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LiDAR point cloud registration with improved ICP algorithm XU Zhe, DONG Linxiao, WU Jiayue Bulletin of Surveying and Mapping    2024, 0 (4): 1-5.   DOI: 10.13474/j.cnki.11-2246.2024.0401 Abstract （199）      PDF（pc） （3266KB）（192）       Knowledge map    Save The traditional ICP algorithm has long matching time and is affected by initial values in LiDAR target point cloud matching, which leads to low positioning accuracy and poor robustness when applied to unmanned vehicle SLAM technology. Proposes an NDT-ICP algorithm that combines the KD-tree algorithm. Firstly, voxel grid filtering is used to preprocess the point cloud data obtained from LiDAR, and the method of plane fitting parameters is used to remove point cloud of ground. Secondly, use NDT algorithm for point cloud coarse matching to shorten the distance between the target point cloud and the point cloud to be matched. Finally, the KD-tree proximity search method is used to improve the speed of corresponding point search, and the precise matching of the ICP algorithm is completed by optimizing the convergence threshold. Through experiments, it has been shown that the improved algorithm proposed in this article has significantly improved speed and accuracy in point cloud matching compared to NDT and ICP algorithms, and has better accuracy and robustness in map construction. Reference | Related Articles | Metrics

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Spatial and temporal dynamic change and influencing factors of ecological environment quality in Chaohu Lake basin based on GEE WANG Ying, LI Daiwei, ZHANG Fan, ZHU Huizi, LI Longwei, LI Nan Bulletin of Surveying and Mapping    2023, 0 (7): 7-13.   DOI: 10.13474/j.cnki.11-2246.2023.0193 Abstract （258）   HTML （19）    PDF（pc） （6645KB）（174）       Knowledge map    Save Taking Chaohu Lake basin as the research area, remote sensing ecological index (RSEI) is constructed through Google Earth Engine cloud computing platform, and large-scale and long-time dynamic monitoring analysis and evaluation of ecological environment quality in Chaohu Lake basin are carried out by means of spatial autocorrelation and geographic detectors based on Landsat TM/OLI series remote sensing data from 2000 to 2020. The results show that:①The average value of RSEI increased from 0.70 in 2000 to 0.74 in 2020, showing an overall improvement trend, and the ecological environment level is mainly excellent and good. ②The global Moran's I index of the study area is all greater than 0, and the ecological environment quality in Chaohu Lake basin presented a clustering trend on the global autocorrelation, with a significant spatial positive correlation. In the past 20 years, the low-low aggregation area had a trend of increasing firstly and then decreasing. ③The ecological environment is affected by many factors, among which human factors had a great impact on the ecological environment of Chaohu Lake basin in 2010, which leaded to the decline of ecological environment quality. Reference | Related Articles | Metrics

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A method for constructing true 3D models of complex scenes based on multi-source spatial data ZHOU Baoxing, WANG Bing, ZHANG Hangfan, MA Dengyue, LIU Xizhu Bulletin of Surveying and Mapping    2024, 0 (4): 13-17.   DOI: 10.13474/j.cnki.11-2246.2024.0403 Abstract （99）      PDF（pc） （11003KB）（156）       Knowledge map    Save The 3D models of cities have been widely applied in various fields such as urban construction and social services. In order to rapidly and accurately construct 3D city models to meet the needs of urban detailed planning and management,this article focuses on the main theme of fast,reasonable,and precise construction of true 3D models,with city terrain and urban features as the research objects. It proposes a fast construction solution for city 3D models,starting from terrain to features,and from rough to precise. It realizes the rapid modeling of urban basic terrain,buildings,and other 3D scenes. The proposed modeling approach is specifically implemented using the Skyline platform,forming a complete operating process. Reference | Related Articles | Metrics

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Analysis of the effects of UAV-borne LiDAR point cloud density on DEM accuracy XIAO Jie Bulletin of Surveying and Mapping    2024, 0 (4): 35-40.   DOI: 10.13474/j.cnki.11-2246.2024.0407 Abstract （100）      PDF（pc） （5613KB）（146）       Knowledge map    Save UAV-borne LiDAR point cloud data is an important data source for producing DEM. In order to further improve DEM production efficiency,selecting flat terrain and mountainous terrain as test areas,the ground point cloud,which is processed by filtering method,is thinned and simplified according to the a lgorithm based on TIN with seven different the ground point cloud retention rate of 80%,60%,40%,and so on,and the corresponding DEM is generated and its accuracy is evaluated by mean error (ME),standard deviation (SD),and root mean square error (RMSE). The results show that: ①The accuracy of the produced 0.5 m grid-spacing DEM could exceed 0.05 m when the ground point cloud density reached 2 points/m 2 for flat terrain and 9 points/m 2 for mountainous terrain. ②As the density of ground point cloud increases,the DEM accuracy level gradually stabilizes,and the DEM accuracy would decrease rapidly when the ground point cloud density is thinned to 1 point/m 2. For the DEM production tasks in large regions using UAV-borne LiDAR point cloud data,the conclusions of this research have a certain guiding and reference significance in reducing data acquisition costs and improving DEM production efficiency. Reference | Related Articles | Metrics

