Table of Content

25 August 2024, Volume 0 Issue 8

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Design and implementation of the GNSS/INS integrated software for bridge monitoring based on GINav

MA Weihao, DAI Wujiao, YU Wenkun, LI Xin

2024, 0(8):  1-7.  doi:10.13474/j.cnki.11-2246.2024.0801

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In large-scale bridge monitoring,the accuracy and reliability of GNSS positioning are severely affected by environmental factors such as obstruction from bridge cables,towers,fences,and reflections from passing vehicles.INS technology operates autonomously after initial alignment,eliminating the influence of external environmental factors.By combining GNSS and INS technologies,GNSS's resistance to interference and positioning accuracy can be significantly improved.Therefore,targeting the requirements and characteristics of bridge deformation monitoring,we have designed and implemented GNSS/INS bridge deformation monitoring software based on the open-source navigation software GINav.This software includes features such as visualization of monitoring sites,automatic matching of raw data,IMU downsampling,GNSS/INS combined solution computation,and result analysis and evaluation.Vibration table simulations of bridge vibrations show that compared to GNSS-RTK technology,the GNSS-RTK/INS combination achieves significantly improved accuracy,with a mean error reaching 1/20 of the allowable deformation value for deformation monitoring,meeting the precision requirements of bridge deformation monitoring.

Multimodal data collection and high-precision point cloud map construction for assisted intelligent driving under the pre-fusion strategy

LIU Chun, MA Xiaolong, QI Yuanfan, LI Yanyi, QIAO Yihong

2024, 0(8):  8-12,19.  doi:10.13474/j.cnki.11-2246.2024.0802

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With the rapid development of artificial intelligence and ubiquitous sensing technology, intelligent assisted driving systems based on multimodal data fusion processing are gradually entering households. The core of intelligent driving technology relies on advanced artificial intelligence and ubiquitous sensor technology to enhance or replace the perception, decision-making, and execution capabilities of drivers. Among them, real-time, accurate, and robust perception of the road environment is an important part of vehicle intelligence. This article introduces a vehicle platform that integrates multiple sensors for multimodal data collection, and provides basic point cloud data services for assisted intelligent driving by constructing high-precision point cloud maps. Different from the “post-fusion” strategy of multi-source data fusion, this article adopts the strategy of “pre-fusion” with time-space synchronized multi-source data. Based on completing the synchronization and calibration of multi-source sensor data, it provides intelligent driving vehicles with perception data that is consistent in time and space. At the level of map construction, this article achieves high-precision reconstruction of environmental point cloud maps by coupling with IMU and solid-state lidar, providing important technical support for the realization and further development of assisted intelligent driving.

Terrain measurement method of large scene 3D scanner based on automatic extraction of spherical target

ZHANG Xu, MAO Qingzhou, XU Haoxuan, LOU Liangwei, WANG Xiaokai

2024, 0(8):  13-19.  doi:10.13474/j.cnki.11-2246.2024.0803

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Aiming at the problem of lack of credible feature points in large point cloud terrain mapping of ground 3D scanner, a method flow of high-precision automatic positioning and extraction of spherical target center is proposed. The point cloud intensity map is obtained from the 3D point cloud, and the intensity map is divided into multiple subgraphs. The candidate target region of interest(ROI) in the subgraph is obtained by using the existing image processing algorithm. An improved Zernike moment sub-pixel edge detection algorithm based on one-dimensional maximum entropy is proposed to accurately obtain the pixel set of candidate targets in each ROI. The credible target is determined by the relevant threshold of the target radius and the sphere rate.The point cloud of the trusted target is alternately performed to fit the ball and eliminate gross errors,and the accurate target center is obtained. The experimental results show that when the measurement range of the target ball with a diameter of 14.5 cm is within 80 m, the success rate of the algorithm to extract the target from the point cloud is 96.7%.Based on the method of automatically extracting the center of the spherical target,the scanning coordinate system is converted to the specified coordinate system,and the mean square error of the three items(elevation mean square error/plane mean square error/3D space error)is less than 1 cm.

Identification characteristics and potential analysis of geological hazards in realistic 3D scenes

WANG Defu, LIU Li, LI Yongxin, ZHANG Zhiqiang, LUO Chao, LIAO Yangyang

2024, 0(8):  20-25.  doi:10.13474/j.cnki.11-2246.2024.0804

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Accurate identification and analysis of geological hazards is a crucial step before prevention and early warning. Compared to 2D interpretation environments, the 3D reality of real scenes highlights more favorable data advantages due to its 3D and realistic characteristics. This article takes the real scene 3D construction as the background and adopts the 3D visual analysis method to establish a total of 12 3D identification characteristics for landslide tension cracks, shear cracks, fresh landslides, landslide walls, falling platforms, collapsed dangerous rocks, slope foot accumulation, debris flow source area, circulation area, and accumulation area; Using 3D webGIS technology to analyze the 3D characteristics of landslides and collapses, and analyzing and summarizing the potential of real-world 3D applications from geometric information, image features, micro topography, and other aspects. The results indicate that real-world 3D provides a new dimension for geological hazard interpretation, enhances the ability to identify geological hazards, and the interpretation results are more in line with reality, which helps to improve interpretation accuracy. The research results can provide inspiration for real-time 3D applications and provide reference value for high-quality identification of geological hazard hazards.

