As the main carrier of long-distance transmission of power resources, fine inspection of the operation status of transmission lines is an important means of power grid operation management and risk assessment. However, there are technical difficulties in the current inspection of transmission lines, such as low automation and low efficiency. The paper combines the high-precision spatial position information of LiDAR carried by unmanned aerial vehicles and the powerful 3D spatial analysis function of GIS. Through the research of point cloud data processing and GIS system development, such as fine segmentation of point cloud data and structured modeling of individual geographic entities, a refined 3D model of transmission line body elements based on airborne laser point cloud is constructed. An intelligent detection system for transmission line safety hazards based on GIS system is developed, forming the application of LiDAR+GIS transmission line safety inspection technology. The engineering application and effectiveness evaluation results show that LiDAR+GIS technology can achieve rapid detection of safety hazards in transmission lines, with advantages such as fine inspection and high efficiency. Research provides a more realistic spatial data foundation and technical solutions for the digital construction and upgrading of power grids, disaster reduction and prevention applications.

The fault identification accuracy in transmission lines is crucial for improving the reliability of power supply in the power system. To solve the problem that existing methods are difficult to effectively identify complex external factors such as tree discharge, wind deviation, lightning strikes, bird damage, wildfires, external damage, and foreign objects caused by power lines, a fault identification method for transmission lines based on frequency features and an improved KAN network is proposed. Firstly, the three-phase data under different faults such as external force damage, wildfire, foreign objects, etc. are analyzed, and a fractional frequency transformation method is introduced to extract the depth features of the faults. Then, a self-attention convolutional Kolmogorov-Arnold network (SCKAN) incorporating self-attention and convolution module is proposed, and the improved wavelet basis function is used for network weight initialization. Finally, the proposed methods effectiveness is verified through experiments based on the collected real data of transmission lines. The results show that the proposed method greatly enhances the ability to identify transmission line faults.

Aiming at the overhead transmission line ice cover prediction problem, this paper proposes a prediction model based on graph convolutional neural network. Firstly, a graph model containing overhead transmission line topology and environmental factors is constructed by integrating the relative air humidity, wind speed, air temperature, and related data such as conductor surface temperature, conductor temperature, ambient humidity, and conductor tension change, defining the nodes as the monitoring points of the line, and the edges represent the spatial relationship between the monitoring points and the environmental impact supervision. Then, a graph convolutional neural network is used to extract features from the graph model, capture the interactions between nodes by passing node information layer by layer, and introduce an attention mechanism to weight the features of different nodes to improve the prediction performance; finally, supervised learning is performed using historical ice cover data to optimize the model parameters and ensure the generalization ability. The experimental results show that the model has high prediction accuracy and robustness under different weather conditions and line environments, providing effective support for the power sector to take timely ice melting measures.

In recent years, the application of deep convolutional neural networks in UAV aerial image super-resolution tasks has made a huge leap in the performance of UAV aerial image super-resolution. However,super-resolution methods based-on convolutional neural network rely on specific training datasets, which are typically constructed by downsampling images using a fixed bicubic kernel. When the processed image does not meet this “ideal” situation, its performance will drop dramatically. This paper proposes a self-supervised UAV aerial image super-resolution reconstruction method based on non-local mining reconstruction network (NLMRN) to solve this problem. NLMRN does not require external datasets for pre-training, only an input image. It exploits the non-local reproducibility of the internal information of UAV aerial images by downsampling the input image itself to obtain a lower resolution image for training. To better learn non-local repeated features, we use the non-local context mining block (NLCM) to establish relationships between non-local regions and select a subset of global feature maps to supplement each specific location to obtain precise details and texture reconstruction. NLCM effectively makes up for the shortcoming that the convolution operation can only process one local neighborhood at a time. Through extensive experimental verification, NLMRN is significantly better than other advanced super-resolution methods when processing UAV aerial images under “non-ideal” conditions.