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Shallow sea water depth inversion from WorldView-3 multispectral images based on seabed sediment classification YAO Chunjing, YU Zheng, WANG Jie, QIAN Chen, XU Junhao Bulletin of Surveying and Mapping    2023, 0 (7): 25-31.   DOI: 10.13474/j.cnki.11-2246.2023.0196 Abstract （140）   HTML （12）    PDF（pc） （1559KB）（143）       Knowledge map    Save In recent decades, sea water bathymetry inversion method based on remote sensing image has been a hot research topic. This paper uses WorldView-3 high-resolution satellite imagery, combined with satellite altimetry data, to focus on Wuzhizhou island which is near Hainan Island, China, and its adjacent waters as the main study area. After data preprocessing and substrate classification, multiple linear regression model, Stumpf logarithmic ratio model and BP neural network model are used to invert and analyze the water depth around the island. Results show that: for the three model, after the bottom sediment classification accuracy will be improved significantly. Among them, BP neural network model has the highest accuracy (root mean square error range of 0.2～0.7 m), followed by multiple linear regression model (root mean square error range of 0.3～0.8 m), and log ratio model has the lowest accuracy (root mean square error range of 0.6～1.1 m). Reference | Related Articles | Metrics

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A slope anomaly monitoring technology based on deep learning and image local feature extraction LIN Bokun, DING Yong, LI Denghua Bulletin of Surveying and Mapping    2024, 0 (4): 23-28.   DOI: 10.13474/j.cnki.11-2246.2024.0405 Abstract （91）      PDF（pc） （5137KB）（118）       Knowledge map    Save In order to improve the monitoring ability of slope hazards,this paper proposes a slope anomaly monitoring technology based on deep learning and image local feature extraction. By extracting the two-dimensional coordinates of natural features of the slope,this technology constructs the triangular target network. As the slope danger range is defined by the changing area of the triangular network,feature points with the same name are extracted within the change range,while the displacement of those feature points describes the slope change. The first step is to take images before and after the slope occurs,followed by identifying the natural features of the slope with the target detection model YOLOv5. In the semantic segmentation model DeepLabV3+,the extracted natural features are semantically segmented to obtain their binarized areas,and their two-dimensional coordinates are determined by determining the centre of the binarized area. As a next step,the triangular target network will be constructed by combining the two-dimensional coordinate lattices of all natural features,and the slope change range is delineated as the triangular network changes. After analyzing the image,the feature points with the same names within the change range are extracted using the image feature extraction technology,and their displacement distance and direction are used to evaluate the slope change. According to the test results,this technology is effective at monitoring slope changes,and it is a feasible tool for slope monitoring engineers. Reference | Related Articles | Metrics

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Intelligent identification and location of defects in water supply pipeline based on improved YOLOX algorithm SU Changwang, HU Shaowei, ZHANG Haifeng, PAN Fuqu, SHAN Changxi Bulletin of Surveying and Mapping    2023, 0 (12): 70-75.   DOI: 10.13474/j.cnki.11-2246.2023.0361 Abstract （80）   HTML （6）    PDF（pc） （4266KB）（95）       Knowledge map    Save To solve the problem of difficult and slow real-time automated detection of defects in water supply pipelines, a new intelligent identification and positioning method for water supply pipelines is proposed based on a dataset of pipeline defect data collected from actual engineering projects. The new YOLOX algorithm model, which incorporates an attention module, is developed and used for algorithm training and prediction using a dataset of video frames. Test results show that the YOLOX algorithm model with attention mechanism achieved an average testing accuracy of 94%, a mAP value of 84%, and an average recognition speed of 16 m/s. Additionally, compared with three other commonly used algorithm models (YOLO V3 and Fast R-CNN), the new model showed the best overall performance. This proposed model can also be applied to real-time video detection, providing an efficient and accurate detection technology and method for the intelligent identification and positioning of defects in water supply pipelines. Reference | Related Articles | Metrics

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Surface subsidence monitoring and predictive analysis in Hexi area of Nanjing based on SBAS-InSAR and MA-PSO-BP BI Lingyu, SUN Chengzhi, QIAO Shen Bulletin of Surveying and Mapping    2024, 0 (4): 48-53,82.   DOI: 10.13474/j.cnki.11-2246.2024.0409 Abstract （78）      PDF（pc） （5184KB）（94）       Knowledge map    Save In view of the rapid urbanization in Hexi area of Nanjing and the few researches on settlement prediction in this area, this paper proposes a monitoring and prediction model of urban surface deformation based on small baseline subsets-interferometric synthetic aperture radar (SBAS-InSAR) and moving average-particle swarm optimization-backpropagation(MA-PSO-BP) neural network algorithm. The settlement monitoring of the Hexi area of Nanjing is carried out by using the 22 Sentinel-1A lift rail data from March 2020 to March 2022, the variable of the lifting rail in the study area is obtained, the trend and the causes of settlement in Hexi are analyzed, and the settlement value obtained is used as the sample input of the PSO-BP network model to construct a network prediction model. The results show that SBAS-InSAR technology can effectively monitor the long-term settlement of the city, there are different degrees of settlement in Hexi area of Nanjing, the settlement rate is -25.3～20.5 mm/a, compared with the historical settlement study, the settlement trend expands from north to south, combined with the settlement monitoring data of SBAS-InSAR, compared with BP neural network and PSO-BP neural network prediction model, the accuracy of the settlement prediction model after interpolation of sample data is the highest. Reference | Related Articles | Metrics