Remote sensing image water body extraction based on U-Net, U-Net++ and Attention-U-Net networks

LI Zhenxuan, HUANG Miner, GAO Fei, TAO Tingye, WU Zhaofu, ZHU Yongchao

2024, 0(8):  26-30.  doi:10.13474/j.cnki.11-2246.2024.0805

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Currently, the application of deep learning in the extraction of water bodies from high-resolution remote sensing images has become a research hotspot in the remote sensing field. Among them, algorithms based on the U-Net network have demonstrated good performance in water body extraction. However, there is scarce research that provides in-depth and detailed comparisons of the performance differences of different U-Net network algorithms in water body extraction tasks. Therefore, this article selects three convolutional neural networks, named U-Net, U-Net++, and Attention-U-Net, and based on the GID dataset, draws conclusions through experiments and quantitative analysis. The results indicate that: U-Net++ achieves the highest training accuracy, followed by U-Net and Attention-U-Net, with accuracies of 0.912, 0.907, and 0.899 respectively. U-Net++ exhibits superior edge extraction capability compared to the other two networks. In segmenting different types of water bodies and distinguishing non-water areas similar to water bodies in remote sensing images, U-Net++ shows significantly better extraction results, while U-Net and Attention-U-Net are prone to omission errors and exhibit suboptimal performance.

Research on BDS-3 B1C/B2a long baseline relative positioning

FANG Zhuo, WANG Lishiyun, MICHAEL Floyd

2024, 0(8):  31-36,53.  doi:10.13474/j.cnki.11-2246.2024.0806

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To verify the accuracy of the long baseline relative positioning of B1C/B2a signals from the BeiDou-3 Navigation Satellite System (BDS-3), this study firstly conducts a data quality analysis of the continuous 10day observation data from 8 Multi-GNSS experiment (MGEX) stations in the Australian region for day of year (DOY) (304～313) in 2023. Then, an improved GAMIT 10.76 software is used to perform long baseline relative positioning solutions on the observation data. Finally, the quality of theBDS-3 and GPS signal combination data at different frequency points and the accuracy of dual-frequency ionosphere-free linear combination baseline solutions and network adjustments are evaluated. The experimental results show that the data quality, signal-to-noise ratio, and pseudorange multipath error of B1C/B2a, B1C/B2b, and B1C/B3I frequency signal combinations are comparable and superior to the L1/L2. The normalized root mean square(NRMS) of the B1C/B2a is slightly better than that of B1C/B2b and L1/L2 frequency points, significantly superior to the B1C/B3I. The ambiguity fixing rate of B1C/B2a and B1C/B2b is basically the same, significantly superior to the B1C/B3I, but inferior to the L1/L2. Furthermore, the experimental results indicate that compared to the B1C/B3I, the average standard deviation(STD) of the baseline components east(E), north(N), and up(U) and the average 3D positioning error of B1C/B2a are reduced by 29.4%, 27.0%, 30.0%, and 41.0%, respectively.

The spatio-temporal distribution of ecosystem health and its driving factors in the Yunnan-Guizhou Plateau region

ZHANG Xuepeng, ZENG Cheng, GOU Peng, HUANG Yingshuang

2024, 0(8):  37-41.  doi:10.13474/j.cnki.11-2246.2024.0807

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To solve the problem of the spatio-temporal distribution of ecosystem health (EH)and the influence of its driving factors in the Yunnan-Guizhou Plateau, this paper constructs an EH assessment model of “vigor-organization-resilience-service” to analyze the spatio-temporal distribution of EH from 2000 to 2020, and then uses the XGBoost model and the SHAP explanation model to analyze the specific functional relationship between each driving factors and EH. The results showed that: ①From 2000 to 2020, the EH of the western region in Yunnan-Guizhou Plateau is better than that of the eastern region, and the low EH counties are concentrated in the northeast region. ②Weak EH counties decreased from 40% in 2000 to 24% in 2020, the overall trend of global improvement is present. ③There is a decreasing cubic function relationship between urbanization level, rainfall and EH, there is an increasing cubic function relationship between temperature, normalized vegetation index and EH, and there is a fluctuating quadratic function relationship between elevation and EH. This study aims to provide a new scheme for region EH monitoring and a reference for ecological protection and restoration in Yunnan-Guizhou Plateau region.

