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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 Bulletin of Surveying and Mapping    2024, 0 (8): 26-30.   DOI: 10.13474/j.cnki.11-2246.2024.0805 Abstract （480）      PDF（pc） （1871KB）（361）       Knowledge map    Save 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. Reference | Related Articles | Metrics

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Study on multisource data fusion methods and their application in comprehensive subsidence monitoring of mining area surface DU Yuzhu, LIANG Tao Bulletin of Surveying and Mapping    2024, 0 (11): 120-125.   DOI: 10.13474/j.cnki.11-2246.2024.1121 Abstract （321）      PDF（pc） （2039KB）（111）       Knowledge map    Save With the development of unmanned aerial vehicle (UAV), sensor, and data processing technologies, lightweight and low-cost UAVs can carry a variety of sensors to obtain diverse high-precision observation data. In response to the characteristics of mining-induced subsidence, this paper designs a lightweight and small-scale UAV mining area ground monitoring scheme that integrates aerial photography and LiDAR. It studies key technologies such as multi-period and multi-source data registration, selection of subsidence monitoring points, construction of surface rock movement observation lines, and proposes effective solutions. According to the research results, application tests have been carried out, and the results show that the lightweight UAV measurements using fused point clouds and imagery can obtain comprehensive mining area ground subsidence models with a precision better than 0.25 meters. Reference | Related Articles | Metrics

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Application of multi-source point cloud fusion technology in urban renewal data acquisition MU Cuiwei, WANG Zhaoze Bulletin of Surveying and Mapping    2024, 0 (11): 151-155.   DOI: 10.13474/j.cnki.11-2246.2024.1126 Abstract （292）      PDF（pc） （1978KB）（171）       Knowledge map    Save Multi-source point cloud fusion technology is playing an increasingly important role in urban renewal data collection. This technology can achieve comprehensive and accurate perception of urban features by integrating point cloud data obtained from different devices and sensors. In the process of urban renewal, the use of multi-source point cloud data fusion technology can quickly and efficiently obtain urban basic spatial data, providing rich spatial data information for urban renewal planning, design, and decision-making. Through the fusion processing and analysis of point cloud data obtained from data collection equipment such as vehicle-mounted mobile measurement systems, motorcycle-mounted mobile measurement systems, drone-mounted mobile measurement systems, and stationary 3D laser scanners, various basic spatial data required for urban renewal can be quickly obtained. Furthermore, an in-depth analysis and research on the accuracy of the data in plane coordinates is carried out, thereby promoting the popularization and application of this technology in urban renewal data collection, and providing beneficial assistance for accelerating the intelligent and fine development of urban renewal work. Reference | Related Articles | Metrics

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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 Bulletin of Surveying and Mapping    2024, 0 (8): 1-7.   DOI: 10.13474/j.cnki.11-2246.2024.0801 Abstract （288）      PDF（pc） （6633KB）（308）       Knowledge map    Save 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. Reference | Related Articles | Metrics

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Research and application of Lutan-1 SAR satellite in survey and monitoring of catastrophic geohazards YU Zhonghai, YAN Libo, LIU Qian, LU Guangbo, LIU Rui Bulletin of Surveying and Mapping    2024, 0 (11): 97-101,176.   DOI: 10.13474/j.cnki.11-2246.2024.1117 Abstract （278）      PDF（pc） （2931KB）（184）       Knowledge map    Save Lutan-1(LT-1) is the first L-band differential interferometric SAR satellite in China. Jinan has established a satellite-based monitoring network to deepen the construction of comprehensive monitoring and early warning system for urban safety risks since 2024. The SAR satellites are designed for monthly deformation monitoring of major infrastructures such as bridges, super high-rise buildings, mines, and geological hazards. This paper conducted a surface deformation study based on LT-1 with LandSAR software for a 2800 km 2 area in the southern region the LT-1 is effective for geological hazard deformation survey and monitoring. At the same time, of Jinan. Research results showed that the obvious subsidence in some mining areas is also detected, which could provide monitoring basis for the supervision of production safety in mining areas. Reference | Related Articles | Metrics

