Most Down Articles

Published in last 1 year | In last 2 years| In last 3 years| All| Most Downloaded in Recent Month | Most Downloaded in Recent Year|

In last 3 years

Please wait a minute...


For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails

Select

Multi-task automatic identification of loess landslide based on one-stage instance segmentation network SHI Yun, SHI Longlong, NIU Minjie, ZHAO Kan Bulletin of Surveying and Mapping    2022, 0 (4): 26-31.   DOI: 10.13474/j.cnki.11-2246.2022.0105 Abstract （259）   HTML （11）    PDF（pc） （5292KB）（345）       Knowledge map    Save Automatic landslide identification can solve the problem of slow speed of manual visual interpretation. The existing automatic identification methods based on deep learning are mainly single-task recognition methods such as object detection and semantic segmentation.In this paper, the instance segmentation network based on deep learning is used to explore a multi-task identification method that can achieve landslide target location and semantic segmentation simultaneously.Firstly, a dataset of 3822 loess landslide samples is constructed based on Google Earth images. Then,the multi-task automatic identification model of loess landslide based on small sample learning is constructed by using the one-stage instance segmentation network YOLACT. Finally, the identification results are evaluated by the large, medium and small scale landslide test samples. The results show as follows:①The average precision of landslide target positioning Box is 61.66%, the average precision of landslide semantic segmentation Mask is 62.0%, and the intersection over union of Mask in large scale test is 0.88. ②The landslide identification model built based on YOLACT can complete the dual-task identification of landslide target positioning and high-precision mask segmentation at the same time, which proride technical support for the automatic multi-task identification and rapid mapping of landslide. Reference | Related Articles | Metrics

Select

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

Select

Analysis of soil moisture content changes in resource-based cities over a long time series: a case study of Xilinhot city LI Jun, SANG Xiao, ZHANG Chengye, ZHAO Wei, LIU Xinhua, WANG Hongpeng, WANG Jinyang, LI Jiayao, YANG Ying Bulletin of Surveying and Mapping    2021, 0 (7): 17-22,38.   DOI: 10.13474/j.cnki.11-2246.2021.0202 Abstract （422）   HTML （13）    PDF（pc） （10210KB）（183）       Knowledge map    Save Large-scale coal mining activities have disturbed the ecological environment. Soil moisture content is important as one of the disturbed ecological parameters. Currently, existing soil moisture content products have coarse resolution and are not suitable for regional scale studies, while microwave inversion of soil moisture content with fine resolution are limited by the data so that can not be used for long time series studies. This paper takes Xilinhot, an important coal production base in China, as the study area, and uses AMSR-E, AMSR-2 soil moisture content products from 2004 to 2020 and Landsat remote sensing images for the same period as the main data sources. The random forest method is used to downscale the AMSR-E/2 soil moisture content products. The variation characteristics of soil moisture content in the study area are analyzed by standard deviation ellipse. And the results show that:① Passive microwave soil water moisture downscaling method enables long time series and high spatial resolution monitoring of soil moisture content in resource-based cities. ② Precipitation is the dominant factor affecting soil moisture content changes in both mining and non-mining areas. ③ The overall distribution of soil moisture content in the study area shows a gradual increase in spatial characteristics from northwest to southeast, and this distribution pattern remained stable over long time scales. ④ Coal mining activities disturb soil moisture content, and the impact of different mining stages has different characteristics. The results of the study provide a scientific basis for the evaluation and protection of the ecological environment of coal cities. Reference | Related Articles | Metrics