Landslide dynamic monitoring technology by integrating LiDAR point cloud and UAV image

XU Yuxiang, HU Qingwu, DUAN Yansong, LI Jiayuan, AI Mingyao, ZHAO Pengcheng

2024, 0(8):  42-47.  doi:10.13474/j.cnki.11-2246.2024.0808

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Landslide is a kind of natural disaster with great harm. How to monitor it efficiently and accurately has important research value and practical significance. Using the technology of LiDAR and UAV aerial photography in landslide monitoring can quickly, safely and accurately obtain ground information of landslide area. In this paper, a dynamic monitoring method of UAV image landslide based on LiDAR point cloud is proposed. Firstly, point cloud and images are used to obtain high-quality DSM reliably, and then a filtering algorithm based on irregular triangulation network and slope fusion is designed to filter out low vegetation in DSM and produce high-precision DEM. Finally, the dynamic monitoring of landslide area is realized through the difference of two DEM. In this paper, the LiDAR data and UAV image data of a slope area near Huangdeng hydropower station are used to carry out experiments. The results show that the landslide dynamic monitoring method intuitively judges the change of landslide topography and has a certain application prospect.

Baseline correction method of Lutan-1 for topographic mapping with and without control

LI Peizhen, XIE Zhipeng, LI Tao, ZHANG Xiang

2024, 0(8):  48-53.  doi:10.13474/j.cnki.11-2246.2024.0809

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In the process of generating digital surface models (DSM) using interferometric synthetic aperture radar (InSAR), centimeter level baseline error will lead to meter level height error. For the Lutan-1(LT-1) topography mapping mission, an InSAR DSM generation method without control based on far and near beams baseline calibration is proposed in this article. This method obtain accurate baseline correction parameter with calibration sites, and applies the baseline correction parameter to other LT-1 bistatic data in Datong, reducing the mean square error of height by 0.98 m. Meanwhile, we compare and analyze this method with traditional baseline estimation methods assisted by control points. The results show that the baseline correction parameter obtained by baseline estimation method assisted by control points is not applicable to other LT-1 data, while the method in this paper can be robustly used for high-precision DSM generation in long-term and large areas, providing technical support for InSAR topography mapping mission.

Identification method of underground disease body based on 3D ground penetrating radar and PSO-ELM: a case study of Jinniu district of Chengdu

XIE Xiaoguo, LUO Bing, HUANG Changbing, ZHANG Yuling, YANG Shengbo

2024, 0(8):  54-59.  doi:10.13474/j.cnki.11-2246.2024.0810

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It is the key to prevent road collapse to accurately identify the types of underground diseases in urban roads. 3D ground penetrating radar (GPR) is the most commonly used road disease detection technology, but its data interpretation is mainly manual interpretation, which has the disadvantages of heavy workload and low recognition accuracy. Taking Jinniu district of Chengdu as an example, this paper proposes a PSO-ELM automatic disease body prediction model based on the analysis of the spectral characteristics of underground disease bodies. Seven characteristic parameters, maximum peak amplitude, maximum trough amplitude, amplitude variance, kurtosis factor, mean square value, spectrum variance and spectrum mean value, are selected as the input of the model. PSO is used to optimize the parameters of the ELM model. The optimized model is used to identify the disease body in the study area. The results show that PSO-ELM model has a disease recognition accuracy of 92.5%, which is significantly better than ELM model and traditional artificial image feature recognition method.

Improving YOLOv5's domestic optical image radiation anomaly detection method

SHI Yijian, TAN Hai, ZHONG Xuhui

2024, 0(8):  60-65,72.  doi:10.13474/j.cnki.11-2246.2024.0811

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With the continuous increase in the number of domestic optical satellites, the obtained satellite image data has shown a large-scale increase. There is a considerable proportion of image radiation anomalies in the images obtained by satellites and processed through sensor correction. Image radiation quality is an important factor determining the evaluation of image quality inspection level. Currently, its inspection mainly adopts human-computer interaction. In response to the current radiation problem in optical image quality inspection, an improved YOLOv5 deep learning network is proposed to identify targets in radiation abnormal areas. Integrate the improved light BiFPN feature fusion network and ShuffleNetV2 backbone network into YOLOv5s. By exploring the principle of image radiation anomalies, this network can accurately determine the range of targets in radiation anomaly images. The trained model can effectively detect the range of radiation issues through anchor frames, lay the foundation for further model deployment and application.

Dynamic deformation analysis of super high-rise buildings by integrating GNSS and accelerometers

WANG Shuai, YIN Chuan, SUN Yu, WANG Jian

2024, 0(8):  66-72.  doi:10.13474/j.cnki.11-2246.2024.0812

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In view of the serious multi-path error and unreliable monitoring accuracy of GNSS in the deformation monitoring of super high-rise buildings, this paper constructs a data fusion algorithm based on Kalman filtering and RTS smoothing for the fusion of GNSS and accelerometer data by constructing a systematic trend separation and filtering and denoising model with tunable Q-factor wavelet transform. The dynamic deformation information in the fused displacement is extracted using the tunable factor Gabor wavelet transform, and the validity of the fusion model is verified by comparing with the dynamic displacement after the quadratic frequency domain integration of the accelerometer data. The simulation results show that the fusion displacement algorithm constructed in this paper can effectively restore the real data, the root mean square error of the fused displacement data is 0.088 5 mm, the correlation number is 0.993 4, and the signal-to-noise ratio is 17.53. Through the super high-rise building measured data, the method in this paper achieves the noise cancellation and the data fusion of GNSS and accelerometer, and is able to extract the dynamic deformation information in the fused data, which improves the accuracy of the deformation monitoring and provides an effective method for the analysis of dynamic deformation of super high-rise buildings.

