## 2 Datasets and Method
### Proposed Scheme and Experiments  
A total of 29 data tiles out of 40 tiles from the DALES datasets were used for training of PointNet++. Test of the trained PointNet++ was performed with the remaining 11 tiles. In addition to tests with DALES datasets, another test was performed with new data from the ISPRS Vaihingen datasets. The ultimate goal of DL is to generalize the trained models. In other words, ability of the models to adapt properly to new data is critical to achieving generalization (versatility or universality) of DL. Tests using data similar to the training datasets (i.e., data selected from the same region) could not refer to evaluation of DL models. In this respect, ISPRS Vaihingen datasets were chosen as the new data. To improve capturing the local features, we partitioned the training data tiles with size of 500 \(\times\) 500 m into 50 \(\times\) 50 m sub-areas because various objects could be mixed in the larger areas, leading to decreased training performance. Data partitioning not only increases the amount of data but also improves uniqueness and diversification of the data that are requirements of the training data for DL models.  
The experiments for the semantic segmentation were performed using PointNet++ with airborne LiDAR data of the DALSE and ISPRS Vaihingen datasets. PointNet++ is one of the successful point-based DL models for semantic segmentation by training unorganized 3D point cloud data. The PointNet++ model consists of partitioning point clouds and extracting features. The model applies previously developed PointNet recursively to extract multi-scale features for accurate semantic segmentation [27]. The main tasks of using airborne LiDAR data in the field of photogrammetry are spatial database building, topographic mapping, DSM generation, 3D object reconstruction and modeling, and 3D city modeling for digital twin. Recently, deep learning solutions are increasingly involved in accomplishing these tasks. To improve performance of the DL model, one can add the number of return additional attributes (or additional features) to the training data. Figure 11 shows the workflow of the proposed method.  
Figures 12 and 13 show the characteristics of the two returns, and these datasets were used for evaluating the proposed method. It is clear that the two returns dominantly conveyed building boundaries. If LiDAR data with two returns is utilized, it is particularly beneficial to identify and extract buildings. The quality of the DALES datasets is superior to the ISPRS Vaihingen datasets in terms of the point density. Moreover, the quality of the LiDAR data depends on the performance of the laser sensor. Therefore, points with two returns in DALES datasets represent much clearer building boundaries formed by two returns than ISPRS Vaihingen datasets. The experiments showed that the quality of the points on the building boundaries affect building segmentation.  
Figure 11: Workflow of the proposed method.  
Figure 12: DALES data with two returns: (**a**) test \(1\); (**b**) test \(2\). Two returns belong to building boundaries: (**c**) test \(1\); (**d**) test \(2\).  
Figure 13: ISPRS Vaihingen data with two returns: (**a**) new \(1\); (**b**) new \(2\). Two returns belong to building boundaries: (**c**) new \(1\); (**d**) new \(2\).