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Application of time series InSAR technology in deformation monitoring along the subway GAO Peng, GENG Changliang, LI Peng, XIONG Qizhi Bulletin of Surveying and Mapping    2024, 0 (4): 112-118.   DOI: 10.13474/j.cnki.11-2246.2024.0419 Abstract （69）      PDF（pc） （2330KB）（93）       Knowledge map    Save Time series InSAR technology is an effective means to monitor surface deformation. Based on 179 Sentinel-1A image data,PS-InSAR technology is used to monitor and analyze the surface deformation along the Cishousi station to Lucheng station of Beijing subway Line 6,and the typical settlement interval is analyzed in detail. At the same time,the monitoring results are compared and analyzed and the accuracy is evaluated by using the ground level data.At the same time,the accuracy of monitoring results is evaluated using ground leveling data. The results show that there are different degrees of settlement along Beijing subway Line 6,and the average annual settlement rate is different from west to east. The average annual settlement rate in the west of Jintailu station is small,but it increases rapidly from Jintailu station to east,and the maximum average annual settlement rate is -32 mm/a. The tilt value to the east of Jintailu station is large,and the tilt value fluctuates obviously,which leads to uneven settlement.There is a strong correlation between leveling results and PS-InSAR monitoring results. The average error between them is 3.5 mm and the median error is 4.7 mm,which shows that PS-InSAR has certain reliability and accuracy. Reference | Related Articles | Metrics

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High-precision 3D modeling technology of urban real scene based on NeRF SUN Jianhua, LI Wei, YUAN Weizhe, WANG Feng, GU Jiaming Bulletin of Surveying and Mapping    2024, 0 (4): 129-134.   DOI: 10.13474/j.cnki.11-2246.2024.0422 Abstract （99）      PDF（pc） （6094KB）（91）       Knowledge map    Save In order to better apply NeRF high-precision 3D modeling in the 3D digital reconstruction of urban real scenes,this paper divides the large scene into sub-NeRF based on NeRF technology,and initializes the polygon mesh by constructing multiple octahedral bodies in the scene. And the vertices of the polygon faces are continuously optimized during the training process. After the training is completed,the weights of the encoder-decoder network are obtained,and different levels of polygon mesh refinement are performed on each independent block through vertex optimization. From satellite-level images that capture city overviews to ground-level images that show complex details of buildings,multi-scale data for urban detail and spatial coverage are constructed through progressive learning. The neural network voxel rendering model uses a multilayer perceptron (MLP) to realize the parameterization of volume density and color,and uses a hierarchical sampling method to realize the sorting distance vector of rays between the near plane and the far plane of a predefined viewing angle,so as to realize real-time interactive rendering of large-scale scenes. Then,GIS and NeRF are fused to provide a new solution for tasks such as multi-data fusion,query,analysis,measurement,annotation and sharing,so as to achieve instant and smooth dragging,zooming and 360° browsing and viewing of scenes without dead ends. This fusion makes it easy to integrate various data sources for spatial analysis in 3D scenarios such as urban planning,land management and environmental monitoring. Reference | Related Articles | Metrics

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Temporal and spatial evolution characteristics of land desertification sensitivity in Inner Mongolia autonomous region from 1980 to 2020 FU Qiang, ZHANG Haiming Bulletin of Surveying and Mapping    2023, 0 (7): 14-17,24.   DOI: 10.13474/j.cnki.11-2246.2023.0194 Abstract （120）   HTML （2）    PDF（pc） （1347KB）（74）       Knowledge map    Save Ecological sensitivity evaluation is one of the important foundations for studying the ecological status of territorial space and identifying different kinds of ecological protection spaces. This paper studies the spatio-temporal evolution of desertification ecological sensitivity in Inner Mongolia autonomous region from the year 1980 to 2020 at 1 km×1 km spatial grid scale based on quantitative evaluation method of desertification ecological sensitivity. The results show that: ①The spatial distribution of desertification sensitivity grade represents a basic pattern of lower in the east and higher in the west and north of Inner Mongolia;②38.3% of the grids remain the same sensitivity grade unchanged, and 37.9% of the grids maintain the sensitivity grade below moderate, and 38.8% of the grids maintain the sensitivity grade above moderate sensitivity (inclusive); ③Places such as mountain area of the northern section of great Khingan mountains, northern part of Hulunbuir grassland, West Liaohe plain, Hetao-Tumochuan plain and eastern and southern part of Ordos plateau remain low sensitivity grade or continue to reduce, while places such as southern part of Alxa plateau, the region on the north of hunshandake sand land remain high sensitivity grade or continue to increase. This study can provide scientific methods and spatial basis for delimitation of ecological protection and related policy-making. Reference | Related Articles | Metrics

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Landslide stability monitoring in southwest mountainous areas based on SBAS-InSAR technology XING Mingze, ZUO Xiaoqing, ZHANG Jianming, HUANG Cheng, LI Yongfa, BU Jinwei, SHI Chao Bulletin of Surveying and Mapping    2024, 0 (2): 63-68.   DOI: 10.13474/j.cnki.11-2246.2024.0211 Abstract （94）      PDF（pc） （6409KB）（72）       Knowledge map    Save In this study, the small baseline subset time-series InSAR (SBAS-InSAR) technique is employed to monitor surface deformation in the northeastern part of Xuanwei, Yunnan province. Deformation results from January 2021 to June 2023 are obtained, and the deformation characteristics of the selected typical landslide are analyzed. GNSS monitoring data are collected and compared with the InSAR deformation monitoring for validation. Furthermore, the response of InSAR deformation to precipitation is analyzed. The research findings demonstrate the effectiveness of SBAS-InSAR in monitoring typical landslide in the southwestern mountainous regions. These results offer support for the early identification of landslide hazards and hold significant reference value for the stability monitoring of similar landslide disasters. Reference | Related Articles | Metrics