In the inspection process of electric power distribution networks, complex environmental conditions, such as tree shading and random obstacles, often lead to UAVs encountering problems such as hovering and crashing when performing their tasks, which seriously affects inspection efficiency and safety. To cope with this challenge, this paper proposes an intelligent obstacle avoidance technique for automatic UAV inspection in complex scenarios. An environmental sensing system fusing LiDAR and machine vision is developed to capture multi-scale information for real-time obstacle identification by utilizing an atrous spatial pyramid pooling structure to increase the sensory field of convolution kernel. Advanced path planning algorithms are utilized to dynamically adjust the UAV's flight path to avoid obstacles. The results on the simulation tests show that the system's obstacle avoidance ability in complex environments is significantly improved, the inspection efficiency is increased by more than 20%, and the risk of accidents is effectively reduced. The proposed intelligent obstacle avoidance technology provides an efficient and safe solution for UAV inspection of power distribution networks, which has a wide range of application value and promotion prospects.

The prediction of icing on power transmission lines is a key technology to ensure the safe operation of the power grid. Icing prediction is a complex task characterized by high-dimensional nonlinearity and multimodal heterogeneity, as it necessitates the comprehensive consideration of terrain and meteorological changes. This paper proposes a deep learning approach based on an ensemble random configuration network to predict icing on transmission lines. Icing transmission line recognition is enhanced by utilizing multiscale fusion of wavelet mod-maxima for icing image edge detection. Considering features such as micro-geography and micro-meteorology in historical observational data, a Boosting ensemble learning framework is employed along with a random configuration network prediction model to forecast icing conditions on transmission lines. Case study analysis demonstrates that the proposed ensemble model outperforms individual models, effectively achieving icing transmission line recognition and thickness prediction, thereby enhancing model generalization capability and improving the accuracy of icing disaster prediction.

The medium-resolution imaging spectrometer (MERSI-Ⅱ), a key payload on the Fengyun-3D (FY-3D) satellite, provides essential data for ecological monitoring through high-frequency, multi-band observations over extensive areas. While primarily focusing on L1-level swath observations, MERSI-Ⅱ offers limited downstream products including surface reflectance and ecological parameter retrieval. This study develops an innovative image spatial enhancement method tailored to MERSI-Ⅱ's imaging characteristics. By efficiently leveraging redundant scanning observations to eliminate the Bowtie effect, this method ensures quantitative integrity while achieving tonal and spatial coherence. It effectively converts L1-level digital signals(DN) into geospatially comprehensive, high-quality surface reflectance data. The accuracy of lake monitoring depends on factors like image resolution, positioning accuracy, and imaging quality. To validate the effectiveness of our approach, we analyze 96 lakes in the Tibetan Plateau region. Using Landsat 8 OLI water extraction as a reference, our enhanced images demonstrated average extraction errors below 3.5% for large lakes(≥550 km²) and under 6.5% for small to medium-sized lakes(<550 km²). Overall, the application of spatial information enhancement improved the monitoring accuracy for all sample lakes by 3.62% compared to conventional imagery.

In response to the limitations of single satellite data in conducting large-area, long-term, and continuous water level monitoring, this paper integrates data from ICESat, CryoSat-2, and ICESat-2 satellite altimeters, and combines actual water level data with meteorological information to construct a water level sequence for major lakes and reservoirs in the Yellow River basin from 2003 to 2022. The paper also compares the accuracy of different algorithms. The results indicate that the built-in algorithms of ICESat-2 and CryoSat-2 can effectively extract water level data, with ICESat-2 demonstrating the highest precision. Rainfall is positively correlated with the water levels of inland lakes and negatively correlated with those of reservoirs. Additionally, global warming and the consequent increase in evaporation are significant factors affecting water level changes.

