@misc{TrappDoellner2019,
  author    = {Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Real-time Screen-space Geometry Draping for 3D Digital Terrain Models},
  series = {2019 23rd International Conference Information Visualisation (IV)},
  journal   = {2019 23rd International Conference Information Visualisation (IV)},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Los Alamitos},
  isbn      = {978-1-7281-2838-2},
  issn      = {2375-0138},
  doi       = {10.1109/IV.2019.00054},
  pages     = {281 -- 286},
  year      = {2019},
  abstract  = {A fundamental task in 3D geovisualization and GIS applications is the visualization of vector data that can represent features such as transportation networks or land use coverage. Mapping or draping vector data represented by geometric primitives (e.g., polylines or polygons) to 3D digital elevation or 3D digital terrain models is a challenging task. We present an interactive GPU-based approach that performs geometry-based draping of vector data on per-frame basis using an image-based representation of a 3D digital elevation or terrain model only.},
  language  = {en}
}
@article{VolandAsche2017,
  author    = {Voland, Patrick and Asche, Hartmut},
  title     = {Processing and Visualizing Floating Car Data for Human-Centered Traffic and Environment Applications: A Transdisciplinary Approach},
  series = {International journal of agricultural and environmental information systems : an official publication of the Information Resources Management Association},
  volume    = {8},
  journal   = {International journal of agricultural and environmental information systems : an official publication of the Information Resources Management Association},
  publisher = {IGI Global},
  address   = {Hershey},
  issn      = {1947-3192},
  doi       = {10.4018/IJAEIS.2017040103},
  pages     = {32 -- 49},
  year      = {2017},
  abstract  = {In the era of the Internet of Things and Big Data modern cars have become mobile electronic systems or computers on wheels. Car sensors record a multitude of car and traffic related data as well as environmental parameters outside the vehicle. The data recorded are spatio-temporal by nature (floating car data) and can thus be classified as geodata. Their geospatial potential is, however, not fully exploited so far. In this paper, we present an approach to collect, process and visualize floating car data for traffic-and environment-related applications. It is demonstrated that cartographic visualization, in particular, is as effective means to make the enormous stocks of machine-recorded data available to human perception, exploration and analysis.},
  language  = {en}
}