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Airborne LiDAR data allow the precise modeling of topography and are used in multiple contexts. To facilitate further analysis, the point cloud classification process allows the assignment of a class, object or feature, to each point. This research uses ConvPoint, a deep learning method, to perform airborne point cloud classification at scale, in rural and urban contexts. Specifically, our experiments are located near Montreal (QC) and Saint-Jean (NB) and our approach is designed to classify five classes; we used “Building”, “Ground”, “Water”, “Low Vegetation” and “Mid-High Vegetation”. Experimenting with different configurations, we achieved excellent Intersection-over-Union results for the “Mid-High Vegetation” (93%) and “Building” (86%) classes on both datasets and provide insights to improve processing times as well as accuracy.