For hyperspectral data, due to the narrow sensor operating band, small sensing energy, and the various bands in the imaging process to produce the sensor radiation error and atmospheric effects are intertwined, resulting in unilateral consideration of the atmospheric factors of the correction method is difficult to obtain high-precision results. Therefore, from the principle of radiative transfer, this paper takes the Zhuhai-1 hyperspectral data as an example, combines two typical features of high and low reflectance, proposes an integrated radiation and atmosphere correction model for hyperspectral data, and compares its correction results with FLAASH, QUAC and EMPL methods, and at the same time, selects three types of typical features, namely, bare soil, vegetation and water bodies, for accuracy analysis. The results show that the correction results in this paper can effectively correct the effect of atmospheric scattering, the correlation coefficients of the correction results are all above 0.9, the spectral angle SAM are all located within 13°, the maximum root mean square error RMSE is not more than 0.15, and the correction results are stable, especially in the case of low-reflective water bodies, and the effect is much better than other atmospheric correction methods.

The response of ecological environment quality to climate change and its relationship with water and sediment changes can provide scientific basis for ecological protection and high-quality development in the Yellow River basin. Based on the MODIS data products, temperature, precipitation, runoff and sediment transport data from 2000 to 2022, combined with the remote sensing ecological index (RSEI) of multiple indicators, the spatial and temporal variation characteristics of RSEI in the Yellow River basin and its response to temperature and precipitation, as well as its relationship with runoff and sediment transport are analyzed by linear regression and correlation analysis. The results show that: ①From 2000 to 2022, the RSEI of the Yellow River basin and the upper and middle reaches of the Yellow River basin showed an upward trend, while the lower reaches showed a downward trend. The middle reaches of the Yellow River basin had the fastest growth rate and the largest proportion of significantly increased area. ② The RSEI of the Yellow River basin is significantly positively correlated with annual average temperature and annual precipitation, with an area ratio of 17.29 % and 27.97 %, respectively, concentrated in the central Loess Plateau region. ③The annual runoff and annual sediment load in the upper and middle reaches of the Yellow River basin are significantly positively correlated with RSEI. The overall and downstream areas of the basin are positively correlated with annual runoff and negatively correlated with sediment load. Under the background of climate change, the ecological environment quality of the Yellow River basin is generally improving, and the internal relationship between the change of ecological environment quality and the change of runoff and sediment transport still needs further study.

With the rapid development of the aerospace satellite industry, the manufacturing and launch of satellites have entered a phase of rapid expansion. Concurrently, as mass satellite production takes precedence, the posture measurement of satellite sensors is progressively advancing towards automation and intelligence. In response to the requirements of automated attitude determination and control measurement technology for large-scale satellites, corresponding technical methodologies have emerged. Building upon the existing automated attitude determination and control measurement technology for mass-produced satellites, this paper conducts a thorough analysis of the optical crosshair extraction model and automated search algorithm within the current models. Addressing identified shortcomings, the paper proposes novel optical crosshair extraction and automated search algorithms, significantly enhancing the efficiency of satellite automated attitude determination measurements.

Mountainous scenic areas rely on distributed camera networks for real-time visitor perception, a crucial aspect of intelligent scenic area development. This paper exploits these networks to propose a method for dynamic and precise passive perception of visitors in mountainous scenic areas through distributed cameras. By enhancing the YOLOX network with a convolutional block attention module (CBAM) and adaptive spatial feature fusion (ASFF) techniques, and incorporating a dynamic target tracking algorithm, this method achieves accurate detection and tracking of visitors' movements. Subsequently, a localization algorithm is utilized to derive real-time positional information of visitors. The results demonstrate the method's effectiveness in near-field passive perception and spatial positioning within mountainous scenic areas, achieving a detection precision over 90%, spatial positioning accuracy within 1 m, and a root mean square error (RMSE) of less than 1.109 4. This provides a technical solution and information support for the passive real-time dynamic precise perception and safety management of visitors in areas with weak or no satellite navigation signals.

When extracting water information from single-polarization radar images, the ability to distinguish ground objects with similar scattering characteristics to water bodies is not high. Based on the image data of full-polarization GF-3 satellite, scattering characteristics of different polarization modes are analyzed and water index characteristics of different polarization modes are constructed to obtain the feature image data constructed by combining index features. The feature image combined with object-oriented method is used to extract water information quickly and accurately. Full polarization Gaofen-3 is selected as the experimental data, and the experimental results show that the water index feature image data constructed with different polarization data has better water extraction effect and higher precision.

