## 3 Experimental Results and Analysis  
Two LiDAR data were selected from the DALES datasets as test data, and two LiDAR data were selected from the ISPRS Vaihingen datasets as new data. The test and new data are shown in Figures 14 and 15, respectively. The aerial images in Figure 14 were captured from Google Earth since images were not included in the DASLES datasets. The aerial IRimages were provided by ISPRS Vaihingen datasets. Even though the aerial images were not involved in training PointNet++, they are presented for the purpose of identifying regional characteristics and visually analyzing semantic segmentation results. The parameters for training PointNet++ were that training epoch was 20, mini-batch size was 6, and learning rate started from 0.0005 for the first 10 epochs then 0.00005 after 10 epochs. Results of the semantic segmentation from following cases are presented:  
Case 1: Original datasets (i.e., all number of returns)  
Case 2: Datasets of two returns with randomly selected points of 10% from the original datasets  
Case 3: Datasets of two returns with randomly selected points of  
Figure 16: Results of semantic segmentation of test data \(1\) for case 1: (**a**) prediction; (**b**) ground truth (i.e., label data); (**c**) difference between prediction and ground truth for all classes; (**d**) difference between prediction and ground truth for building class only. Figure 17: Results of semantic segmentation of test data \(1\) for Case 2: (**a**) prediction; (**b**) ground truth (i.e., label data); (**c**) difference between prediction and ground truth for all classes; (**d**) difference between prediction and ground truth for building class only. Figure 18: Results of semantic segmentation of test data \(1\) for Case 3: (**a**) prediction; (**b**) ground truth (i.e., label data); (**c**) difference between prediction and ground truth for all classes; (**d**) difference between prediction and ground truth for building class only. Figure 21: Results of semantic segmentation of test data 2 for Case 3: (**a**) prediction; (**b**) ground truth (i.e., label data); (**c**) difference between prediction and ground truth for all classes; (**d**) difference between prediction and ground truth for building class only. Figure 19: Results of semantic segmentation of test data 2 for Case 1: (**a**) prediction; (**b**) ground truth (i.e., label data); (**c**) difference between prediction and ground truth for all classes; (**d**) difference between prediction and ground truth for building class only. Figure 23: Results of semantic segmentation of new data 1 for Case 2: (**a**) prediction; (**b**) ground truth (i.e., label data); (**c**) difference between prediction and ground truth for all classes; (**d**) difference between prediction and ground truth for building class only. Figure 24: Results of semantic segmentation of new data 1 for Case 3: (**a**) prediction; (**b**) ground truth (i.e., label data); (**c**) difference between prediction and ground truth for all classes; (**d**) difference between prediction and ground truth for building class only. Figures 28 and 29 depict 3D perspective views of the point clouds belonging to the building class of the DALES test data and ISPRS Vaihingen new data, respectively. The building points were extracted from the semantic segmentation results by applying the proposed methods. Figure 27: Results of semantic segmentation of new data 2 for Case 3: (**a**) prediction; (**b**) ground truth (i.e., label data); (**c**) difference between prediction and ground truth for all classes; (**d**) difference between prediction and ground truth for building class only. Figure 25: Results of semantic segmentation of new data 2 for Case 1: (**a**) prediction; (**b**) ground truth (i.e., label data); (**c**) difference between prediction and ground truth for all classes; (**d**) difference between prediction and ground truth for building class only.