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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 （688）      PDF（pc） （2931KB）（231）       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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Dynamic monitoring for open-pit mine reclamation based on UAV oblique photogrammetry ZHONG Weihua, LIU Jingkuang Bulletin of Surveying and Mapping    2025, 0 (3): 21-26.   DOI: 10.13474/j.cnki.11-2246.2025.0304 Abstract （583）      PDF（pc） （8280KB）（218）       Knowledge map    Save Traditional open-pit mine reclamation monitoring relies on satellite remote sensing and on-site investigation, but suffers from low precision and efficiency. To enhance dynamic monitoring and improve oversight, this paper explores UAV oblique photogrammetry. Using an open-pit mine in Guangzhou as a test case, the technology generated a real-life 3D Mesh model across four phases, synchronously outputting DEM and producing DLG through stereo acquisition. Monitoring indices such as earth backfill elevation, volume, and building demolition were analyzed using DEM and DLG data. Results indicate that UAV data collection over 1.56km 2 took about one hour, three minutes, and fifty-five seconds, with a GSD of 2.51cm/pixels, marking an improvement in efficiency and accuracy over traditional methods. Additionally, the DLG data helped count a demolition area of 16442.36m 2, and DEM data allowed for the calculation of a total earth backfill volume of 0.017km 3, demonstrating the technology's digital and visual analysis capabilities. This study offers technical support for regional authorities in dynamically monitoring open-pit mine reclamation and provides references for monitoring other mines. 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 （567）      PDF（pc） （1978KB）（213）       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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Monitoring and prediction of ground subsidence in mining areas using DS-InSAR and LSTM WANG Benhao, WANG Yanxia, XIANG Xueyong, HU Hong Bulletin of Surveying and Mapping    2024, 0 (11): 44-48.   DOI: 10.13474/j.cnki.11-2246.2024.1108 Abstract （401）      PDF（pc） （3558KB）（167）       Knowledge map    Save In response to the problem of low point density and uneven distribution in subsidence monitoring of mining areas using conventional InSAR technology, this paper uses 36 Sentinel-1A image data from August 2020 to August 2023 to obtain surface deformation information of Langyashan mining area in Chuzhou city, Anhui province using DS-InSAR technology. And the LSTM neural network model is used to predict the future settlement trend of the area with severe ground subsidence in the mining area, in order to understand the future development trend of ground subsidence in the mining area. The research results indicate that:①Compared with traditional PS-InSAR technology, DS-InSAR technology can significantly increase the number of monitoring points in mining areas and more comprehensively reflect surface subsidence information in mining areas. ②During the monitoring period, there are three deformation zones in the mining area, with a maximum settlement of 32.4 mm and a maximum settlement rate of 10.8 mm/a. ③By comparing with the GM (1,1) model and using the selected 6 settlement feature points, it is found that the LSTM neural network model exhibited higher prediction accuracy. ④For the area with the highest cumulative settlement, we use the LSTM model to predict the cumulative settlement of the 6 feature points in the area for the next 12 months. The prediction results show that the future settlement in the area fluctuates within a certain range, and no obvious settlement trend has been observed yet. 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 （360）      PDF（pc） （2039KB）（122）       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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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 （352）      PDF（pc） （1953KB）（202）       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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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 （334）      PDF（pc） （3246KB）（247）       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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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 （313）      PDF（pc） （2352KB）（156）       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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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 （300）      PDF（pc） （3612KB）（119）       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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Target detection method of railway catenary components in UAV images based on improved YOLOv7 SONG Zongying, WANG Xingzhong, ZENG Shan, ZHANG Zhengjun, YIN Taijun, LIU Hongli Bulletin of Surveying and Mapping    2024, 0 (11): 108-114.   DOI: 10.13474/j.cnki.11-2246.2024.1119 Abstract （299）      PDF（pc） （2863KB）（83）       Knowledge map    Save The catenary system, as an essential component of electrified railways, provides energy to trains and ensures their normal operation. Damage to catenary system components poses a threat to train safety, making it crucial to regularly inspect the condition of these components. In recent years, UAVs have been widely used in monitoring the condition of critical catenary components. However, due to the complex and variable backgrounds, significant scale changes, and the presence of many small targets in the catenary images captured by UAVs, existing detection algorithms frequently suffer from false detections and missed detections of catenary components. To address this issue, this paper proposes a catenary component target detection method based on an improved YOLOv7 algorithm. By introducing an enhanced receptive field module, the network's feature extraction capability is strengthened, leading to more discriminative target feature representations. Additionally, an improved coordinate attention mechanism is incorporated during the fusion of adjacent scale feature maps to highlight the target features of catenary components and suppress redundant background information. The bounding box loss function is optimized using the Wasserstein distance, effectively improving detection accuracy. Experiments on the catenary component dataset show that the improved YOLOv7 algorithm can accurately detect various catenary components in drone-captured images, achieving a mean average precision of 97.27%, which is 3.83% higher than before the improvement. The proposed algorithm enhances the high-precision and rapid detection capabilities of drones for critical catenary components, providing technical support for achieving more intelligent drone inspections. 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 （288）      PDF（pc） （2142KB）（104）       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 （282）      PDF（pc） （1834KB）（107）       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 （272）      PDF（pc） （2387KB）（137）       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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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 （264）      PDF（pc） （2083KB）（189）       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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Automatic extraction of water body using multi-feature index ZHANG Baowen, ZHAO Zhan Bulletin of Surveying and Mapping    2024, 0 (11): 21-26.   DOI: 10.13474/j.cnki.11-2246.2024.1104 Abstract （252）      PDF（pc） （3268KB）（138）       Knowledge map    Save At present, water extraction is still a semi-automatic method, which has low efficiency and is prone to omissions and extraction errors. In this paper, an automatic water extraction method using multi-feature index is proposed. Vegetation index, water index, improved water index and building land index are used to automatically extract water and non-water samples, and the classifier is trained to realize high-precision and automatic urban water extraction. Four typical experimental areas with different geographical conditions at home and abroad are selected for comparison and analysis with the traditional extraction method. Under this method, high extraction accuracy is achieved in all the four experimental areas, and Kappa coefficient reached above 0.94. Compared with single-band threshold method, Kappa coefficient increases by 7.2% on average. Compared with the water index method, Kappa coefficient increased by 3.8% on average. Reference | Related Articles | Metrics