Earthquakes usually trigger a large number of landslides that seriously threaten the safety of people's lives and property. How to effective use multi-source remote sensing technology to rapidly and accurately classify post-earthquake landslides is one of the key technologies for emergency response to earthquake disasters. In this paper, based on the GEE platform, the co-seismic landslides in Luding county after a magnitude 6.8 earthquake are extracted using multi-source remote sensing data (optical images, digital elevation model, synthetic aperture radar images) combined with machine learning algorithms (support vector machine (SVM), random forest (RF), gradient boosted tree (GBT)). The results show that the RF model performs best when there are few features (overall accuracy OA=93.1%, Kappa=0.859) and the GBT model performs best when there are a wealth of features (OA=96.3%, Kappa=92.3). Topographic features had the highest importance for landslide classification, followed by remote sensing spectral indices, and SAR image features had the lowest importance. Based on the best landslide classification model GBT, this study obtained the distribution map of landslides in the seismic area, with a landslide area of about 25.86 km². The results of this paper provide an important reference for the rapid identification of seismic landslides, in terms of model and feature selection.

Spaceborne atomic clock is a key part of satellite navigation system, which provides stable and reliable high-precision time-frequency signals for the system. Its performance determines the user's navigation, positioning and timing accuracy, so it is particularly important to carry out comprehensive performance evaluation of spaceborne atomic clock. This paper introduces the main methods and principles of the performance evaluation of spaceborne atomic clocks and the pre-processing process, and summarizes and analyzes the frequency characteristics, fitting precision, frequency accuracy, frequency drift rate and frequency stability of the clock difference data of spaceborne atomic clocks. A comprehensive performance evaluation of GNSS spaceborne atomic clocks is carried out with the after-the-fact precision clock difference products provided by GFZ. The results show that the fitting accuracy of Galileo spaceborne hydrogen clock is the best, and more than half of them are better than 0.1 ns. Galileo and BDS-3 spaceborne hydrogen clocks perform well in frequency accuracy analysis and frequency drift rate analysis. In terms of stability, Galileo spaceborne hydrogen clock, BDS-3 spaceborne hydrogen clock and GPS Ⅲ-A spaceborne rubidium clock are basically in the order of 10^-15/d. The research in this paper is of great significance in improving the reliability and stability of PNT.

Aiming at the problems of low localization accuracy as well as poor robustness of underwater visual navigation in weak texture environments, this paper proposes an underwater monocular visual inertial odometry based on the sparse direct method. The method is based on the assumption of pixel gray scale invariance, and estimates the camera position by optimizing the photometric error, avoids the complex process of feature point extraction and matching, thus improves the real-time and robustness of navigation, while combines the data from the inertial measurement unit (IMU) and uses error state Kalman filter (ESKF) for data fusion to further reduce the error, in order to improve the stability of navigation of autonomous underwater vehicle (AUV) in underwater complex environments. The stability and accuracy of navigation in underwater complex environments are improved. The experimental results show that the error reaches the centimeter level and is reduced compared with the vision-only algorithm, which proves that the system can effectively fuse vision and inertial information, and has high accuracy and robustness in the field of underwater navigation.

In this paper, we propose a moving window-based Doppler smoothing pseudorange algorithm for multi-source fusion navigation systems where GNSS pseudorange observations are greatly affected by the environment, which results in a decrease in positioning accuracy. Firstly, we establish a GNSS/IMU/monocular vision tightly combined navigation system model based on the optimisation of the factor graph, and then we use open-source datasets to validate the algorithm and compare it with the conventional weighted Doppler smoothing pseudorange algorithm. and compared with the conventional weighted Doppler smoothing pseudo-ranging algorithm. The results show that the positioning accuracy of the algorithm in the three directions of E, N and U in pseudo-range positioning is 1.591, 2.892 and 2.001 m respectively, and the positioning accuracy is improved by 50.7 %, 61.7 % and 56.8 %. The positioning accuracy of the multi-source fusion navigation and positioning system is 1.390, 2.561 and 1.606 m, respectively, which is 9.8 %, 20.0 % and 11.1 % higher than the positioning accuracy in the three directions before smoothing. At the same time, compared with the conventional weighted Doppler smoothing, the positioning accuracy is improved by 4.7 %, 3.1 % and 0.5 % respectively.