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Improved object detection algorithm HCAM-YOLO in traffic scenes based on YOLOv5 WANG Zhitao, ZHANG Ruiju, WANG Jian, ZHAO Jiaxing, LIU Yantao Bulletin of Surveying and Mapping    2024, 0 (11): 61-67.   DOI: 10.13474/j.cnki.11-2246.2024.1111 Abstract （264）      PDF（pc） （2352KB）（150）       Knowledge map    Save The rapid and precise detection of targets in traffic scenarios is crucial for intelligent traffic management and driving path decision-making. Traditional target detection models often grapple with issues such as inadequate detection accuracy, high rates of leak detection and false detection due to the complexity and variability of the traffic environment, and the diversity and sparsity of target features. To address these challenges, this paper introduces a YOLOv5 target detection model, HCAM-YOLO, which leverages the HcPAN feature fusion network. The crux of this approach lies in addressing the issue of local information being easily lost during the PAN network's feature fusion process. A hybrid convolutional attention mechanism(HCAM) is designed to enhance multi-scale information extraction in feature fusion networks. By integrating the HCAM module into the PAN's underlying structure, the sensitivity of key local features is enhanced, while the fusion effect of deep semantic information and shallow positional data is strengthened. This method's novelty lies in its use of an attention mechanism to optimize the feature fusion process, thereby improving the model's detection performance of pedestrians, motor vehicles, and other targets in complex traffic environments. The experimental dataset comprises the Rope 3D dataset, Road Veh dataset, and Road Ped dataset. The results demonstrate that the HCAM module is more suitable for integration into the underlying PAN network than other attention mechanisms. When compared to the basic YOLOv5 model, the precision and recall of the final HCAM-YOLO algorithm model increased by 3.4% and 3.2%,respectively, and mAP@0.5/% by 3.8%. The HCAM-YOLO algorithm model proposed in this paper exhibits strong adaptability to target detection tasks in traffic scenes with complex backgrounds. Reference | Related Articles | Metrics

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Farmland soil moisture monitoring based on UAV multispectral imagery ZHAO Guiping, XU Fajun Bulletin of Surveying and Mapping    2024, 0 (11): 177-182.   DOI: 10.13474/j.cnki.11-2246.2024.1131 Abstract （259）      PDF（pc） （1953KB）（172）       Knowledge map    Save Using drones to monitor soil moisture is low-cost, convenient, fast and accurate, and has important practical significance for intelligent management of farmland areas. This study selected Liangfeng Farm as the research area, where a drone equipped with a multispectral camera is used to monitor soil moisture. Through gray correlation screening, soil moisture sensitive spectral data are selected, and regression analysis was performed with the measured soil moisture data to construct a soil moisture inversion model based on UAV multispectral remote sensing. Through comparative analysis of the results of the NIR-RE-G model and the B-R-G-RE-NIR model, it is found that the determination coefficient R 2 is both greater than 0.77. The B-R-G-RE-NIR model is better than the NIR-RE-G model in terms of accuracy evaluation results of R 2 and RMSE, so the overall inversion results of both models have higher accuracy. Therefore, this study verified the effectiveness and feasibility of the NIR-RE-G model and the B-R-G-RE-NIR model in soil moisture monitoring in this region, which provides an effective method and reliable reference for rapid monitoring of soil moisture in large-scale farmland. Reference | Related Articles | Metrics

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Key technologies and applications of new energy site selection based on GIS+BIM WANG Lei, MIAO Chengguang, HAN Xiaoliang, CHEN Jiajing, HOU Peng, ZHAO Haifeng Bulletin of Surveying and Mapping    2024, 0 (10): 168-173.   DOI: 10.13474/j.cnki.11-2246.2024.1028. Abstract （255）      PDF（pc） （4123KB）（148）       Knowledge map    Save With the proposal of carbon neutrality goals, the development and utilization of new energy are growing. Due to the numerous factors involved in project design, it is difficult to select the location of new energy sources under the constraints of land and geography. Facing the explosive growth of new energy projects, traditional site selection design methods urgently need to improve efficiency to ensure the scientific and rational nature of new energy projects. Based on the analysis of the principles and application status of GIS+BIM technology, this article proposes a new solution for intelligent site selection of new energy sources. A 3D digital platform is constructed based on the integration of GIS+BIM technology. Through the platform, various resource conditions such as geographical location and environmental factors are fully analyzed, and comprehensive evaluation and optimization of new energy project locations are achieved. Through application analysis, the research results of the article can ensure the scientific and rationality of site selection, improve design efficiency by 15%, improve site selection accuracy, reduce costs, and promote the sustainable development of new energy projects. Reference | Related Articles | Metrics