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Fusion technology and application of GIS based marine digital system: taking Zhejiang Province's Intelligent Ocean Control as an example ZHU Junxia, MAO Keqin, ZHANG Can Bulletin of Surveying and Mapping    2024, 0 (11): 162-166.   DOI: 10.13474/j.cnki.11-2246.2024.1128 Abstract （249）      PDF（pc） （1371KB）（98）       Knowledge map    Save Application scenarios are the main carrier of digital reform, serving as the backbone and link for achieving digital empowerment and institutional reshaping. Based on the pilot work of application scenarios in various counties (cities, districts), the use of fusion technology can avoid duplicate financial investment at all levels, reduce the burden of grassroots operation and maintenance, and further enhance the effectiveness of digital reform. This article focuses on four existing marine related business systems and application scenarios:marine spatial resource supervision, marine ecological warning and evaluation, marine disaster perception and prevention, and marine economic monitoring and evaluation. From the dimensions of business, technology, and interaction, GIS based digital fusion technology for marine related business is studied, achieving data fusion, technology fusion, and interaction fusion between business systems and application scenarios. Based on the fusion architecture, multiple applications of intelligent control of the ocean in Zhejiang province are integrated and tested. Reference | Related Articles | Metrics

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Automatic extraction of transmission line flat sections and vegetation envelopes based on airborne laser point clouds ZU Weiguo, TAN Jinshi Bulletin of Surveying and Mapping    2024, 0 (11): 27-32.   DOI: 10.13474/j.cnki.11-2246.2024.1105 Abstract （239）      PDF（pc） （2077KB）（98）       Knowledge map    Save The flat cross-section map of transmission lines is a crucial basis for optimizing transmission line routes, reducing environmental impact, and lowering costs. Traditional methods for mapping flat cross-sections involve considerable labor and high risk, with low accuracy in estimating vegetation height and rare direct vegetation envelope lines to affect line design. To address this, a method for automatically extracting and drawing flat cross-sections and vegetation envelope lines of transmission lines based on airborne laser point clouds is proposed. The overall technical approach are described firstly. Then, key technologies are investigated in depth, including laser point cloud calculation and DSM construction, filtering and DEM construction, automatic extraction of flat cross-sections with adaptive intervals, and vegetation envelope line simplification algorithms. Finally, through practical cases, the DSM and DEM construction from laser point clouds, extraction of flat cross-sections and vegetation envelope lines, and their accuracy and efficiency are analyzed. Results show that laser point clouds provide high measurement accuracy, enabling fast, accurate, and automated extraction of flat cross-sections and automatic drawing of vegetation envelope lines, So offer significant technical support for optimizing transmission line routes and being worthy of widespread promotion and application. Reference | Related Articles | Metrics

