# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 7 Conclusions  
This study proposes a multi-source IoT and intelligent vision-integrated deformation monitoring system to address limitations in conventional methods. The system combines GNSS terminals, environmental sensors, and visual sensors for real-time data acquisition via the MQTT protocol. Point cloud data acquired from UAV tilt photography and 3D laser scanning are processed using ContextCapture and SVSGeoModeler to generate large-scale and component-level 3D models. These models undergo vertex compression with Cesium  
Figure 17: The display interface of the highway slope monitoring system features a top menu including buttons for Historical Alerts, InSAR, GNSS, Environmental Monitoring, Base Map, Layers, and Tools. The figure shows (**a**) the system page for monitoring station 1, monitoring station 2, and monitoring station 3 and (**b**) the system page for monitoring station 4, monitoring station 5, and monitoring station 6. Lab and lightweight optimization based on ASTC for efficient storage and rendering. A Zlmediakit streaming server, combined with 3D fusion and nighttime fill lighting, enhances virtual-real integration. Blender-simulated cameras and edge detection enable real-scene synthesis and crack extraction. By fusing 3 DRS models with GNSS positioning, meteorological monitoring, and vision-based dynamic tracking, the system achieves real-time digital twin interoperability, significantly improving observational capacity compared to traditional approaches. Compared with traditional single-source data-based deformation monitoring methods that rely solely on GNSS, our proposed system's multi-source data integration approach offers a more comprehensive and accurate assessment of deformation. Single-source methods often fail to capture the complex environmental factors and detailed object characteristics that can influence deformation. For example, they may overlook the impact of sudden weather changes, like strong winds or heavy rainfall, on the monitored objects. In contrast, our system, by incorporating meteorological sensors, can take these factors into consideration, providing a more reliable assessment of deformation. Furthermore, unlike vision-based methods that only focus on 2D image analysis for crack detection, our system uses 3D models and edge detection in a virtual environment for more accurate identification of cracks, especially in complex scenes. The 3D model provides a more realistic spatial representation, enabling better differentiation between actual cracks and false positives caused by shadows or texture variations in 2D images. The system achieves comprehensive monitoring and early warning of surface deformation by integrating all diverse sensor data, providing precise data and robust analysis paths for surface deformation assessment. This approach supports efficient decision-making and rapid response by managers, serving as a critical component in the development of smart cities and the enhancement of urban security resilience. Future work will integrate additional sensors, such as crack meters and displacement meters, with existing GNSS and meteorological sensors, to capture deformation information more comprehensively and accurately at different scales. By incorporating data from more sources, the system can construct a complete, multi-dimensional information map of surface deformation, ranging from small local displacements to large-scale overall deformations. This will provide a robust foundation for timely hazard detection and scientific safety assessments. Conceptualization, Y.H. and W.Y. ; methodology, Y.H. and W.Y. ; validation, Q.S. ; formal analysis, W.Y. and Q.H. ; resources, W.Y. ; data curation, H.L., S.L., and W.Y. ; writing--original draft preparation, S.Z., W.Y. and Q.S. ; writing--review and editing, W.Y., H.L., S.L. and S.Z. ; supervision, Y.H. All authors have read and agreed to the published version of the manuscript.