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Application of landslide geohazard investigation based on realistic 3D and GIM technology MA Jianxiong, MING Jing, ZHOU Chengtao, GAN Ze Bulletin of Surveying and Mapping    2024, 0 (11): 74-77,161.   DOI: 10.13474/j.cnki.11-2246.2024.1113 Abstract （252）      PDF（pc） （3612KB）（110）       Knowledge map    Save Accompanied by the accelerating pace of infrastructure construction and the continuous improvement of the quality requirements of engineering construction, the accuracy of landslide engineering survey also puts forward higher requirements, this paper takes a project on the right bank of the Yangtze River in Liangjiang New Area of Chongqing Municipality as an example, and adopts the geological fine investigation method based on the realistic 3D and GIM technology, realizing a breakthrough of the application technology of three-dimensional fine survey of landslide engineering in the mountainous city, which provides support for the emergency disposal of landslide and the implementation of engineering. The results show that:① Based on the real-life 3D model realized the interpretation of the key elements of landslide geohazards, which provides a powerful auxiliary decision-making and basis for geohazard investigation; ② Through the 3D geologic modeling method with GIM technology as the core, the construction of a refined 3D geologic information model of the study area is realized, which has the characteristics of rapidity, accuracy, and reliability; ③ Through the integrated integration and fusion application of the real-life 3D model and GIM technology, the survey results are more refined, providing effective data support and scientific decision-making for the emergency disposal of landslides and the implementation of he project. Reference | Related Articles | Metrics

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Identification characteristics and potential analysis of geological hazards in realistic 3D scenes WANG Defu, LIU Li, LI Yongxin, ZHANG Zhiqiang, LUO Chao, LIAO Yangyang Bulletin of Surveying and Mapping    2024, 0 (8): 20-25.   DOI: 10.13474/j.cnki.11-2246.2024.0804 Abstract （251）      PDF（pc） （9379KB）（228）       Knowledge map    Save 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. Reference | Related Articles | Metrics

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Optimization and application of deep learning model-based subway tunnel defect detection YOU Xiangjun, ZHAO Xia, LONG Sichun, WANG Jiawei, ZHENG Ying, KUANG Lijun Bulletin of Surveying and Mapping    2024, 0 (8): 96-101.   DOI: 10.13474/j.cnki.11-2246.2024.0817 Abstract （250）      PDF（pc） （3414KB）（154）       Knowledge map    Save 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. Reference | Related Articles | Metrics

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Application of time series InSAR technology in surface subsidence monitoring and spatio-temporal evolution analysis in mining area ZHANG Yuxin, YUAN Xiping, GAN Shu, PENG Xiang, WANG Song Bulletin of Surveying and Mapping    2025, 0 (3): 15-20.   DOI: 10.13474/j.cnki.11-2246.2025.0303 Abstract （249）      PDF（pc） （3246KB）（201）       Knowledge map    Save In view of the hazards caused by surface subsidence to the safety, environment, socio-economic development and sustainability of resource utilization in the mining area, 63 Sentinel-1A data from December 31, 2021 to March 2, 2024 were first obtained, and SBAS-InSAR (time series interferometry) technology was adopted to monitor the surface deformation of Baicao mining area. The results of surface settlement rate and cumulative settlement in the mine area are obtained, and then the reliability analysis of the monitoring results is carried out by using the measured data. Finally, the settlement of the mine area is predicted based on the LSTM model, and the spatiotemporal variation characteristics and evolution rules of the settlement of the mine area are analyzed in detail.The final conclusions are as follows: ① Spatially, the surface subsidence of Baichuang Mining area is mainly concentrated in the west of the mining area, with the maximum subsidence of -316.86mm and the maximum annual average subsidence rate of -148.4mm/a, and the total subsidence area of 0.6236km 2, of which the heavy and extremely heavy subsidence area of 0.2804km 2 needs to be monitored.②In time series, the area with severe subsidence starts to settle from the monitoring starting point, and the subsidence rate tends to be uniform. If no protection is taken, the area will continue to settle in the future, and the settlement may be intensified.③The fitting degree of measured data and monitoring data is high, and the coefficient of determination R 2 is up to 0.994. The prediction effect of LSTM prediction model on monitoring data is good, and the linear fitting coefficient of determination R 2 of predicted value and monitoring value can reach more than 0.946, indicating that the prediction of surface settlement by LSTM model can meet the requirement of accuracy. The experimental results can provide technical support for disaster prevention and control in mining areas, and provide strong support for more accurate surface deformation evaluation in mining areas. Reference | Related Articles | Metrics