Select

Evaluating ecological environment based on remote sensing ecological index in Shenfu mining area FAN Deqin, QIU Yue, SUN Wenbin, ZHAO Xuesheng, MAI Xiamei, HU Yingwen Bulletin of Surveying and Mapping    2021, 0 (7): 23-28.   DOI: 10.13474/j.cnki.11-2246.2021.0203 Abstract （410）   HTML （22）    PDF（pc） （16136KB）（183）       Knowledge map    Save Monitoring and evaluating the ecological environment with the help of remote sensing technology is of great significance for the sustainable development of mining area. In this paper, an improved scheme of remote sensing ecological index (RSEI) is proposed. Based on the original model which contains four ecological indexes (NDVI, WET, LST, NDSI), the net primary productivity (NPP) of vegetation is introduced to characterize the ecosystem function of mining area. The ecological environment of the mining area is evaluated and analyzed by every single ecological index using the five ecological indexes. Using the improved remote sensing ecological index, the ecological environment changes of Shenfu mining area from 2000 to 2016 are evaluated. The evaluation results show that the remote sensing ecological index of Shenfu mining area shows an overall upward trend from 2000 to 2016 (0.1/10 a), and the vegetation productivity in most areas increases gradually, which means that the ecosystem function recovers gradually, and the ecological environment quality improves gradually. Reference | Related Articles | Metrics

Select

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

Select

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

Select

Integrated navigation algorithm of multi-joint deep-sea vehicle WAN Yingneng, XU Xuehan, LIU Kexian Bulletin of Surveying and Mapping    2022, 0 (4): 90-95.   DOI: 10.13474/j.cnki.11-2246.2022.0116 Abstract （187）   HTML （4）    PDF（pc） （1266KB）（152）       Knowledge map    Save To solve the positioning problem of a multi-joint underwater vehicle, a integrated navigation algorithm based on strapdown inertial navigation+dead reckoning is proposed in this paper. The method measures the position of vehicle by the strapdown inertial navigation, and gets the position of the vehicle at the next moment by the dead reckoning, and the measured information is processed by Kalman filter to get the high accuracy position information. The performance of other two single navigations systems are studied comparatively on MATLAB/SIMULINK platform. The simulation results show that the position error can be controlled within 5 m when using the integrated navigation algorithm, which can meet the positioning requirements of the multi-joint underwater vehicle. Reference | Related Articles | Metrics

Select

Remote sensing image change detection based on improved interval type-2 fuzzy clustering SU Yifan, DANG Jianwu, WANG Yangping, YANG Jingyu Bulletin of Surveying and Mapping    2021, 0 (7): 44-51,58.   DOI: 10.13474/j.cnki.11-2246.2021.0207 Abstract （260）   HTML （5）    PDF（pc） （2309KB）（152）       Knowledge map    Save The complex fuzziness of remote sensing image can interfere with the result of image change detection so that the interval binary fuzzy C-means clustering algorithm is introduced to solve the problem. But the randomness of algorithm parameters will affect the accuracy of change detection. In this paper, the candidate solution of firefly algorithm is optimized by using local optimal solution, and the variable step size factor is introduced, so as to find the fuzzy factors of interval type-2 fuzzy C-means clustering algorithm adaptively. The interval type-2 fuzzy C-means clustering is carried out combining with the fuzzy factors obtained by optimization and the image change information is extracted by iteratively updating the membership degree. Finally, the weighted Karnik-mendel algorithm based on compound trapezoid rule is used to reduce type and resolve fuzzy to optimize clustering centre. And the change types are determined according to the principle of maximum membership. Through experimental verification, the method in this paper obtains the better fuzzy factors and more accurate clustering centers, has better robustness, and improves the change detection accuracy, and the detected change area is more elaborate. Reference | Related Articles | Metrics

Select

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

Select

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

Select

Extraction of rocky desertification information using NDVI-Albedo feature space LUO Jie, LIU Suihua, RUAN Ou, HU Haitao Bulletin of Surveying and Mapping    2022, 0 (4): 56-60,82.   DOI: 10.13474/j.cnki.11-2246.2022.0110 Abstract （289）   HTML （13）    PDF（pc） （7642KB）（125）       Knowledge map    Save Rocky desertification is one of the most crucial ecological and environmental problems in the karst area of Southwest China. Monitoring rocky desertification is an important task for the prevention and control of rocky desertification. Taking a typical rocky desertification research area of Dougu town in western Weining as an example, based on Landsat8 OLI remote sensing data, the normalized vegetation index(NDVI) and Albedo of the research area are calculated, and the rocky desertification difference index (RSDDI) is constructed through the NDVI-Albedo feature space to extract the rocky desertification information and verify its accuracy. Studies have shown that the rocky desertification difference index constructed based on the NDVI-Albedo feature space method can extract and classify rocky desertification information more accurately and conveniently, and the accuracy of the mapping for moderate rocky desertification and severe rocky desertification is both reaching more than 89%, the extraction effect is excellent, which is conducive to the quantitative assessment and monitoring of rocky desertification in the southwest of China karst area. Reference | Related Articles | Metrics