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InSAR observations constrained coseismic slip distribution and Coulomb stress variation of Mw 6.7 Menyuan earthquake in 2022 WANG Xin, LI Shuiping, KANG Jing Bulletin of Surveying and Mapping    2023, 0 (7): 32-38.   DOI: 10.13474/j.cnki.11-2246.2023.0197 Abstract （188）            Knowledge map    Save In this paper, the line-of-sight (LOS) co-seismic deformation field of the Mw 6.7 Menyuan earthquake in Qinghai province on January 8, 2022 is obtained by using synthetic aperture radar differential interferometry (DInSAR) technology based on the Sentinel-1A satellite ascending and descending data. The non-negative least squares principle is used to retrieve the geometric parameters and co-seismic slip distribution of seismogenic faults. Finally, the Coulomb stress variation is calculated based on the fault slip distribution parameters and Coulomb fracture criterion. The results show that the Menyuan earthquake caused significant surface deformation, the coseismic deformation area is about 33 km×22 km, and the maximum LOS shape variables of ascending and descending data are -60 and 85 cm, respectively. Co-seismic sliding model display, the Menyuan earthquake is a left-lateral strike-slip event with a little thrust, and caused a co-seismic rupture about 36 km long (24 km for the main fault and 12 km for the branch fault) on the surface. The main rupture area is concentrated in 0～15 km depth, and the maximum slip of the main fault is 2.94 m, corresponding to 1.5 km depth.The maximum slip of the branch fault is 1.43 m, corresponding to 4.5 km depth. The seismic moment releases by inversion is 1.37×10 19 N·m, which is equivalent to a Mw 6.73 earthquake. Based on the results of field investigation and fault inversion, it is preliminarily determined that the co-seismogenic fault is the west end of Lenglongling fault and ruptures to the east end of Tuoleshan fault. The results of coseismic Coulomb stress variation and aftershock distribution show that the Coulomb stress at the east end of Lenglongling fault and the west end of Tuoleshan fault are obviously under loading condition, and the risk of strong earthquakes in the future is high. Reference | Related Articles | Metrics

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Based on 3D laser scanning and BIM integrated technology 3D modeling method of underground buildings in urban rail transit YANG Qixuan, REN Ruiliang, MA Quanming, SUN Xuekong, BIAN Chunlei Bulletin of Surveying and Mapping    2024, 0 (4): 119-123.   DOI: 10.13474/j.cnki.11-2246.2024.0420 Abstract （56）      PDF（pc） （1739KB）（70）       Knowledge map    Save This paper combines 3D laser scanning technology with BIM technology to realize 3D modeling of underground stations, tunnels and ancillary facilities of urban rail transit. The point cloud data is obtained by laser scanner, and the point cloud processing software is used for pre-processing, including splicing, denoising and simplifying operations. Then, the feature point cloud extraction method combined with BIM modeling software and visualization programming technology, which is used to effectively solve the problem that traditional surveying and mapping methods can not obtain and process complex underground building information and information island in BIM model. This method is of great significance for the future development of urban rail transit engineering construction and operation and makes maintenance management towards informatization and three-dimensional visualization. Reference | Related Articles | Metrics

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Extraction of domestic satellite images patches based on deep learning PANG Min Bulletin of Surveying and Mapping    2024, 0 (4): 124-128,134.   DOI: 10.13474/j.cnki.11-2246.2024.0421 Abstract （66）      PDF（pc） （6969KB）（68）       Knowledge map    Save This paper addresses the characteristics of domestic satellite imagery,such as multi-temporal,long-time series,massive,and massive multi-source data,proposes an efficient and accurate method for the extraction of satellite imagery patches. Based on the principles of deep learning,this method constructs a semantic segmentation model for ground objects and a group of intelligent algorithms for patch extraction based on deep learning theory,enabling the automatic recognition of the features,patterns,and attributes of satellite imagery patches,which leads to the intelligent and automated extraction of these patches. Experimental results demonstrate that this method achieves a high level of accuracy in the extraction of patches from domestic satellite imagery,provides important support for subsequent image processing,analysis,and applications. Reference | Related Articles | Metrics

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Crack information extraction based on high-resolution images of metro tunnels WEI Xianghui, SUN Liang, ZHAO Shuoyang Bulletin of Surveying and Mapping    2024, 0 (4): 90-95.   DOI: 10.13474/j.cnki.11-2246.2024.0415 Abstract （56）      PDF（pc） （3136KB）（67）       Knowledge map    Save Due to the fact that most of the cracks do not have distinctive features and are affected by cable,scratches,cobwebs and other linear interferences inside the tunnels,the detection effect of the existing crack detection methods,using high-resolution images,still needs to be improved. This paper takes the lining cracks as the research object,realizes the non-destructive data acquisition of the tunnel surface information based on the tunnel camera system,and acquires the high-resolution image data of 4096×2168 pixels. And we clarify the interference factors of crack identification,and constructs the interference data set and the real texture data set based on the characteristics of the disease; takes the Mask R-CNN model as the baseline framework,and adopts the K-means and genetic algorithm to optimize the parameters of RPN network. The detection effect and performance of this paper's algorithm are illustrated using comparative and ablation experiments. The results show that the algorithm proposed in this paper can realize the recognition and length measurement of tunnel cracks under high-resolution images,with lower probability of leakage and false detection,and has better detection performance for the slender and less obvious cracks,and the measured values of the cracks can provide reference information for the operation and maintenance of the subway. Reference | Related Articles | Metrics

