {10.1007/s41064-020-00102-3},
  pages     = {15 -- 24},
  year      = {2020},
  abstract  = {Artificial intelligence (AI) is changing fundamentally the way how IT solutions are implemented and operated across all application domains, including the geospatial domain. This contribution outlines AI-based techniques for 3D point clouds and geospatial digital twins as generic components of geospatial AI. First, we briefly reflect on the term "AI" and outline technology developments needed to apply AI to IT solutions, seen from a software engineering perspective. Next, we characterize 3D point clouds as key category of geodata and their role for creating the basis for geospatial digital twins; we explain the feasibility of machine learning (ML) and deep learning (DL) approaches for 3D point clouds. In particular, we argue that 3D point clouds can be seen as a corpus with similar properties as natural language corpora and formulate a "Naturalness Hypothesis" for 3D point clouds. In the main part, we introduce a workflow for interpreting 3D point clouds based on ML/DL approaches that derive domain-specific and application-specific semantics for 3D point clouds without having to create explicit spatial 3D models or explicit rule sets. Finally, examples are shown how ML/DL enables us to efficiently build and maintain base data for geospatial digital twins such as virtual 3D city models, indoor models, or building information models.},
  language  = {en}
}
@article{RheinwahltGoswamiBookhagen2019,
  author    = {Rheinwahlt, Aljoscha and Goswami, Bedartha and Bookhagen, Bodo},
  title     = {A network-based flow accumulation algorithm for point clouds},
  series = {Journal of geophysical research : Earth surface},
  volume    = {124},
  journal   = {Journal of geophysical research : Earth surface},
  number    = {7},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2169-9003},
  doi       = {10.1029/2018JF004827},
  pages     = {2013 -- 2033},
  year      = {2019},
  abstract  = {Flow accumulation algorithms estimate the steady state of flow on real or modeled topographic surfaces and are crucial for hydrological and geomorphological assessments, including delineation of river networks, drainage basins, and sediment transport processes. Existing flow accumulation algorithms are typically designed to compute flows on regular grids and are not directly applicable to arbitrarily sampled topographic data such as lidar point clouds. In this study we present a random sampling scheme that generates homogeneous point densities, in combination with a novel flow path tracing approach-the Facet-Flow Network (FFN)-that estimates flow accumulation in terms of specific catchment area (SCA) on triangulated surfaces. The random sampling minimizes biases due to spatial sampling and the FFN allows for direct flow estimation from point clouds. We validate our approach on a Gaussian hill surface and study the convergence of its SCA compared to the analytical solution. Here, our algorithm outperforms the multiple flow direction algorithm, which is optimized for divergent surfaces. We also compute the SCA of a 6-km(2)-steep, vegetated catchment on Santa Cruz Island, California, based on airborne lidar point-cloud data. Point-cloud-based SCA values estimated by our method compare well with those estimated by the D-infinity or multiple flow direction algorithm on gridded data. The advantage of computing SCA from point clouds becomes relevant especially for divergent topography and for small drainage areas: These are depicted with much more detail due to the higher sampling density of point clouds.},
  language  = {en}
}