A marked point process for automated building detection from lidar point-clouds

This letter presents a novel algorithm for automated building detection from light detection and ranging (lidar) point-clouds. The algorithm takes advantage of a marked point process to model the locations of buildings and their geometries. A Bayesian paradigm is used to obtain a posterior distribution for the marked point process. A Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm is implemented for simulating the posterior distribution. Finally, the maximum a posteriori (MAP) scheme is used to obtain an optimal building detection. The results obtained on a set of lidar point-clouds demonstrate the efficiency of the proposed algorithm in automated detection of buildings in complex residential areas.