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Analysis of local surface subsidence characteristics in Tianjin based on InSAR technology ZHANG Qian, MA Yue, ZHOU Hongyue, YAN Shiyong Bulletin of Surveying and Mapping    2024, 0 (2): 74-79.   DOI: 10.13474/j.cnki.11-2246.2024.0213 Abstract （97）      PDF（pc） （39597KB）（66）       Knowledge map    Save Tianjin is one of the areas in our country with the most severe ground subsidence. This article is based on the integrated distributed scatterer interferometry (DS-InSAR) technology, which processed 58 scenes of Sentinel-1A data from January 2021 to June 2023. It obtained the latest surface deformation characteristics in the southern region of Tianjin. By combining information on land use, hydrogeology, and other factors, a typical subsidence analysis was conducted. The results are as follows:①There is significant variation in the distribution of ground subsidence in Tianjin, with obvious uneven subsidence characteristics. The southwestern region is the most severely affected area, with a maximum subsidence rate of 85.2 mm/a. ②The ground subsidence in Tianjin is closely related to excessive groundwater extraction, increased surface loading, and geological structures. This study provides data support and decision-making basis for geological disaster prevention and control in Tianjin. Reference | Related Articles | Metrics

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Water leakage detection of shield tunnels based on 3D laser scanning BAO Yan, KIM IL BOM, ZHANG Dongliang, ZHU Zetian, MA Nengneng Bulletin of Surveying and Mapping    2024, 0 (4): 101-106.   DOI: 10.13474/j.cnki.11-2246.2024.0417 Abstract （49）      PDF（pc） （2089KB）（65）       Knowledge map    Save Water leakage always occurs in shield tunnels during operations that may result in cracks and corrosion of rebars. Such defects could jeopardize the safety of tunnel operations. Water leakage detection is thus necessary to ensure safe tunnel operations. This study proposes a method using 3D laser scanning technology for identifying locations of water leakages in shield tunnels. Firstly,the proposed method collects point cloud data for generating a grayscale image of the tunnel surface based on the corrected reflection intensity values. Then,dilation and erosion algorithms are used for preprocessing of the imagery data. Next,the authors use connected domain algorithm to derive locations and area of water leakage. Finally,the authors have validated the proposed method through a case study. Results show that the proposed method could achieve 92% accuracy for water leakage detection in shield tunnels. Reference | Related Articles | Metrics

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Some thoughts on the national platform for common geospatial information services (Tianditu) HUANG Wei, ZHANG Hongping, XU Yong, WANG Zhen Bulletin of Surveying and Mapping    2024, 0 (4): 145-149,155.   DOI: 10.13474/j.cnki.11-2246.2024.0425 Abstract （57）      PDF（pc） （1327KB）（64）       Knowledge map    Save Tianditu is an important carrier for government departments to provide common geographic information services. It integrates massive geographic information resources and provides users with authoritative, standard, and unified online geographic information services. In the new era, with the development of economy, society, and technology, various fields have put forward higher requirements for common geographic information services. Therefore, by analyzing the situation faced by the construction of Tianditu, this paper proposes that the construction of Tianditu in the new era needs to focus on solving the problemof accurate coupling among the service objects, service content, and service methods, and focus on promoting the continuous evolution of the construction model and overall structure of the common geographic information service platform under new technological conditions, continue to improve the service model of geographic information resources from discrete, offline to integrated, and online, and establish a nationally integrated geographic information resource opening and sharing system. Reference | Related Articles | Metrics

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Exploration and practice of reforming innovation education for geographic information science specialty in the big data era SHI Yan, DENG Min, LIU Baoju, CHEN Bingrong Bulletin of Surveying and Mapping    2024, 0 (4): 179-182.   DOI: 10.13474/j.cnki.11-2246.2024.0431 Abstract （51）      PDF（pc） （1444KB）（62）       Knowledge map    Save Big data brings new opportunities and challenges for college professional innovation education in the new era. Taking the geographic information science specialty as an example, this paper systematically analyzed and summarized the current situation of college innovation education in our country and the main challenges it is facing. We made an exploration and practice for the reform of innovation education for geographic information science specialty in the big data era, and proposed new reform measures based on the permeation of innovation idea, the raising of innovation awareness, the training of innovation thinking and the evaluation of innovation ability. These measures can provide references for the comprehensive construction of college innovation education system. Reference | Related Articles | Metrics