Aircraft target detection based on improved YOLOv5 in remote sensing imagery

HUANG Ziheng, RUI Jie, LIN Yuzhun, WANG Shuxiang, LIU Xiangyun

2024, 0(8):  73-78,89.  doi:10.13474/j.cnki.11-2246.2024.0813

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In response to the problems of slow detection speed, low accuracy, complex image background, difficulty in distinguishing background and target, slow convergence speed and low efficiency in the prediction process of existing object detection algorithms in remote sensing image aircraft target detection, this paper adopts two optimization strategies, namely the introduction of attention mechanism and replacement loss function. Based on the YOLOv5 algorithm model, to improve the algorithm's detection performance for aircraft targets, the training results on the DOTA dataset show that by introducing the CBAM attention mechanism in the C3 module of the YOLOv5 network architecture backbone, the algorithm's detection performance has been significantly improved. Among them, the accuracy of the model training results P has been improved by 6%, the recall rate R has been improved by 2%, and the average precision(mAP) value has been improved by 2.8%. In the YOLOv5 prediction process, Focal EIoU and SIoU loss functions are used to replace the original CIoU loss functions. The experimental results show that the improved algorithm model significantly improve the regression accuracy, among which the model optimized using SIoU loss function have the best effect. The precision(P) of the model training results increased by 4.3%, the recall rate R increased by 2%, and the average precision(mAP) value increased by 2.7%. The improved YOLOv5 algorithm provides a reference for achieving high-precision real-time detection of aircraft targets.

InSAR study of current tectonic deformation and seismic hazard analysis of the Kongur tension system in northeastern Pamir's

CHEN Rongliu, LI Jie, LIU Daiqin

2024, 0(8):  79-83,89.  doi:10.13474/j.cnki.11-2246.2024.0814

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The Pamir Syntaxis is characterized by frequent strong earthquakes and has been a key area of interest in continental dynamics research. The Kongur tension system located within the Pamir is one of the most important tensional faults within the central and eastern Pamir. Studying its current tectonic deformation is of great significance for exploring the current deformation state,dynamic mechanism, strong earthquake activity and disaster prevention in the Pamir. This paper processed Sentinel-1 SAR data from two orbits during the period from 2018 to 2022, obtained a high-density 3D deformation rate field in the study area, and discussed the seismic hazard in the study area in combination with Coulomb stress. The results show that in the east-west direction, the Muji Fault had a dextral strike-slip of 10 and the Kungai Shan South Fault and the Kongur Shan Fault had a tensile rate of 11 mm/a and 5 mm/a, respectively. The Mushtag Fault almost did not undergo tensile movement; vertically, the south wall of the Muji fault rose by 7 mm/a and the north wall rose by 3 mm/a. Both sides of the Kungai Shan South Fault, the Kongur Shan Fault, and the Mushtag Fault had a certain degree of uplift, about 3 mm/a; static Coulomb stress indicated that the northern section of the Kongur tension system is a dangerous area for future strong earthquakes.

AI-based remote sensing identification of waterway obstructions using GF-1 multispectral imagery

GU Zhujun, LIU Bin, ZHU Li, QIU Shineng, REN Xiaolong, WU Jiasheng, XIAO Bin, LIAO Guanghui, YAO Lulu

2024, 0(8):  84-89.  doi:10.13474/j.cnki.11-2246.2024.0815

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The obstructions in waterways are significant factors affecting flood disasters, thus their efficient and precise management has garnered widespread attention. Traditional manual inspections cannot meet the needs for efficient and precise applications; the integration of artificial intelligence (AI) with remote sensing technology applications is an inevitable path. However, the performance of many AI models in remote sensing applications is not yet clear and urgently needs further investigation. This research takes the Datengxia reservoir area in Guangxi as an example to study the construction methods of AI recognition models for obstructions in waterways using remote sensing. Based on GF-1 remote sensing imagery, an obstructions in waterways training dataset is constructed. Using ResNet101 as the core Network, six current mainstream semantic segmentation models are adopted, including PSPNet, PAN, MANet, FPN, DeepLabV3+, and UNet++. The models are trained for the identification of obstructions in waterways to further evaluate their precision and efficiency. Key findings include: ①Deep learning models utilize ResNet101 as the backbone Network shows excellent performance in identifying obstructions in waterways with all models achieving an F1 score above 0.70 and IoU above 0.58. Among them, the DeepLabV3+ model, which combines atrous convolution and global pooling techniques, achieves an F1 score of 0.82 and an IoU of 0.72, demonstrating significant advantages in capturing contextual information and micro-features. ②PSPNet, despite having a lower number of parameters, exhibits high processing efficiency and accuracy, capable of handling 8 samples per batch with a frame rate of 10.49. In summary, DeepLabV3+ stands out in precise identification and contour delineation, while PSPNet shows great potential in large-scale data processing. The study results can provide a reference for constructing AI remote sensing models and offer technical support for waterway safety monitoring.