Select

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

Select

Hyperspectral image classification based on multi-feature fusion and dimensionality reduction algorithms DOU Shiqing, CHEN Zhiyu, XU Yong, ZHENG Hegang, MIAO Linlin, SONG Yingying Bulletin of Surveying and Mapping    2022, 0 (4): 32-36,50.   DOI: 10.13474/j.cnki.11-2246.2022.0106 Abstract （229）   HTML （21）    PDF（pc） （1956KB）（101）       Knowledge map    Save Hyperspectral images does exist redundant information, which brings certain side-effects on image classification. In this study, two dimensionality reduction algorithms, the CB method (CfsSubsetEval evaluator combines Best-First search strategies) and the PCA, and four multi-feature fusion combinations are proposed to construct eight schemes. The eight schemes combining with RF(random forest) classifier are then applied to classily hyperspectral images, and the best scheme for hyperspectral image classification are selected on the bases of the classification accuracy and Kappa coefficient. The results show that:①Multi-feature fusion can improve the classification accuracy of hyperspectral images, the classification accuracy of the hyperspectral image increases with considering geographic characteristics, texture characteristics, and exponential features gradually both in the two dimensionality reduction algorithms.②Considering the two dimensionality reduction algorithms, the classification accuracy based on CB reduction is generally higher than that of PCA dimensionality reduction. In terms of the classification accuracy based on eight schemes, the CB method with spectrum information, geographic characteristics, texture characteristics, and exponential features has the highest classification accuracy with 98.01% of overall classification accuracy, and 0.969 9 of Kappa coefficient. Reference | Related Articles | Metrics

Select

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

Select

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

Select

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

Select

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

Select

Improved HRNet applied to segmentation and detection of pavement cracks ZHANG Boshu, ZHANG Zhihua, ZHANG Yang Bulletin of Surveying and Mapping    2022, 0 (3): 83-89.   DOI: 10.13474/j.cnki.11-2246.2022.0082 Abstract （322）            Knowledge map    Save Aiming at the problems of low accuracy,loss of information and blurred edges in the traditional convolutional neural network for pavement crack segmentation,a pavement crack segmentation algorithm based on the improved HRNet model is proposed.The model is improved on the basis of the original HRNet,the backbone network part uses DUC module instead of bilinear interpolation;downsampling is changed to passthrough layer to replace the original convolution,SE-Block is introduced while performing step-by-step upsampling to re-calibrate the fusion of different feature layers.Comparing with the original HRNet and the other traditional convolutional neural networks U-Net,it can be concluded that the segmentation accuracy of this algorithm is the best on public data and self-made data sets,with F1 score reaching 91.31% and 78.69% respectively,proving that the algorithm can be very good to meet the needs of actual engineering. Reference | Related Articles | Metrics