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Inversion of Taipu River water quality parameters by UAV hyperspectral imaging technology LIANG Wenguang, WU Yongfeng, SHI Yifan, WANG Dongmei, WANG Yihong Bulletin of Surveying and Mapping    2024, 0 (4): 29-34.   DOI: 10.13474/j.cnki.11-2246.2024.0406 Abstract （68）      PDF（pc） （6568KB）（60）       Knowledge map    Save In order to quickly and comprehensively grasp the water environment status of Taipu River,this study takes part of Taipu River as the test area,builds mathematical statistical models of three water quality parameters,including suspended matter concentration,turbidity and transparency,based on UAV hyperspectral data and measured water quality data,and then carries out accuracy evaluation. The model with the highest evaluation accuracy is selected to invert and analyze the water quality of Taipu River. The results show that: ①The hyperspectral band with the highest correlation with suspended matter concentration,turbidity and transparency is about 880～900 nm,and the correlation trend of suspended matter concentration and turbidity is consistent with the reflectance of each hyperspectral band,while the correlation trend of absolute value between transparency and reflectance of each hyperspectral band is consistent with the previous two. ②In the inversion model of suspended matter concentration,the ratio index model has the best effect (test set R 2=0.91,validation set RMSE=27.04 mg/L,validation set MAPE=47.04%). And in the turbidity inversion model,the ratio index model has the best effect (test set R 2=0.92,validation set RMSE=16.50 NTU,validation set MAPE=15.24%). The normalized index model has the best inversion effect among transparency inversion models (test set R 2=0.85,validation set RMSE=2.43 cm,validation set MAPE=8.38%). By comparing the inversion effects of the three water quality parameters,we can see that the inversion effect of transparency is the best,followed by turbidity,and finally the concentration of suspended matter. ③The inverted suspended matter concentration of Taipu River is generally low,while the eastern part is high. Turbidity is generally high in the eastern part,followed by the western part,and lowest in the central part. Transparency is generally high in the western and central part,and lowest in the eastern part. Reference | Related Articles | Metrics

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Multi-source remote sensing landslide hazard identification method driven by knowledge graph LI Yongxin, WANG Defu, MA Zhigang, FAN Yajun, YANG Benyong, LIU Li, LUO Chao Bulletin of Surveying and Mapping    2024, 0 (1): 12-18.   DOI: 10.13474/j.cnki.11-2246.2024.0103 Abstract （212）      PDF（pc） （4718KB）（60）       Knowledge map    Save Remote sensing technology plays an important role in the field of geological disaster prevention and control. With the development of aerospace technology, more remote sensing data can be obtained and effectively applied to the identification of geological hazard bodies, especially in the identification of landslide hazards. Comprehensive use of InSAR and optical remote sensing data to identify geological hazards is a hot topic in recent research. The traditional recognition process relies entirely on the work experience of interpreters, with strong subjectivity and no fixed recognition logic to follow. Based on SBAS-InSAR and optical satellite imagery, this paper analyzes the process of landslide hazard identification, and constructs the Knowledge graph and identification extraction matrix model of landslide identification. Under the logic drive of the Knowledge graph, the regional spatial characteristics of landslide hazards identified by the combination of “optical remote sensing+InSAR” are analyzed, providing a reference implementation scheme with the significance of semi quantitative extraction of indicators for landslide wide area identification, and realizing the identification process of landslide hazards from completely subjective to semi quantitative. Experiments show that this method can provide reference for relevant research and practical engineering applications, and has certain application value. Reference | Related Articles | Metrics

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Review of the current research status of subway deformation monitoring HEI Junmiao, WANG Li, LI Ang Bulletin of Surveying and Mapping    2024, 0 (2): 39-44,79.   DOI: 10.13474/j.cnki.11-2246.2024.0207 Abstract （150）      PDF（pc） （1655KB）（59）       Knowledge map    Save This article includes the deformation monitoring techniques during subway construction and operation respectively, and then sorts out the current research status of subway deformation monitoring data processing methods. It summarizes the existing problems in the current research of subway deformation monitoring, and prospects the development direction of subway deformation monitoring research, providing some ideas for the systematization and automation of subway deformation monitoring research. Reference | Related Articles | Metrics

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A combined method of PCA and IR-MAD for detecting rainfall landslide ZHAO Qiong, ZHANG Jinshui, ZHENG Wenwu Bulletin of Surveying and Mapping    2024, 0 (5): 7-11,18.   DOI: 10.13474/j.cnki.11-2246.2024.0502 Abstract （33）      PDF（pc） （28368KB）（56）       Knowledge map    Save Seasonal patterns of torrential rainfall is one of the primary triggering agents which predisposes to catastrophic landslides, especially in the complex terrain medium-elevation mountains and hilly of China. The geographic scene resulted in landslide occurrence is very complex. Therefore, exploring the landslide detection methods in complex situations has important significance for damage assessment and post-disaster emergency investigation. In this paper, we propose a landslide detection method combining PCA and IR-MAD to realize the accurate extraction of nascent landslide.The research results show that compared with the existing methods, the proposed method effectively inhibits the disturbance of landslide detection caused by seasonal factors such as crop seeding and harvest, flood season caused by heavy rainfall, and other similar remote sensing features of bare land and tidal flat. The accuracy rate of landslide detection and the stability of landslide identification model have been improved to a certain extent. Reference | Related Articles | Metrics