Pavement cracks recognition by using UAV image based on GrabCut method

WU Guangchen, LIU Yan

2024, 0(8):  90-95.  doi:10.13474/j.cnki.11-2246.2024.0816

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The crack extraction by UAV images is one of the hot issues in recent years,for the problem of strong edge information interference of UAV image, a crack recognition method based on GrabCut operator is proposed. Firstly, the method uses GrabCut operator to extract the foreground pavement with cracks, and then uses denoising, edge detection and double threshold contour recognition methods to detect pavement cracks. This method eliminates a lot of false edge information and secondary noise interference, and realizes automatic crack recognition of high resolution UAV image. The experimental results demonstrate that the road surface extraction method based on GrabCut operator is superior to color extraction algorithm and watershed algorithm, and it is suitable for road surface extraction in complex scene, it is with high universality. At the same time, the method proposed in this study can quickly obtain fracture information, the detection scale can be manually controlled, and it is easy to realize multi-scale fracture information recognition. The research results can be applied to pavement crack location and identification, linear pavement facility detection, pavement disaster assessment and other fields.

Optimization and application of deep learning model-based subway tunnel defect detection

YOU Xiangjun, ZHAO Xia, LONG Sichun, WANG Jiawei, ZHENG Ying, KUANG Lijun

2024, 0(8):  96-101.  doi:10.13474/j.cnki.11-2246.2024.0817

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Aiming at the four common defects of subway tunnel, such as leakage, crack, structural plaster cracking and spalling, a defect detection method of subway tunnel based on laser radar scanning point cloud data and deep learning is studied.Firstly,the ACmix attention module is introduced into the YOLOv8 model to make the network take into account both global and local features, and improve the detection effect of small targets such as cracks and cracks.Then,the regression loss function is optimized, the convergence stability and regression accuracy are improved, and the detection error is reduced. Finally,the complete process of orthographic projection image preprocessing, batch detection and result fusion, and report generation of detection results is realized, and the defect detection of large-scale orthographic projection is efficiently realized. The experimental results show that under the condition that the IoU threshold is 0.5, the mAP of the improved YOLOv8 algorithm on the tunnel defect test set increases from 90.65% to 91.18%, and the AP of cracks increases from 77.89% to 78.70%. The intelligent detection of four common defects of subway tunnel based on LiDAR scanning is solved, and has been successfully applied in actual tunnel operation and maintenance engineering.

Elevation fitting method in high altitude area with large elevation difference based on deep learning

MA Xiaping, WANG Fengkai, ZHAO Qingzhi, GAO Yuting

2024, 0(8):  102-108.  doi:10.13474/j.cnki.11-2246.2024.0818

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The terrain elevation in high altitude areas with large elevation differences is complex and variable. Although traditional elevation fitting methods,such as linear functions,surface fitting,BP neural network and genetic algorithm improved neural network can achieve elevation fitting,their fitting accuracy in high-altitude areas with large elevation differences remains to be improved. To effectively fit the terrain elevation in high altitude areas with large elevation differences,this paper proposes an elevation fitting method based on deep learning,using the second order leveling measurement data of a railway control network in the western region. The method employs a multi-layer perceptron as the core model,and selects the suitable combination of optimizers and activation functions according to their characteristics,to capture the terrain features and elevation change patterns of the region,and achieve high-precision elevation fitting. The paper also analyzes the impact of different combinations of optimizers and activation functions on the model performance. The results show that the deep learning model outperforms the BP neural network and genetic algorithm improved neural network methods in elevation fitting in high-altitude areas with large elevation differences,with the lowest MSE,the smallest MAE,and the R² closest to 1. Among them,the combination of RAdam optimizer and GELU activation function performs the best. The elevation fitting method of the deep learning model has higher accuracy and better generalization ability,and can effectively adapt to the complex and variable terrain features of high-altitude areas with large elevation differences.

Applicability of three image matching models in map image matchings

HOU Jiaxin, CHE Xianghong, LIU Jiping, WANG Hongyan, WANG Yong, XU Shenghua, LUO An

2024, 0(8):  109-114,121.  doi:10.13474/j.cnki.11-2246.2024.0819

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With the rapid development of science and technology such as computer and artificial intelligence, there are a variety of intelligent image matching models based on deep learning. However, these models are mostly used for matching images with relatively regular texture features (such asportraits, building images,industrial parts, etc), and there is a lack of applicability research in map image matching with complex and diverse texture features. To this end, by taking three types of maps, namely the administrative division map, traffic map and thematic map as data sources, this study compares the map matching performances of the currently popular three image matching models, such as Superglue, COTR and GlueStick, by integrating semantic segmentation model (SAM). The visual matching performance, matching accuracy and matching efficiency results show that:①The boundary matching performance of GlueStick model is the best among the three types of maps, followed by Superglue, while COTR model has the worst matching performance.②Using SAM to extract map segmentation mask images is able to reduce peripheral features of the map, and further improve the matching performance of GlueStick and Superglue models where the accuracy are increased by 47.50% and 34.43%, respectively.③The matching efficiency of the COTR model is the lowest. While the matching efficiency of GlueStick model is lower than Superglue using the original map, their matching efficiency is comparable using the map segmentation masks. This study has important application values for contrast, recognition and review of map content.

