Browsing by Subject "Decision trees"

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  • Paul, A.; Yang, C.; Breitkopf, U.; Liu, Y.; Wang, Z.; Rottensteiner, F.; Wallner, M.; Verworn, A.; Heipke, C. (London : International Society for Photogrammetry and Remote Sensing, 2018)
    In this paper we investigate the potential of automatic supervised classification for urban hydrological applications. In particular, we contribute to runoff simulations using hydrodynamic urban drainage models. In order ...
  • Klinger, Tobias; Rottensteiner, Franz; Heipke, Christian (Hannover : International Society for Photogrammetry and Remote Sensing, 2014)
    Many tracking systems rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated ...
  • Schlichting, Alexander; Brenner, Claus (Göttingen : Copernicus GmbH, 2016)
    LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like ...