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Analysis of spatiotemporal evolution characteristics of typical landslides in the Jinsha River Basin based on SBAS-InSAR technology YANG Fang, DING Renjun, LI Yongfa Bulletin of Surveying and Mapping    2024, 0 (11): 102-107.   DOI: 10.13474/j.cnki.11-2246.2024.1118 Abstract （238）      PDF（pc） （2131KB）（170）       Knowledge map    Save The Jinsha River Basin belongs to the high mountain canyon area, with complex geological and geomorphological features.The numerous canyons, steep terrain, and a large amount of rainfall have led to frequent landslide disasters,which have caused serious impacts on human safety,production,and the environment.However,conventional measurement methods have drawbacks such as high cost,long cycle,and insufficient spatial resolution,making it difficult to fully reflect the evolution characteristics of landslides.Therefore,this article uses SBAS-InSAR technology combined with Sentinel-1A data from the lifting track to obtain surface deformation information of the Ahai Reservoir area in the Jinsha River Basin from January 2019 to December 2020.Three typical landslides,namely Ligu,Baiya,and Luoziru,are selected for spatiotemporal evolution characteristics analysis.The research results indicate that SBAS-InSAR technology can effectively identify typical landslides in high mountain canyon areas.During the monitoring period,the maximum deformation rate of the Li Gu landslide is -68 mm/a,and the cumulative deformation variable is -148 mm.The overall spread from the deformation center to the west towards the Jinsha River is in a strip shape.The maximum deformation rate of Baiya landslide is -40 mm/a,and the cumulative deformation variable is -77 mm.The maximum deformation rate of Luoziru landslide is -90 mm/a,and the cumulative deformation reaches -260 mm. Reference | Related Articles | Metrics

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Classification of tunnel point clouds based on improved cascaded BP neural network DING Penghui, LI Zhiyuan, LIU Yi, WANG Zhenghui Bulletin of Surveying and Mapping    2024, 0 (11): 172-176.   DOI: 10.13474/j.cnki.11-2246.2024.1130 Abstract （235）      PDF（pc） （1795KB）（78）       Knowledge map    Save Efficient classification of tunnel point cloud data is crucial for safety monitoring and 3D reconstruction in underground transportation and mining operations, as it facilitates the comprehensive exploration and utilization of point cloud data. This study addresses issues in existing tunnel point cloud classification methods, such as noise sensitivity, low processing efficiency, and susceptibility to overfitting, by proposing a cascaded backpropagation (CBP) neural network classification method optimized with an early stopping mechanism and adaptive parameter tuning. Firstly, the Trimble RealWorks software is used to separate tunnel and ground point clouds. Then, local geometric features are extracted using spherical neighborhood space and covariance matrix eigenvalues to construct feature vectors. Finally, an improved CBP network is employed to hierarchically classify internal tunnel lighting equipment, signage, and various pipelines, thereby enhancing classification efficiency and accuracy. Experimental results demonstrate that the improved CBP neural network achieves high accuracy and reliability in tunnel point cloud classification, significantly improving data processing efficiency and providing data support for tunnel maintenance, renovation, and safety management. Reference | Related Articles | Metrics

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Flood disaster risk and urban resilience assessment in Guangzhou based on open-source information and SDGSAT-1 nighttime light data LIU Jincang, DONG Jing, WANG Huanhuan, Lü Mingyang, DING Yixing Bulletin of Surveying and Mapping    2024, 0 (11): 156-161.   DOI: 10.13474/j.cnki.11-2246.2024.1127 Abstract （209）      PDF（pc） （1520KB）（128）       Knowledge map    Save Guangzhou is one of the 31 key flood control cities in China, which has the characteristics of heavy rain flood, transit flood and heavy typhoon impact. Researches on flood disaster risk and urban resilience assessment can provide scientific references for building a resilient city, improving modern urban governance, and achieving sustainable development in Guangzhou. This paper uses open-source geo-information and statistical data, combined with the nighttime light data of SDGSAT-1, to construct 18 flood evaluation indicators. Combined with the analytic hierarchy process, a model for flood disaster risk assessment in Guangzhou is formed. In addition, this paper refines 18 indicators to evaluate the urban resilience of Guangzhou from 2013 to 2022. The comprehensive analysis results indicate that the northern mountainous area of Conghua, the vicinity of Liuxi River in Conghua-Huadu-Baiyun, the Dongjiang-Zengjiang area of Zengcheng, and the main urban area with concentrated population are relatively high-risk areas. It also indicates that the urban resilience has significantly improved. However, there is still room for improvement in terms of adjusting population structure and increasing public safety expenditures. Reference | Related Articles | Metrics