The improvement of measurement and particularly surveying technologies results in a largeas well as rapidly increasing amount of spatial data. These data stem from various measurementtechniques as well as platforms and, therefore, may compile quite di erent densities,qualities, and error characteristics. E ective tools are required to understand and interpretthem. The challenges include e cient processing, robustness against data flows and uncertainty,rationality of modeling, and the potential of automation and learning. This thesispresents an exploration of the use of statistical models and related techniques in spatial dataanalysis. The foundation of the methodology employed in the scope of this thesis consistsof Bayesian statistics and Markov models. Selected approaches conceived by the author,including 3D building reconstruction, semantic building classification, pattern recognitionin trajectories, and segmentation of RGBD data, demonstrate their potential in spatial datamodeling and interpretation.
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