Spatio-temporal evolution, prediction and ecological security pattern construction based on kNDVI: a case study of the Loess Plateau with severe soil erosion

ZHOU Kangsheng, YANG Dehong, HAN Yang, ZHOU Peng, JIANG Yuncheng

2024, 0(8):  115-121.  doi:10.13474/j.cnki.11-2246.2024.0820

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As a crucial ecological region in China, the Loess Plateau faces serious environmental challenges. How to accurately monitor and predict vegetation changes has become the focus of current research. This paper uses the kernel normalized difference vegetation index (kNDVI), which is more suitable for studying the Loess Plateau, to conduct a new exploration of the vegetation changes in this area from 2000 to 2019. The results reveal that 2001 and 2013 are the watershed of ecological structure transformation, and the high and low vegetation types show significant changes. In addition, to understand the evolution of vegetation in the future more comprehensively, we introduce the BP neural network and the GeoSOS-FLUS model for spatio-temporal prediction. We verify the applicability of the GeoSOS-FLUS model in kNDVI spatial prediction for the first time. We also find a significant increase in low and lower vegetation types predicted for 2020—2022. It is worth noting that although the slope of kNDVI has doubled compared to the past, its peak value (August) has slightly decreased, while the values in early spring and winter have increased. Finally, we use kNDVI and NDVI to construct the ecological security pattern of the Loess Plateau, and the comparative analysis results show that the ecological security pattern by kNDVI is better than NDVI. Further results reveal that the ecology of the northwestern Loess Plateau is more fragile and more affected by human activities.

An overview of geographical information security in crowdsourced updating of high-definition maps

GUO Yuan, LI Bijun, YING Shen, ZHONG Wei, ZHOU Jian

2024, 0(8):  122-127.  doi:10.13474/j.cnki.11-2246.2024.0821

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High-definition (HD) maps, as crucial precursory information, stand as a key technology in overcoming bottlenecks in intelligent driving. Among the feasible methods for maintaining highly updated HD maps, crowdsourced updating emerges as a viable solution. This paper primarily focuses on the information security issues associated with crowdsourced updating of HD maps. Starting with an exploration of the three critical technologies: data collection, information extraction, and change detection, related to crowdsourced updates, the paper summarizes and analyzes the current research status. It delves into the problems existing in crowdsourced updating technology from the perspective of geographical information security. Furthermore, it reviews the existing policies, regulations, and relevant standards related to geographical information security in the context of crowdsourced updating of HD maps. Finally, it analyzes and summarizes the new challenges posed by the development of crowdsourced updating for HD maps to existing policies, regulations, and standards from the perspective of geographical information security. It concludes with suggestions for further refinement and improvement of policies, regulations, and standards.

Research on the change of regional ecological sensitivity under the background of returning polder to lake: taking Xinghua city as an example

JIANG Zhihao, WANG Dongmei, WAN Jun, SHI Yifan, WU Yongfeng

2024, 0(8):  128-134.  doi:10.13474/j.cnki.11-2246.2024.0822

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Taking Xinghua city, Jiangsu province as an example, this study explores the impact of the reforestation project on regional ecological sensitivity. Based on the analytic hierarchy process (AHP), the weight of each evaluation factor is determined by Delphi method, and the ecological sensitivity of Xinghua city is analyzed by single factor and multiple factor superposition. The results showes as follows: ①The mean values of regional comprehensive ecological sensitivity are 2.37 and 2.43, and the standard deviations are 1.18 and 1.28, respectively. The ecological sensitivity is increased, and the spatial distribution of each sensitivity level is more discrete. ②The receding lake has a greater impact on the sensitivity of buffer area, land use and vegetation coverage, but a lesser impact on the sensitivity of elevation, slope and aspect of slope,which is related to the characteristics of the plain water network in the Lixia River area. ③The weights of influencing factors for the comprehensive ecological sensitivity of Xinghua city are as follows: The spatial distribution of land type, vegetation index, water area buffer, elevation, slope and slope direction were similar to the first three indexes, showing that northwest is higher than southeast and suburban was higher than urban. This study can provide scientific guidance for the planning and ecological evaluation of returning polder to lake.