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Inversion of soil moisture in the Yuanmou hot-dry river valley area based on the PSO_GA-RBF neural network model DU Jinming, LUO Mingliang, BAI Leichao, WU Qiusheng Bulletin of Surveying and Mapping    2024, 0 (11): 1-6.   DOI: 10.13474/j.cnki.11-2246.2024.1101 Abstract （236）      PDF（pc） （2083KB）（170）       Knowledge map    Save Soil moisture has a significant impact on hydrological and climatic processes. A comprehensive and accurate understanding of soil moisture status is of great research value for hydrological simulation, ecological governance, and other related fields. In response to the soil moisture inversion issue in the Yuanmou hot-dry river valley area, a new soil moisture inversion model is constructed using the PSO_GA-combined optimized RBF neural network. The experiment utilizes Sentinel-1 radar data and Sentinel-2 optical data, and employs the water-cloud model suitable for low vegetation cover types in the study area to correct the vegetation scattering effects. The obtained VV and VH polarized soil backscattering coefficients and cross-polarization differences are incorporated into the constructed model, enabling the remote sensing inversion of soil volumetric water content in the hot-dry river valley area of Yuanmou county, Yunnan province. Comparisons and validation against measured soil volumetric water content data show a root mean square error of 0.55% m 3/m 3 and a coefficient of determination ( R 2) of 0.855, demonstrating a significant improvement in accuracy compared to traditional RBF neural network models.Correlational analysis is conducted between the inversion results and NDVI values, revealing a coefficient of determination ( R 2) of 0.512 7 between the two. This verifies the high precision of soil volumetric water content inversion based on Sentinel-1 radar image data, utilizing the water-cloud model and PSO_GA-combined optimized RBF neural network, validating the feasibility of large-scale soil moisture monitoring in hot-dry river valley areas. Reference | Related Articles | Metrics

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Application of topographic survey of island (reef) based on multi-beam sounding system SUN Dong, DING Shijun, LI Xiaohong, LIU Yuan, LIU Haibin Bulletin of Surveying and Mapping    2024, 0 (11): 90-96.   DOI: 10.13474/j.cnki.11-2246.2024.1116 Abstract （233）      PDF（pc） （2142KB）（93）       Knowledge map    Save With the deepening of marine comprehensive survey, islands (reefs) as an important element of the ocean, its complete surface and underwater terrain data is the basis of understanding and planning islands and reefs. The multi-beam sounding system combined with 3D laser is used to obtain the integrated topographic data of islands and reefs above and below water. The experiment is carried out in combination with a typical island in eastern Shandong province, focusing on the application of the multi-beam sounding system in the topographic data acquisition of islands and reefs, the accuracy assessment is carried out, and the data results are displayed. The results of the study area show that the ship-borne measurement system combined with the unmanned ship measurement system can obtain the land and water interface area of the reef completely at one time by using the high-low tidal range, and the data is complete and high precision. The full coverage data results can truly reflect the topography of islands and reefs and their surrounding areas, which provide the data support for planning and construction and ecological monitoring. Reference | Related Articles | Metrics

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Color restoration of underwater images using color compensation and convolutional neural network based defogging model MA Zhenling, CHEN Yuan, FAN Chengcheng, PAN Yan Bulletin of Surveying and Mapping    2024, 0 (11): 68-73.   DOI: 10.13474/j.cnki.11-2246.2024.1112 Abstract （232）      PDF（pc） （1834KB）（100）       Knowledge map    Save Underwater vision measurement has important applications in marine surveying, underwater engineering surveying, underwater archaeology and underwater environmental monitoring. However, underwater images suffer from color distortion, image blurring and low contrast, which limits the application of underwater visual measurement technology in practical environments. A color restoration method for underwater images based on color compensation and convolutional neural network (CNN) defogging model is proposed in this paper, in which the image enhancement is carried out step-by-step.Firstly,the color deviation of underwater images is analyzed, and then an adaptive color compensation strategy combined with the grayscale world white balance algorithm is used to correct underwater image color. Secondly, a CNN based dehazing model was designed to achieve dehazing processing of underwater images. Finally, the adaptive histogram equalization CLAHE method is used to enhance the contrast of underwater images. In order to prove the applicability and superiority of the proposed method, two image datasets are combined to study, and several known underwater image enhancement and restoration methods are compared. The proposed method and several compared methods are evaluated in two aspects of subjective visual effect and quantitative evaluation index. The comparison results show that compared with other enhancement algorithms, the proposed method successfully improves the clarity of the image and reduces the color deviation of the damaged underwater image when processing underwater images in various environments and has superior image color recovery compared with existing enhancement methods. Reference | Related Articles | Metrics

