In order to use point cloud technology to better obtain surface information, the built-in optical lens of unmanned aerial vehicle(UAV) AA1300 was used to collect image data and build a 2-D digital orthophoto map (DOM) and GS-1350N lens was hung to collect a 3-D light detection and ranging point cloud. DOM classification was realized by three methods, namely, the k-nearest neighbor(KNN) method, support vector machine （SVM） method, and random forest （RF） method. 3-D point cloud was classified by the method with high accuracy in quantitative analysis. The comparative analysis of 2-D and 3-D classification mapping was carried out. The results show that, in 2-D DOM classification, kappa coefficients of RF are 3.74% and 2.16% higher, and the overall accuracy is 4.04% and 2.88% higher than those of KNN and SVM, respectively. The classification results of 2-D can be directly linearly transformed into 3-D point clouds, achieving 2-D and 3-D point cloud classification with a mapping accuracy of 94.15%. Under the same conditions, compared to 2-D/3-D point cloud mapping, direct 3-D point cloud classification can present more complete terrain information. This study indicates that the precise classification of 3-D point clouds can be helpful for better obtaining surface information.