Road marking extraction method from mobile LiDAR point clouds based on multi-loss fusion and shuffle attention

HE Yinxin, QI Hua, ZHU Yunquan, LU Zilai, PENG Shiyong, LIU Yang

2024, 0(8):  135-140.  doi:10.13474/j.cnki.11-2246.2024.0823

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The accurate extraction of road markings is of great significance in the development of advanced driving assistance system and high precision map. Since the point clouds have uneven distribution on reflection intensity and density or low contrast between road line and its surrounding road surface, the existing thresholding method is difficult to extract road line accurately,So this paper proposes the vehicle-mounted LiDAR point cloud road marking extraction method based on multi-loss fusion and mixing and shuffling attention, and selects a typical highway test sample area to conduct the road marking extraction test and compare and analyze the accuracy of the method with that of the conventional method. The accuracy comparison analysis is carried out with the conventional method. The test shows that the method in this paper is better than other methods in improving road marking extraction accuracy, which is expected to better serve the development and application of high-precision maps for autonomous driving.

The application of 3D realistic technology in digital metro station system construction

XU Shuying, HE Wei, TONG Tong

2024, 0(8):  141-144.  doi:10.13474/j.cnki.11-2246.2024.0824

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The important foundation of digital metro station constructiong is to build a visualized and measurable model. The traditional metro station measurement method has many disadvantages, such as heavy workload, low automation, low measurement efficiency, poor information acquisition and incomplete results. In this paper, 3D laser scanning technology is used to quickly obtain the spatial information of metro stations. This method has the advantages of non-contact measurement, high efficiency, high precision, high degree of automation, comprehensive and rich information, diverse forms of results, strong scalability, etc. It can provide authoritative, accurate and intuitive mapping data for daily operation and management of subway. At the same time, it can minimize the impact on the daily operation and management of the station,assisting in the digital transformation of Shanghai metro.

Modeling for InSAR orbit error based on time-varying polynomials to estimate DEM

PAN Ziyang, WAN Afang, WANG Feng, ZOU Mingpu, WEN Kangfeng, ZOU Meifang

2024, 0(8):  145-150,176.  doi:10.13474/j.cnki.11-2246.2024.0825

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The existing digital elevation model update production mode is difficult to meet the urgent needs of real-life 3D and smart city information construction. InSAR repeat-pass technology is expected to achieve fast, large-scale, and high-precision DEM estimation through short-cycle revisit. However, in the repeat-pass mode, the severe time-varying orbital error is difficult to meet the requirements of high-precision mapping because the orbital error of the two SAR images is difficult to be offset by the interferometric process. Therefore, one of the key points of using repeat-pass InSAR technology to estimate DEM is how to reduce the influence of orbital error. Based on this, this paper proposes a method for estimating orbital error based on a time-varying polynomial model. This model constructs the functional relationship between the time-frequency baseline and the orbital error by block construction, and uses weighted least squares to achieve robust estimation. In order to verify the effectiveness of the proposed method, the data of L-band InSAR data in Hunan province are used for the first time to conduct tests, and InSAR DEM is estimated. In the two test sites, the experimental results verified the effectiveness and robustness of the proposed method and estimated the orbital error well. In addition, the accuracy of the DEM estimated by the proposed method is verified using satellite ICESat-2 data. The RMSE of the two test areas are 4.58 and 6.44 m, respectively, which are 48.8% and 52.9% higher than the traditional satellite method of removing orbital error to estimate DEM (8.94 and 13.68 m).

Application of multi-source surveying and mapping technology in geological hazard investigation of slopes along the Yellow River highway

WANG Shuang, XU Jian, LIN Lu, YANG Xinfei, LIU Xiaonan, LIU Na

2024, 0(8):  151-154,176.  doi:10.13474/j.cnki.11-2246.2024.0826

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In response to the shortcomings of low efficiency, poor accuracy, and high risk in traditional highway slope geological hazard investigation, geological hazard investigation technology experiments are carried out along the Yellow River highway. By comprehensively utilizing various surveying and mapping techniques such as unmanned aerial vehicle tilt photography, vehicular mobile measurement, ground laser scanner, and close proximity photogrammetry, the production process, technical parameters, and key technical links are studied to form a solution that integrates space and land, improve the accuracy, efficiency, and safety of slope geological hazard investigation.

Angle elevation mapping method of airport pavement based on laser scanning

HAN Libin, HOU Zhiqun, LI Zhaoyong, WEI Baofeng, XIONG Jianhua

2024, 0(8):  155-159.  doi:10.13474/j.cnki.11-2246.2024.0827

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In the process of airport non-stop navigation construction, the angle elevation measurement of pavement surface has the characteristics of high precision, long working time and strict environmental management requirements. It can be carried out by optimizing the layout of elevation control network, 3D laser scanning point cloud registration processing based on the graph intensity factor of control network, and interpolating the angle elevation fitting method. It is proved by an airport that the method can overcome the above difficulties effectively, and the measurement results are reliable and the accuracy is stable, which provides an advanced and reliable surveying technology for the precision elevation measurement of the board angle in the airport or similar engineering projects.