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Study on land use change and spatiotemporal variation of carbon storage in Beijing-Tianjin-Hebei based on InVEST model PENG Yunni, SANG Huiyong, ZHAI Liang, ZHANG Ziyi, DUAN Jinjiang Bulletin of Surveying and Mapping    2024, 0 (1): 19-24,31.   DOI: 10.13474/j.cnki.11-2246.2024.0104 Abstract （137）      PDF（pc） （3110KB）（53）       Knowledge map    Save The increase in atmospheric CO 2content is an environmental issue of widespread international concern, and human activities change land use patterns, and land-use/land-cover (LULC) changes further affect terrestrial ecosystem structure, function, and carbon cycling. With the support of global land cover data GlobeLand30, This paper analyzed the land use changes in Beijing-Tianjin-Hebei from 2000 to 2020, used InVEST model to imitate the Spatiotemporal changes of carbon stocks, and used the spatial autocorrelation analysis to study its zoning. The results show that:①From 2000 to 2020, the largest change area in Beijing-Tianjin-Hebei region is cultivated land and artificial surface, with an area decrease of 340 222.124 hm 2and an area increase of 246 333.493 hm 2respectively. ②The total carbon reserves of Beijing-Tianjin-Hebei in 2000, 2010 and 2020 are 1 666.47×10 6、1654.63×10 6、1632.88×10 6 t, the main reason for the decline in carbon storage are the loss of arable land and the expansion of artificial land surface. ③The high value of carbon storage is mainly distributed in mountain and forest areas with relatively high altitude, while the low value areas of carbon reserves are mainly concentrated in central Beijing, the coastal zone of Tianjin and Hebei and the eastern Cangzhou, southern Tangshan. ④The results of local autocorrelation show that the high value areas of carbon reserves are clustered in the north and west of the study area. Among the regions with low to low aggregation, Dongli district of tianjin city and Hanshan district of Handan city, Hebei province show a relatively obvious weakening trend. Reference | Related Articles | Metrics

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Co-seismic deformation extraction and accuracy analysis of the Maduo Earthquake based on the combined of different global navigation satellite systems TAN Mingming, FAN Yaling, Lü Kexin, HAO Shang, XU Tao, CAI Guotian, ZHANG Jie, LI Zhicai Bulletin of Surveying and Mapping    2023, 0 (7): 80-84.   DOI: 10.13474/j.cnki.11-2246.2023.0205 Abstract （98）   HTML （3）    PDF（pc） （1431KB）（48）       Knowledge map    Save At present, high frequency global navigation satellite system (GNSS) signal has been widely used in co-seismic deformation monitoring of major earthquakes. In this paper, the accuracy of seismic deformation monitoring by different satellite navigation system combinations is comprehensively analyzed. Based on the high frequency (sampling rate of 1 Hz) GNSS observation data of eight regional CORS stations in the near and far fields around the Qinghai Maduo Earthquake (Mw 7.4), the co-seismic deformation achieved and accuracy improvement ratio from single system to multi-system combination are preliminarily analyzed by precision point positioning technology processing strategy. Then, the co-seismic deformation error and accuracy improvement ratio of multi-system combination with the same number of systems and different satellite navigation system combinations are compared and analyzed. Finally, based on the combination of GREC2 (GPS/GLONASS/Galileo/BD-2) and GREC3 (GPS/GLONASS/Galileo/BD-3), the accuracy difference between BD-2 and BD-3 is analyzed. The results show that the accuracy of co-seismic deformation observation by multi-system combination is better than that by single system, among which GEC2+3 (GPS/Galileo/BD-2/BD-3) combination has the best accuracy. Compared with single system GPS, the accuracy of co-seismic deformation observation by GEC2+3 combination can be improved by 30%～60% in horizontal direction, while the vertical accuracy can be improved by 30% on average. The satellite navigation system combination with Beidou has the best performance. Among the satellite navigation system combination with 3 systems, the error of the combination with Beidou in horizontal direction and vertical direction can reach 5～6 mm and 6～9 mm, respectively. Reference | Related Articles | Metrics

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Evaluation of eco-environment quality in karst area using improved remote sensing ecological index JIAO Xiaomeng, ZHANG Bing, GAO Yang, ZHENG Mingxuan, ZHOU Yan, KUANG Weijie Bulletin of Surveying and Mapping    2024, 0 (4): 54-60.   DOI: 10.13474/j.cnki.11-2246.2024.0410 Abstract （57）      PDF（pc） （3474KB）（48）       Knowledge map    Save This paper selects four indices,including greenness (NDMVI),wetness (Wet),rocky desertification (RDI),and heat (LST),to construct an improved remote sensing ecological index (IRSEI) to evaluate the ecological environment quality of county karst areas. Based on county-scale remote sensing images and other geographical information data from 2003 and 2023,IRSEI and RSEI are quantitatively compared in several aspects. The results show that IRSEI contains richer information on mountain vegetation and rocky desertification conditions and can better characterize and evaluate the ecological environment quality of karst areas. And IRSEI is suitable as a quantitative indicator for the ecological environment quality evaluation of karst areas. In the study area of Luoping County,both improvement and degradation of ecological environment quality occurred between 2003 and 2023,but the overall ecological environment quality dropped by 7.46%,and the ecological grade changed from good to medium. From the perspective of spatial distribution,the areas with poor ecological quality in karst areas of Luoping County are mainly distributed in the north,and the areas with excellent ecological environment quality are mainly distributed in the central part. Reference | Related Articles | Metrics