Select

Groundwater storage data inversion and spatiotemporal evolution analysis in Henan province using GRACE and GLDAS data LI Mingyu, CHEN Lijun, LIU Guoxiang, MAO Wenfei, XIANG Wei, CAI Jialun, ZHANG Bo, ZHANG Rui Bulletin of Surveying and Mapping    2022, 0 (3): 121-126.   DOI: 10.13474/j.cnki.11-2246.2022.0089 Abstract （347）            Knowledge map    Save Traditional monitoring methods are difficult to acquire groundwater storage observation in large-scale and long-term.Thus,the groundwater storage data inversion has become a hot topic based on the GRACE gravity satellite.This paper uses the GRACE RL06 monthly data released by CSR during the period of 2012 to 2016 to produce terrestrial water storage changes.Later,subtract surface water storages changes calculated by the GLDAS hydrological model over the same period.Finally,the time series results of groundwater storage changes in Henan province are obtained.Considering the verification between the produced results and the measured groundwater level data,the calculated correlation coefficients are all at the significance level of 0.01,which indicates that the groundwater storage change monitoring method in this paper is highly reliable.From the least squares linear fitting change rate results,it can be seen that the main loss area of groundwater in the province is the northern region with the maximum rate exceeding 26 mm/a,and the main surplus area is located in the central and eastern regions with the maximum rate exceeding 16 mm/a.These mentioned results are basically consistent with existed researches as well as the main groundwater overexploitation areas announced by the Henan provincial Water Resources Bureau.The research results in this paper are aimed at using GRACE gravity satellite data and GLDAS hydrological model to obtain the spatial distribution difference and evolution trend of groundwater storage changes in Henan province.While providing data support for the rational use and protection of groundwater resources,it also offers reference for the protection and reasonable use of groundwater resources in the area. Reference | Related Articles | Metrics

Select

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

Select

Early identifying and monitoring landslides in Guizhou province with InSAR and optical remote sensing WU Lüchuan, WANG Jianhui, FU Yan Bulletin of Surveying and Mapping    2021, 0 (7): 98-102.   DOI: 10.13474/j.cnki.11-2246.2021.0216 Abstract （501）            Knowledge map    Save The topography and landforms of Guizhou province in China are complicated, and the climatic conditions of heavy precipitation make landslide disasters in Guizhou province occur frequently. To avoid damage bringing to people's lives and economic property caused by disasters, a reliable early landslide identification method and landslide monitoring method are urgently needed. Traditional landslide identification and monitoring methods have limitations. InSAR technology has unique advantages in large-scale landslide identification and monitoring, but landslide identification results based on a single deformation value are one-sided. Therefore, this paper uses Sentinel-1A Radar satellite image data and uses InSAR technology and optical remote sensing technology togather to carry out large-scale surface deformation monitoring and identification of dangerous deformation areas in Liupanshui city, Tongren city, Guiyang city and other regions in Guizhou province. The potential landslide identification methods based on the time series normalized difference vegetation index and landslide development environment elements are combined to investigate hidden landslide hazards in the study area. In this paper, time series InSAR technology is used to monitor key landslides in Yujiaying, to grasp the movement status of the landslide in time. The method of landslide identification and monitoring in this paper is of great significance for disaster prevention and management in Guizhou province. Reference | Related Articles | Metrics

Select

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

Select

Step-by-step identification method of crop leaf diseases based on transfer learning ZHAO Hengqian, YANG Yifeng, LIU Zelong, SONG Rui Bulletin of Surveying and Mapping    2021, 0 (7): 34-38.   DOI: 10.13474/j.cnki.11-2246.2021.0205 Abstract （309）   HTML （6）    PDF（pc） （3838KB）（71）       Knowledge map    Save Accurate identification of crop diseases can help improve the yield and quality of crops. As image training sample data of crop disease is limited, this paper adopts the transfer learning method combine with the step-by-step identification model to identify a host of crop disease types. Taking PlantVillage public data set of ten types of crop leaf images of three crops as training samples, the paper uses the direct recognition method to train the original model of VGG16 and ResNet and the model of transfer learning respectively, and obtain the classification results of the model. This paper proposes a step-by-step identification method, the training samples are classified according to the types of crops and disease types, the models are trained separately, and a step-by-step identification model is constructed. The experimental results show that the transfer learning method can increase the recognition accuracy by more than 20% on the basis of the original model. On the basis of this, the step-by-step identification method is introduced and compared with the direct recognition method, the accuracy of the VGG16 and ResNet models is increased by 14% and 8%, respectively. The transfer learning step-by-step identification method proposed in this research can realize the accurate identification of crop diseases in the case of small samples of training data, and can provide effective technical support for crop disease prevention and control. Reference | Related Articles | Metrics

