# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 1 Introduction  
Land subsidence, the gradual sinking or settling of the ground, poses significant risks to infrastructure and public safety. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO), the global area affected by land subsidence is projected to increase by 8% by 2040, with the most severely impacted regions concentrated in Asia. Monitoring surface deformation is therefore particularly important for identifying potential hazards and implementing effective preventive measures [1, 2, 3]. Conventional deformation monitoring methods, such as precision leveling and total station measurement, face limitations in precision, susceptibility to weather conditions, and restricted measuring range. Global Navigation Satellite System (GNSS) providesmillimeter-level [4; 5] all-weather real-time monitoring and exhibits high stability and reliability. However, its positioning performance in complex scenarios is limited by two issues: meteorologically dependent propagation delays and environmentally sensitive signal aberrations [6; 7]. With the advancement in Internet of Things (IoT) technology, its integration with GNSS has emerged as a new way of monitoring various deformations. A GNSS deformation monitoring system, combining 4G data transmission technology and automated data processing, was applied to the South-to-North Water Diversion Project [8]. It enhanced long-term monitoring efficiency with high-frequency data feedback and cloud processing, but it relies on stable communication networks, risking data interruptions in remote areas. Although effective for bridge health diagnosis with high-precision lasers and real-time IoT feedback, the high sensor deployment costs limit its widespread use. [PERSON] [9] implemented deformation monitoring and hazard warning for bridges on a high-speed roadway using a laser displacement sensor and IoT technology. Target recognition accuracy decreases significantly in low light, high dust concentration [10], or rainy and foggy weather. [PERSON]'s [11] lightweight traffic detection system also faces challenges in its performance stability at night or during bad weather. [PERSON], [PERSON] [12] integrated the Global Positioning System (GPS) and the BeiDou Navigation Satellite System (BDS) to conduct deformation observations in long-span railway bridges by fusing multi-source heterogeneous data. This dual-system approach enhances monitoring reliability through redundancy. However, the calibration mechanism for solving data conflicts among multiple systems has not been fully discussed, which may affect its applicability in complex environments. This study integrates meteorological, visual, and IoT information and comprehensively analyzes and corrects meteorological interference based on multi-source data, thereby improving monitoring accuracy and reliability. In addition, it combines visual information to present the real-time deformation condition, expanding the monitoring dimensions and coverage, while enabling real-time dynamic monitoring and early warning. The system improves monitoring reliability in areas with weak GNSS signal coverage [13; 14; 15]. We combine IoT multi-source data fusion and 3D visualization to establish a framework for deformation analysis. With stereoscopic, realistic, and materialized features [16], this framework enables the seamless integration of geospatial data, internal and external building structure details, topography, and related attribute information [17]. By regularly updating 3D data, deformation trends can be identified across various time scales. Furthermore, the component-level 3D real-scene (3 DRS) technology can be effectively applied in location services, virtual reality, and intelligent monitoring [18]. This study constructs an integrated surface deformation monitoring platform, combining GNSS, IoT-based environmental sensors, 3 DRS technology, and intelligent vision technologies to deliver high-precision, multi-dimensional observations.