## 5 Conclusions  
In this study, we created a digital twin of an underground utility tunnel and identified spatial objects using a unique algorithm that utilized a multimodal image sensor, incorporating light detection and ranging (LiDAR) technology. The research consisted of three stages: constructing the digital twin using building information modeling integrated with a geographic information system (BIM-GIS), defining a method for extracting spatial objects, and applying image-sensor-based segmentation and a random sample consensus (RANSAC). This method allows for the creation of digital twins for underground areas and extraction of spatial objects using inbuilt image sensors, enabling the detection of structural shifts and management of future structural irregularities. The following conclusions were drawn regarding the extraction of 3D spatial objects and the implemented algorithm:  
* The rationale behind executing real-time updates of 3D spatial information was discussed. This clarification distinguishes automatic updates from real-time updates that directly mirror object movements, preventing potential confusion. * We described the categories of (near) real-time 3D spatial information updates, the information being updated in them, the update process, and how updates vary based on object types. * We re-evaluated existing algorithms to enhance (near) real-time spatial information update algorithms for each 3D object type. In doing so, we also redefined the scenario process, data flow, and other essential algorithms to support the existing ones. * The supplementary algorithm for extracting and updating 3D spatial objects was scrutinized and analyzed. Specifically, the algorithm for extracting point cloud data spatial objects was enhanced, and the algorithms for extracting feature points, spatial objects, and displacement information were examined and analyzed to propose a new integrated algorithm. * We tested the developed algorithm using point cloud sample data. For this, an environment resembling a utility tunnel was set up, and LiDAR data were collected. The more precise the LiDAR data, the simpler the data analysis. Our results showed that object classification became significantly easier. Lastly, when deriving displacement data, we did not obtain accurate data because relative coordinate data were employed. However, precise data could be obtained through data processing. In this study, data accuracy was compromised owing to axial transformations and asynchronous data visualizations, which arise from the general motion of unfixed line data and shifts in LiDAR installation positions. To address these issues in future studies, we propose two main solutions:  
* Collecting sufficient data from fixed multimodal sensors: By securing and using multiple sensors that operate in different modalities (for instance, combining LiDAR with photogrammetric or radar sensors), you can gather richer, more comprehensive data. If these sensors are also fixed in position, it could help mitigate issues arising from shifts in sensor installation positions. * Performing repeated testing in diverse environments: This approach would help validate the effectiveness of the extraction algorithms across various conditions, enhancing their robustness and reliability. By repeatedly testing the system under different scenarios, you can refine the displacement detection information extraction process, making it more efficient and accurate. In sum, these strategies could significantly enhance the quality of displacement information extracted in future studies, contributing to more reliable and effective outcomes in the field of spatial data analyses and 3D modelling. **Funding:** This research was supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT, MOIS, MOLIT, and MOTIE) (No. 2020-0-00061, Development of integrated platform technology for fire and disaster management in underground utility tunnel based on digital twin).