A single-epoch satellite classification and screening strategy is given to address the issue of reduced efficiency of traditional RTK single-epoch integer ambiguity resolution of main and auxiliary correlation method (MAXCOM) using the increased number of observation satellites. All satellites in a single-epoch data are classified as reference-satellite, main-satellites, and auxiliary-satellites. The MAXCOM algorithm is used to determine the ambiguity of the main-satellites and the DC algorithm is used to determine the ambiguity of the auxiliary-satellites. A new algorithm of RTK single-epoch integer ambiguity fast resolution, called MAXCOM-DC combination algorithm is proposed. Through a dynamic positioning experiment and result analysis using a set of BDS-3 measured data with 900 consecutive observation epochs, it is shown that the MAXCOM-DC combination algorithm can avoid the impact of auxiliary-satellites on the search efficiency of the main-satellites integer ambiguity in the application of the MAXCOM algorithm. When the number of main-satellites is set to 6, the efficiency of single-epoch integer ambiguity fast resolution is increased by an average of 98.5%, and the success rate of single-epoch ambiguity resolution is 99.9%. The proposed algorithm provides a new algorithm for single-epoch RTK fast positioning in high-sampling data.

BDS-3 is providing the high-precision location-based services. However, due to the impacts of the complicated application environments of users side, the high-quality services of BDS-3 constellation are confronted with challenges. To improve the observation quality control model based on the cycle slip detection solution, a cycle slip detection model of BDS-3 frequency-wide observations is proposed, where the multi-frequency BDS-3 observations are fully used. Firstly, the cycle slip detection values of the epoch-difference phase are constructed by the combinations of the multi-frequency geometry-free(GF) equations. Then, the geometry-based(GB) equations of multi-frequency code and phase observations are used to construct the wide-lane ambiguity of cycle slip detection values. Finally, a method to estimate the model coefficients of the multi-frequency combination models are proposed based the least square algorithm. According to the observations of the static stations and vehicle onboard, it is indicated that a variety of linearly independent cycle slip detection can be obtained by taking the full frequency of BDS-3 into account. Meanwhile, the proposed model can fully utilize the BDS-3 multi-frequency observations to detect the cycle slip. Therefore, It is of great significance to perfect the quality control model and realize the innovative application of BDS-3 based on the proposed method.

In the field of environmental protection and natural resource survey applications, this paper combines the external data collection of oblique aerial photography and measurement using UAVs in the experimental area. It aims at issues such as large elevation differences and weak textures in mountainous areas and complex buildings, and designs specifically for these challenges. By increasing the diversity of flight scales, the paper uses an optimized SFM＿MVS algorithm to jointly process multi-scale data, which compensates for a series of problems that ordinary consumer-grade cameras face in areas with large elevation differences. The construction of 3D realistic models of mountains, forests, and ancient buildings in a key area of Guangzhou has been successfully completed, with the completeness of the model improved by about 30% compared to the results of traditional UAV photogrammetry flights. The flight design and joint processing methods presented in this paper provide a beneficial exploration for the collection of 3D realistic models and environmental resource surveys in complex areas.

Territorial spatial planning is a scientific guide to guide the national space development, coordinating all kinds of spatial planning. In the process of design and implementation of territorial spatial planning, it is necessary to use a large number of collected multi-source heterogeneous data after processing. This study aims at the differences of spatial planning data from different sources in format, coordinate reference, attribute structure and so on. Based on the technology of big data, GIS, artificial intelligence and various self-research algorithms, a method of spatial planning data fusion is proposed in this study, which consists of four steps: data cleaning, data conversion, place name and address matching connection and batch processing. The techniques and methods used are integrated into a“One-click” tool to realize batch fusion of multi-source heterogeneous spatial planning data. The fusion method of multi-source and heterogeneous spatial planning data designed in this study can share the data of different data sources, different storage formats, different space-time, different scales and different coordinate systems without loss, and process the data in batch and automatically, so as to improve the utilization efficiency and management level of territorial spatial planning data.