Research on deformation characteristics of foundation pit excavation and its influence

CAO Liang, LI Yunhe, CHEN Shuai, LIU Shuntao, LIU Wei

2024, 0(8):  160-164.  doi:10.13474/j.cnki.11-2246.2024.0828

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In response to the problem of deformation of deep foundation pit excavation in soft soil areas, this paper takes a deep foundation pit located in Tianjin as the research object. Based on the ABAQUS finite element model, the horizontal and vertical deformation of the retaining wall during the excavation process, as well as the settlement deformation of the surrounding soil and adjacent buildings are analyzed. The finite element analysis results are compared to the field monitoring results, obtaining the deformation characteristics of the deep foundation pit excavation and its influences. Researches indicated that the maximum horizontal displacement of the retaining wall is mainly concentrated in the excavation surface, and the horizontal deformation of the positive angle is about twice that of the negative angle.The vertical deformation of the retaining wall has a significant spatial effect, which has good linear relationship with the length of the wall. The maximum rebound value occurs at the external corner of the foundation pit. As the excavation depth increases, the settlement mode of the soil around the foundation pit changes from a triangle to a groove shape, and the settlement value and range increase with the increase of the distance from the pit angle. In response to the problem of uneven settlement of adjacent buildings caused by excavation of foundation pits, grouting reinforcement measures could reduce settlement deformation by about 30%, confirming the effectiveness of grouting protection measures. This study can provide reference for similar engineering design and field construction.

Forecasting land use-population-economy synergy in non-central urban areas of Beijing

HUANG Yuchen, HU Jiayi, LENG Junjie, LIU Xinyao, ZHANG Ziyu, ZHANG Chunxiao

2024, 0(8):  165-171.  doi:10.13474/j.cnki.11-2246.2024.0829

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The policy of relieving non-capital functions involves many aspects such as urbanisation, economy and population, etc. Current forecasting studies are mostly aimed at simulating and forecasting a single spatial element, without paying attention to the intertwined effects of multiple spatial elements in urban development. In this paper, the non-central urban area of Beijing is taken as the study area, and based on multi-year land use data, GDP grid data and population grid data, the land use pattern in 2030 under the planning scenario and the GDP-population kilometre grid data in 2030 are simulated by the PLUS model and the SCS method in a multi-factor synergistic simulation, and the characteristics of spatial, temporal and aggregate changes are calculated. The results show that: ①Under the policy scenario of ecological priority, the forest and grassland and water bodies in the non-central urban areas of Beijing will expand but the overall expansion trend is not obvious.②Under the guidance of the policy, the population and GDP of the study area from 2020 to 2030 carry the spillover from the central area, and both the economy and population have achieved high-quality development. The research in this paper can provide scientific references for urbanisation, population monitoring and management, and optimal allocation of resources.

Exploration and practice of talent training for the the integration of bachelor's,master's and doctoral degrees in surveying and mapping engineering major under the background of smart surveying and mapping

WANG Shougang, ZHANG Ying, HAN Fushun, CHEN Guoliang

2024, 0(8):  172-176.  doi:10.13474/j.cnki.11-2246.2024.0830

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Under the background of smart surveying and mapping, the surveying and mapping engineering major undertakes an important task of aligning with national strategic needs, accelerating disciplinary transformation and upgrading, and cultivating top-notch innovative talents in the industry. In the face of the current problem of repetitive and lengthy talent training content, taking the the integration of bachelor's, master's and doctoral degrees training model of the surveying and mapping engineering major at China University of Mining and Technology as an example, this paper explores in depth how to cultivate top-notch innovative talents in the field of intelligent surveying and mapping with international perspective, public welfare spirit, innovative consciousness, and practical ability from four aspects: building an integrated curriculum system, an integrated training mechanism, an integrated practical innovation platform, and an integrated student support system.

Accuracy analysis of tightly combined inertial navigation system: taking the Applanix POS MV as an example

FENG Guozheng, YE Fei, SUN Zhenyong, FEI Xinlong, NIE Junwei

2024, 0(8):  177-181.  doi:10.13474/j.cnki.11-2246.2024.0831

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With the development of mobile measurement technology, the demand for high-precision, continuous, reliable, and stable navigation and positioning data is becoming increasingly strong. In response to the difficulty of single GNSS navigation and positioning in complex dynamic environments, Applanix POS MV is used to conduct precision experiments on tightly integrated inertial navigation systems. The research shows that: ①The precision of tightly integrated navigation and positioning is significantly better than that of single GNSS PPK positioning models, especially in areas with large angular rate and attitude changes, the advantage of combined navigation and positioning accuracy is more significant.②The tightly integrated navigation and positioning system has the highest accuracy in tracking all satellites, and the accuracy of shutting down the BeiBou satellite is equivalent to tracking only the GPS satellite. However, shutting down the GPS satellite results in significant loss of accuracy. The tight combination navigation positioning system has better PPK assisted positioning accuracy than RTX assisted positioning accuracy, but the RTX accuracy is better than 0.05 cm, which can meet the accuracy requirements of large-scale mapping. The results indicate that the use of PPK or RTX assisted tightly integrated inertial navigation systems can provide high-precision, continuous, reliable, and stable navigation and positioning data in complex situations.