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Monitoring of deformation in mining buildings based on GNSS and InSAR technologies ZHAO Qi Bulletin of Surveying and Mapping    2024, 0 (11): 126-132,166.   DOI: 10.13474/j.cnki.11-2246.2024.1122 Abstract （231）      PDF（pc） （2387KB）（125）       Knowledge map    Save Mining area buildings are critical components of coal mining production. Dangerous deformations can severely threaten normal production and may even lead to safety incidents. In this study, the GNSS-InSAR fusion method is proposed to achieve high-precision deformation monitoring of mining area buildings. Taking the northern suburbs mining area of Xilinhot city,Inner Mongolia, as the study area, the dynamic deformation of mining buildings is obtained based on the GNSS,SBAS-InSAR, and GNSS-InSAR fusion methods with 30 scenes of Sentinel-1A image data and 35 sites GNSS data. The results show that the GNSS-InSAR fusion method is 43.9% more accurate than the SBAS-InSAR,which indicates that the proposed method provides better support for the deformation monitoring and safety assessment of mining area buildings. The combination of rainfall,temperature,and time-series deformation results inferred that temperature is the primary cause of building deformation,and the impact of surrounding mining activities on the buildings is negligible. Moreover,during the monitoring period, all deformation values of the mining area buildings are below the permissible deformation thresholds.The results indicate no dangerous deformations and confirm that the buildings can continue safe operations. Reference | Related Articles | Metrics

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Application of drone tilt photography technology in identification and stability evaluation of high and steep slope dangerous rock bodies CHEN Fuqiang Bulletin of Surveying and Mapping    2024, 0 (10): 132-137.   DOI: 10.13474/j.cnki.11-2246.2024.1022. Abstract （228）      PDF（pc） （11904KB）（206）       Knowledge map    Save Drone tilt photography technology, with its unique advantages of high precision and multi perspective restoration of real landforms, it has been widely applied in fields such as terrain and geomorphology surveying, urban 3D modeling, engineering survey and construction, and land use planning.This study adopts a comprehensive research evaluation method of “unmanned aerial vehicle oblique photography+remote sensing comprehensive interpretation+rockfall trajectory simulation”,based on the 3D slope model of high and steep slopes on both sides of a new highway in Xizang, a detailed interpretation analysis is conducted on the dangerous rock bodies developed in the region, identifying a total of 67 dangerous rock bodies. Through stability analysis and its threat to the road below, it indicates that the dangerous rock mass poses a significant threat to the western central part of the area, the eastern part of the northern slope, and some areas at the foot of the southern slope, which can easily pose a threat to pedestrians and vehicles traveling on the highway.The research results provide important technical basis for the cleaning and protection of hazardous rock masses on site, effectively compensating for the shortcomings of on-site personnels inability to reach and difficult survey operations, and have important theoretical and practical significance. Reference | Related Articles | Metrics

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Research on BDS-3 B1C/B2a long baseline relative positioning FANG Zhuo, WANG Lishiyun, MICHAEL Floyd Bulletin of Surveying and Mapping    2024, 0 (8): 31-36,53.   DOI: 10.13474/j.cnki.11-2246.2024.0806 Abstract （223）      PDF（pc） （2244KB）（164）       Knowledge map    Save 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. Reference | Related Articles | Metrics

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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 Bulletin of Surveying and Mapping    2024, 0 (8): 84-89.   DOI: 10.13474/j.cnki.11-2246.2024.0815 Abstract （220）      PDF（pc） （5569KB）（129）       Knowledge map    Save 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. Reference | Related Articles | Metrics

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Research on digital review of high-precision maps for intelligent driving WU Jiatong, DI Lin, HUANG Long Bulletin of Surveying and Mapping    2024, 0 (10): 174-178.   DOI: 10.13474/j.cnki.11-2246.2024.1029. Abstract （218）      PDF（pc） （1335KB）（167）       Knowledge map    Save As a new form of map industry, smart driving high-precision maps contain high-rich, high-precision and high-freshness geographical information data, which are related to national sovereignty, security and interests. The current high-precision map review work lacks effective automated review technology, standard databases and institutional guarantees, making it difficult to meet the demand for map freshness by smart driving, which will have a certain impact on smart driving. This article summarizes the development status of digital review of high-precision maps for smart driving, sorts out relevant policy trends, systematically analyzes the key technologies of digital map review and the difficulties in large-scale application, and combines the map review business model to propose solutions for the digital review technology route for high-precision maps for smart driving aims to provide a reference for efficient and reliable high-precision map review. Reference | Related Articles | Metrics