With the continuous progress and evolution of technology, handheld 3D laser scanners, as efficient and high-precision surveying and mapping instruments, have received widespread attention and application in various industries in recent years. In response to the shortcomings of traditional instruments used for facade measurement during the renovation of old buildings in urban renewal, such as low efficiency, difficulty in drawing complex buildings, and inability to guarantee accuracy, this article explores the technical characteristics and parameters of handheld 3D laser scanners. It focuses on introducing the technical process and application case studies of facade measurement for the renovation of old buildings in urban renewal. Finally, it looks forward to the application prospects of handheld 3D laser scanners, which is of great significance for improving the technical level of facade measurement for the renovation of old buildings in urban renewal.

Reservoir capacity curve is the basic data basis of reservoir regulation and storage management, and its accuracy affects the play of various functions of the reservoir. As time goes by, the original storage capacity curve can not meet the management requirements, and the traditional storage capacity curve calculation method is inefficient and the updating cost is high. In order to solve this problem, a storage capacity curve updating method based on airborne LiDAR and water level monitoring is proposed in this paper. The method adopts LiDAR aerial photography during dry season, and realizes the update of the storage capacity curve through the processing of airborne LiDAR point cloud data, the construction of DEM model, and the extraction of water-area-storage capacity. The experimental results show that the storage capacity curve obtained by the method is consistent with the practice, the results are correct, scientific and reliable, and can realize the rapid update of the storage capacity curve, which can provide decision-making basis for water resources management, regional flood water regulation and storage, drought relief and so on.

Aiming at the problems that the deformation of railway subgrade in seasonal freezing area is easily affected by environment and the accuracy of traditional single variable deformation prediction model is insufficient, a TSCSO-SVR model combining multi-source meteorological data is proposed in this paper. Firstly, PS-InSAR technology is used to monitor the deformation of roadbed, and the correlation between meteorological factors and roadbed deformation is analyzed. Then, the improved sand cat swarm algorithm (TSCSO) is obtained by combining nonlinear decline, dynamic disturbance and spiral search, and the TSCSO-SVR subgrade settlement prediction model is constructed. Finally, combined with the measured data of a section of railway in Shihezi, Xinjiang. The results show that the prediction effect of multivariate model is generally better than that of univariate model. Compared with other models, TSCSO-SVR prediction model has the highest prediction accuracy and has good application value.

To enhance the automation of video data from monitoring taskson subway open-cut construction, and to intelligently survey the geometry of earth berm during the tasks, this paper proposes a method for the intelligent measurement of geometric parameters of earth bermfrom monitoring imageson subway open-cut construction. An AFU network for precise segmentation of subway open-cut construction scenes is established, incorporating skip connections with attention modules and triple cross-scale attention modules to enhance the perception of subtle structures and local features. Based on the principle of spatial stratification of steel supports, a pixel length calculation model is constructed to accurately estimate the width and slope angle of earth berm. Tests on three subway open-cut construction sites show that the AFU network achieves optimal performance in ablation experiments and comparisons with other deep learning networks. The geometric size measurements of the earth berm are closely matched with actual values. The results of this study have theoretical and practical value for risk management during construction processes.

The coal transporting trestle is inevitably prone to tilting under its own load and external environmental influences. To address the inefficiencies, high costs, and poor real-time performance of traditional coal conveying gantry monitoring methods, a lightweight wireless inclination real-time measurement system is designed based on the principle of inclination measurement using MEMS three-axis gravity acceleration sensors. By integrating radio frequency (RF) technology and 4G CAT.1 technology, the system achieves on-site short-range wireless data acquisition and remote wireless transmission, featuring characteristics such as lightweight, low power consumption, high precision, and quick installation. Experimental tests demonstrate the stability and reliability of this measurement system, with an inclination measurement accuracy of up to 0.002° in static environments. Combined with internet of things (IoT) cloud platforms, this system can monitor the tilting attitude of the coal transporting trestle in (near) real-time. Application results indicate that the wireless inclination measurement system achieves an accuracy of 0.01° by effectively removing environmental noise generated by belt vibration through filtering regression algorithms. It promptly issues warning messages when the cumulative tilt exceeds 0.18°, ensuring the stable operation of the coal transporting trestle.