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Study on sample unbalance in landslide recognition algorithm based on depth learning WANG Lixia, XI Wenfei, SHI Zhengtao, ZHAO Zilong, QIAN Tanghui, ZHAO Lei, MA Yijie Bulletin of Surveying and Mapping    2024, 0 (5): 12-18.   DOI: 10.13474/j.cnki.11-2246.2024.0503 Abstract （30）      PDF（pc） （6696KB）（46）       Knowledge map    Save Landslides are a common geological disaster that can cause significant property losses and casualties to natural ecosystems and humans once they occur. How to quickly and accurately obtain landslide information is crucial to disaster prevention and mitigation. Traditional deep learning methods depend heavily on the quality of landslide samples, but the quality of existing samples is uneven, and the impact of landslide sample imbalance on the performance of deep learning models is rarely considered. Aiming at the problem of how to improve model accuracy by improving sample quality, this paper proposes a Faster R-CNN landslide target detection method based on multi-source unbalanced samples starting from sample quality. By conducting integrated training on a variety of imbalanced samples, the impact of different samples on the comprehensive performance of the model is studied. The results show that:①The accuracy rate of the model is 85.16%, F1 score of 0.69, precision of 56.96%, recall of 86.58%, and the missed detection rate is 0.33 under the imbalance of difficult samples. After strengthening the sample quality, the accuracy rate increases by 2.04%. The precision increased by 4.29%, the recall rate increased by 1.71%, and the missed detection rate decreased by 0.04. ②Under the imbalance of positive and negative samples, the accuracy rate of the model is 96.03%, F1 score of 0.78, precision of 64.50%, recall of 97.15%, and the missed detection rate is 0.09. After adding difficult samples to participate in the training, the accuracy rate drops by 8.45%. The rate dropped by 6.93%, the recall rate dropped by 7.25%, and the missed detection rate increased by 0.18. Difficult samples have a greater impact on the overall performance of the model. By improving the quality of these samples, the model detection accuracy can be improved. Therefore, the method proposed in this article provides a reference for solving the problem of landslide data sample imbalance in deep learning. Reference | Related Articles | Metrics

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The construction of the deep-sea time variant thermohaline model in the North Pacific Ocean by combining CTD, seabed terrain and ARGO data ZHANG Jinhui, LI Shanshan, YANG Guang, FAN Diao, LING Qing Bulletin of Surveying and Mapping    2023, 0 (12): 94-101,126.   DOI: 10.13474/j.cnki.11-2246.2023.0365 Abstract （59）            Knowledge map    Save Faced with the fact that the measured data of deep-sea temperature are insufficient, this paper redivided the experimental sea area of the North Pacific ocean according to the characteristics of CTD temperature profile changing with sea depth. Combined with the seabed terrain and Argo data, the deep-sea monthly grid temperature model of the North Pacific ocean from 2005 to 2020 is constructed,and the deep ocean steric sea level change is inversed. The experiments results show that: ①Compared with other mathematical model, the difference between the mean mathematical model of deep-sea temperature profile constructed in this paper and the measured data of CTD is 1～2 orders of magnitude smaller, which can more accurately reflect the characteristics of deep-sea temperature profiles changing with sea depth.in various regions. ②The maximum difference between temperature model data and CTD measured data, EN4 do not exceed 0.20℃ and 0.60℃, the average do not exceed 0.03℃ and 0.50℃, and the standard deviation not exceed 0.06℃ and 0.002℃.③ Based on the temperature model and EN4, the deep ocean steric sea level change in the North Pacific ocean is basically consistent, in which the rising trend from 2005 to 2010 is 0.52±0.09 and 0.73±0.11 mm/a, and the rising trend from 2010 to 2020 is 0.02±0.03 and -0.01±0.01 mm/a,which is consistent with the research conclusion based on heat content change in relevant literature.The rising trend in the whole study period is 0.11±0.17 and 0.09±0.11 mm/a. This shows that the temperature model data constructed in this paper has a certain reliability, which have a certain reference value for refining the genetic changes of regional sea level balance equation. Reference | Related Articles | Metrics

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Point cloud construction and differential analysis based on airborne laser scanning and oblique image matching TAN Jinshi, GAO Zhaozhong, YANG Minjing, ZU Weiguo, LIU Li Bulletin of Surveying and Mapping    2024, 0 (2): 134-139.   DOI: 10.13474/j.cnki.11-2246.2024.0224 Abstract （59）      PDF（pc） （3559KB）（45）       Knowledge map    Save Point clouds are an important part of 3D spatial data. Airborne laser scanning and tilt image matching are the two main point cloud construction techniques, which have commonalities and differences. The aim of this paper is to analyse the differences between airborne laser scanning and tilted image matching techniques for point cloud construction. Firstly, the specific methods of airborne laser scanning and tilted image matching point cloud construction are described, and then compare and analyse in terms of point cloud construction effect, data integrity, density, accuracy and vegetation penetration in conjunction with case studies. The results show that both techniques have good data integrity and can produce highly dense point clouds that far exceed the specification requirements, with comparable and high accuracy, but the laser scanning has loopholes in the occluded areas and distorted details in the blind local areas of image matching; the laser point cloud has good penetration and can achieve ground point cloud construction in different vegetation covered areas, while the image matching points have poor penetration. There is a lack in densely vegetated areas, can't even construct ground points. The results of the study provide a reference for the selection and optimization of subsequent point cloud construction methods, and have certain research and application value. Reference | Related Articles | Metrics