High point densities obtained by today’s laser scanning systems enable the extraction of features that are traditionally mapped by photogrammetry or land surveying. While significant progress has been made in the extraction of roads from dense point clouds, little research has been performed on modelling uncertainty in extracted road polygons. In this paper random sets are used to model this uncertainty. Based on the accuracy reported by the data provider, positional errors in laser points are simulated first by a Markov Chain Monte Carlo method. An algorithm is developed next to detect the positions of road polygons in the simulated data and integrating the random sets for the uncertainty modelling. This algorithm is adapted to point data with different densities and variable distributions. Uncertainty modelling includes modelling of the dependence between the vertices of a road polygon. Road polygons constructed from vertices with different truncated normal distributions along with their uncertain line segments are represented by random sets, and their parameters are estimated. The effect of distributions on the area of the mean set is analysed and validated by a set of reference data collected from GPS measurements and image digitising. Results show that random sets provide useful spatial information on uncertainties using their basic parameters like the core, mean and support set. The study shows that random sets are well-suited to model the uncertainty of road polygons extracted from point data.
|Journal||Computers, environment and urban systems|
|Publication status||Published - 2013|