collecting, by a base station, from at least one user equipment (UE) operating within a coverage area of the base station, a plurality of measurement reports from the at least one UE, wherein each measurement report of the plurality of measurement reports comprises a measured value of a signal parameter and location information of the at least one UE;

selecting, by the base station, a random forest LoS/NLOS classification model from among multiple LoS/NLOS classification models based on a topography of the coverage area of the base station;

training, by the base station, the selected random forest LoS/NLOS classification model on the plurality of measurement reports by generating multiple decision trees based on randomly selected subsets of a set of input parameters and polling across the decision trees to identify predictive features, wherein the randomly selected subsets of input parameters correspond to parameters contained in the measurement reports;

obtaining, by the base station, a new measurement report from a UE operating at a first location within the coverage area of the base station; and

passing, by the base station, the new measurement report from the UE to the trained random forest LoS/NLOS classification model to obtain a LoS/NLOS classification of the first location of the UE.