Comprehensively promoting urban digital transformation is an important strategy for Shanghai to promote high-quality development.The scientific,refined, and intelligent governance of mega cities urgently requires the construction of a three-dimensional spatial geographic digital base based on geographical entities that can accurately map the physical world and integrate real-time urban operation information.This article analyzes the technical bottlenecks and difficulties of traditional surveying and mapping technologies in spatial geographic information collection, information fusion, and application.It also proposes ways to build a three-dimensional spatial geographic digital base in Shanghai from three aspects: rapid acquisition of spatial geographic information, precise fusion of multi-source information, and intelligent application of multiple scenes.A new model of geographic information data achievements and services for mega cities has been formed under the background of urban digital transformation.

To address the poor display of road mark in large-scale real-world 3D scenes,as well as the problem of conflicts or occlusions between road marks and real-world 3D models,the method involves preprocessing road data to generate annotation points in advance,and then publishing a road annotation query service interface. The real-world 3D client queries road annotation information and performs the fusion display process. The fusion visualization of road annotations and real-world 3D models has been achieved,exhibiting features such as computational efficiency,non-occlusion,and compatibility with large-scale real-world 3D applications. This method effectively solves the problem of displaying road networks in real-world 3D scenes,providing important semantic data for real-world 3D,and significantly improving the effectiveness of real-world 3D applications.

In some application scenarios, there is a need for concurrent utilization of both GIS 3D engines and gaming engines. This paper proposes a cross-platform synchronous matching positioning algorithm. The aim is to achieve flexible configuration of suitable 3D engines according to different visual scale 3D visualization requirements, through adjusting the synchronous expression of the scene. This also resolves issues such as inaccurate positioning and discontinuous experience during scene switching under different 3D engines. This algorithm has been successfully applied in practical cases, providing a convenient solution for the effective integration and complementary advantages of GIS 3D engines and game engines.

In the survey of major modern urban projects and underground drainage network, the measurement of large sewage caissons is an important but difficult task. The traditional method of frogmen going down to the well to explore has been gradually replaced by high-precision sonar detection systems due to its high risk factor and large measurement errors. In order to achieve highprecision and highefficiency measurement of large sewage caissons, this paper developes a multi-beam sonar caisson measurement system, and compares it with the dual-axis scanning sonar measurement system from multiple angles. The experimental results show that the multi-beam sonar measurement system is light in weight, small in size, high in precision, high in field measurement efficiency, and high in point cloud density. It can quickly obtain the three-dimensional shape of the well chamber and inlet and outlet of the water storage caisson. With the advancement of technology, the sonar measurement system will become an effective tool for measuring urban underground water storage spaces such as pipelines, caissons, and hollow areas.

The accuracy of real estate area surveying and mapping results is of great significance to safeguarding the rights and interests of both parties in real estate transactions. For buildings with complex and irregular indoor spaces, it is difficult to obtain the indoor effective area efficiently and accurately using traditional measurement methods such as laser rangefinders and tape measures. Taking the indoor area surveying and mapping project of a loft building with a special-shaped internal space as an example, a handheld 3D laser scanner is used to quickly obtain massive indoor point cloud data, and a region growing algorithm is proposed to automatically extract the indoor floor model of the building based on the ground model. It can solve the outline of the internal space with a height greater than 2.2 m, and then extract the outline point cloud of the effective indoor building area of the building. Use the point cloud to draw the outline of the effective area through human-computer interaction and other methods, and finally calculate the effective indoor real estate area of the building. Compared with traditional real estate measurement methods, the use of handheld 3D laser scanners for on-site operations is not only highly efficient, but the massive point cloud data obtained can also more comprehensively reflect the indoor spatial structure of special-shaped buildings, and the calculated property area is also more accurate.