In recent years, deep learning-based methods, especially convolutional neural networks (CNNs), have shown their capabilities in remote sensing data processing. The efficacy of light detection and ranging (LiDAR) has been already proven in a wide variety of research areas. Most of the existing methods do not extract the informative features from LiDAR-derived rasterized digital surface models (LiDAR-DSM) data in a deep manner. In order to utilize the advantages of deep models for the classification of LiDAR-derived features, deep CNN is proposed here to hierarchically extract the robust and discriminant features of the input data. Moreover, morphological profiles and multiattribute profiles (MAPs) are investigated to enrich the inputs of the CNN and further to improve the ultimate classification performance. Furthermore, a new activation function, sigmoid-weighted linear units (SiLUs), is introduced. The proposed frameworks are tested on two LiDAR-DSMs (i.e., Bayview Park and Houston data sets). The MAP-CNNs with SiLU outperform original CNNs by 6.62% and 6.88% in terms of overall accuracy on Bayview Park and Houston data sets, respectively, when the number of training samples of each class is 40.