Abstract: A method and a system for processing a point-cloud data. The method includes splitting the point-cloud data into a training dataset and a test dataset; segmenting the point-cloud data in the training dataset to define a plurality of point-cloud data tiles corresponding to an area of predetermined size; sampling the plurality of point-cloud data tiles to select the point-cloud data tiles including at least one data point corresponding to one or more predefined classes; dividing each of the selected point-cloud data tiles into a plurality of voxels of a predetermined volume; filtering a data point from each of the plurality of voxels having a lowest value for corresponding pulse returns ratio; normalizing the point-cloud data tiles with the filtered data points; and implementing the normalized point-cloud data tiles in a graph neural network for training thereof.