Select

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

Select

Application research of BDS precise point positioning on dynamic positioning system of offshore oil drilling rig LUO Youan, JIANG Aiguo, YANG Fuxin, ZHANG Jie, HE Dongxu, XU Yinglong Bulletin of Surveying and Mapping    2022, 0 (4): 111-116.   DOI: 10.13474/j.cnki.11-2246.2022.0120 Abstract （219）   HTML （10）    PDF（pc） （1537KB）（71）       Knowledge map    Save The dynamic positioning (DP) offshore drilling rig depends on the high precision position reference datum. Precise point positioning (PPP) technology based on global navigation satellite system is the best choice to provide ocean high precise position service. At present, the high-precision position reference datum of offshore drilling rig mainly depends on GPS. With the completion of BeiDou Navigation Satellite System (BDS), the BDS real-time PPP will provide independent and high-precision position reference in the global ocean. In this paper, a self-developed receiver based on BDS real-time PPP is carried out and connected to a DP system in the South China Sea. The results show that compared with similar GPS receiver, the three-dimensional position difference after convergence is less than 50 cm. Meanwhile, the weight of BDS and GPS information is the same in Kongsberg DP system. It verifies the feasibility of DP offshore drilling rig operation based on single BDS position reference datum. Reference | Related Articles | Metrics

Select

Vertical accuracy evaluation of DEM data based on ICESat-2 ATLAS DING Xiameng, ZHANG Jixian, GUO Jing, ZHANG He, CHANG Yaru Bulletin of Surveying and Mapping    2022, 0 (12): 84-90.   DOI: 10.13474/j.cnki.11-2246.2022.0361 Abstract （452）            Knowledge map    Save In order to investigate the elevation accuracy of three open source DEM data, ASTER GDEMV3、SRTM1 DEM and AW3D30 DEM. In this paper, high precision ICESat-2 ATLAS altimetry data is used as reference data, comparative evaluation of elevation accuracy of DEM using GIS statistical analysis, error correlation analysis and mathematical statistics.The results show that:①The quality of AW3D30 is the most stable; SRTM1 DEM has the highest accuracy in the plains; the accuracy in the highland mountains is AW3D30 DEM, ASTER GDEMV3, and SRTM1 DEM in descending order.②The elevation accuracy of DEM data is influenced by the surface coverage and is closely related to the topographic factors. The performance of DEM data elevation accuracy in the two study areas with the same surface coverage is not consistent, SRTM has the best performance under the plain surface coverage with an average error of 3.15 m, and AW3D30 DEM has the best performance under the mountainous surface coverage with an average error of 7.61 m.③The influence of slope on the elevation accuracy of DEM data is large, and the elevation error of three kinds of DEM data in two study areas increases with the increase of slope; the influence of slope direction on the elevation accuracy of DEM data is small, and no obvious pattern is found. Reference | Related Articles | Metrics

Select

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

Select

Application of time series InSAR technology in prevention and control of surface deformation disaster of high-voltage transmission line ZHU Xingang, MING Sheng Bulletin of Surveying and Mapping    2021, 0 (7): 92-97.   DOI: 10.13474/j.cnki.11-2246.2021.0215 Abstract （405）            Knowledge map    Save On June 12, 2020, a rainstorm and waterlogging accident occurred on the high-voltage transmission line of Sencong line in Conghua district, Guangzhou city, resulting in collapse and waterlogging accidents in many tower areas. The study should use SBAS technology and based on the Sentine-1A data to obtain the regional surface shape variables from January to June in 2020 in the north of the chemical industry. The influence of heavy rainfall on the surface deformation of mountain body is studied. The comprehensive analysis and monitoring results show that most of the areas tend to be stable in the meantime, and some hilly areas are affected by external conditions and under the influence of high soil water content, it presents an unstable and continuous settlement trend. In the season with the heavy rain, the surface settlement rate will increase significantly, and the maximum settlement is more than -55.3 mm in the meantime. The seasonal heavy rainfall and human frequent construction activities brought by subtropical monsoon climate make landslides, waterlogging and other natural disasters prone to occur, which have a negative impact on regional environmental stability and security, regional development as well as the economic growth. Reference | Related Articles | Metrics

Select

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

Select

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