## 4 Conclusions and Discussion  
Extraction of building data consisiting of 3D information is important for building modeling since 3D building models are utilized in various applications such as 3D mapping, city modeling and planning, and smart city associated with digital twin. LiDAR data contain not only spatial information but also unique attribute information. In particular, multiple returns of the laser beams imply the geometric characteristics of objects. The proposed method aims to accurately extract point clouds belonging to buildings by preserving the structual boundaries on the basis of the number of returns. The results of this study could be used to process 3D building modeling. Utilizing two returns yielded higher recall values of the building boundaries for the test data. The maximum improvement was from 0.7417 to 0.7948 for test data 1, and 0.7691 to 0.7851 for test data 2 in terms of recall value. However, there was no significant improvement for the new data. The quality and characteristics of the test and new data should be similar to the training data of the DL model for optimal results. Therefore, the results of the new data were not satisfactory because the quality of the data was lower than that of the training data. However, buildings were adequately identified and classified compared to the other objects. In consequence, it is not sufficient to successfully carry out the semantic segmentation of real-world objects by the raw (of original) data only. Additional meaningful attribute and intrinsic information in the data could improve the performance for achieving objectives. In this respect, multi-source and multi-modal data are being utilized more frequently. In this regard, it is valuable to implement attributes such as multiple return information of LiDAR data. In addition, multiple return of the LiDAR could be applied to extract specific features. For example, trees generate more multiple returns than other objects. Therefore, in order to extract trees from the LiDAR data and create a 3D forest map for use in environmental and ecosystem research, one must classify trees. By using multiple returns, one can efficiently identify and distinguish trees from other objects. Conceptualization, Y.-H.S. and D.-C.L. ; formal analysis, D.-C.L. ; investigation, D.-C.L. ; methodology, Y.-H.S. and D.-C.L. ; project administration, D.-C.L. ; implementation, Y.-H.S. and K.-W.S. ; software, Y.-H.S., K.-W.S. and D.-C.L. ; supervision, D.-C.L. ; validation, Y.-H.S., K.-W.S. and D.-C.L. ; writing--original draft, D.-C.L. ; writing--review and editing, Y.-H.S., K.-W.S. and D.-C.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Research Foundation of Korea (NRF-2021 RIFA1053149). **Instrucual Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** DALES datasets are available at [[https://docs.google.com/forms/d/e/1](https://docs.google.com/forms/d/e/1) FAlpQLSfehHMMvN0 Uwjnj_vWQgYsvFtOtaoGFWs7 TicRuBTnP09 NHR7A/viewform]([https://docs.google.com/forms/d/e/1](https://docs.google.com/forms/d/e/1) FAlpQLSfehHMMvN0 Uwjnj_vWQgYsvFtOtaoGFWs7 TicRuBTnP09 NHR7A/viewform) (accessed on 15 August 2021). ISPRS Vaihingen datasets are available at [[https://www2.isprs.org/commissions/comm2/wg4/benchmark/](https://www2.isprs.org/commissions/comm2/wg4/benchmark/)]([https://www2.isprs.org/commissions/comm2/wg4/benchmark/](https://www2.isprs.org/commissions/comm2/wg4/benchmark/)) (accessed on 21 April 2020). Figure 30: Graphical representation of Table 5.  
**Acknowledgments:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1F1A1053149).