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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 （374）      PDF（pc） （3266KB）（302）       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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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 （287）      PDF（pc） （6094KB）（231）       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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Remote sensing image water body extraction based on U-Net, U-Net++ and Attention-U-Net networks LI Zhenxuan, HUANG Miner, GAO Fei, TAO Tingye, WU Zhaofu, ZHU Yongchao Bulletin of Surveying and Mapping    2024, 0 (8): 26-30.   DOI: 10.13474/j.cnki.11-2246.2024.0805 Abstract （285）      PDF（pc） （1871KB）（272）       Knowledge map    Save Currently, the application of deep learning in the extraction of water bodies from high-resolution remote sensing images has become a research hotspot in the remote sensing field. Among them, algorithms based on the U-Net network have demonstrated good performance in water body extraction. However, there is scarce research that provides in-depth and detailed comparisons of the performance differences of different U-Net network algorithms in water body extraction tasks. Therefore, this article selects three convolutional neural networks, named U-Net, U-Net++, and Attention-U-Net, and based on the GID dataset, draws conclusions through experiments and quantitative analysis. The results indicate that: U-Net++ achieves the highest training accuracy, followed by U-Net and Attention-U-Net, with accuracies of 0.912, 0.907, and 0.899 respectively. U-Net++ exhibits superior edge extraction capability compared to the other two networks. In segmenting different types of water bodies and distinguishing non-water areas similar to water bodies in remote sensing images, U-Net++ shows significantly better extraction results, while U-Net and Attention-U-Net are prone to omission errors and exhibit suboptimal performance. Reference | Related Articles | Metrics

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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 （250）      PDF（pc） （11003KB）（272）       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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Design and implementation of the GNSS/INS integrated software for bridge monitoring based on GINav MA Weihao, DAI Wujiao, YU Wenkun, LI Xin Bulletin of Surveying and Mapping    2024, 0 (8): 1-7.   DOI: 10.13474/j.cnki.11-2246.2024.0801 Abstract （233）      PDF（pc） （6633KB）（204）       Knowledge map    Save In large-scale bridge monitoring,the accuracy and reliability of GNSS positioning are severely affected by environmental factors such as obstruction from bridge cables,towers,fences,and reflections from passing vehicles.INS technology operates autonomously after initial alignment,eliminating the influence of external environmental factors.By combining GNSS and INS technologies,GNSS's resistance to interference and positioning accuracy can be significantly improved.Therefore,targeting the requirements and characteristics of bridge deformation monitoring,we have designed and implemented GNSS/INS bridge deformation monitoring software based on the open-source navigation software GINav.This software includes features such as visualization of monitoring sites,automatic matching of raw data,IMU downsampling,GNSS/INS combined solution computation,and result analysis and evaluation.Vibration table simulations of bridge vibrations show that compared to GNSS-RTK technology,the GNSS-RTK/INS combination achieves significantly improved accuracy,with a mean error reaching 1/20 of the allowable deformation value for deformation monitoring,meeting the precision requirements of bridge deformation monitoring. Reference | Related Articles | Metrics

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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 （216）      PDF（pc） （5137KB）（205）       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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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 （215）      PDF（pc） （5613KB）（199）       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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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 （206）      PDF（pc） （6568KB）（153）       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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Identification characteristics and potential analysis of geological hazards in realistic 3D scenes WANG Defu, LIU Li, LI Yongxin, ZHANG Zhiqiang, LUO Chao, LIAO Yangyang Bulletin of Surveying and Mapping    2024, 0 (8): 20-25.   DOI: 10.13474/j.cnki.11-2246.2024.0804 Abstract （200）      PDF（pc） （9379KB）（172）       Knowledge map    Save Accurate identification and analysis of geological hazards is a crucial step before prevention and early warning. Compared to 2D interpretation environments, the 3D reality of real scenes highlights more favorable data advantages due to its 3D and realistic characteristics. This article takes the real scene 3D construction as the background and adopts the 3D visual analysis method to establish a total of 12 3D identification characteristics for landslide tension cracks, shear cracks, fresh landslides, landslide walls, falling platforms, collapsed dangerous rocks, slope foot accumulation, debris flow source area, circulation area, and accumulation area; Using 3D webGIS technology to analyze the 3D characteristics of landslides and collapses, and analyzing and summarizing the potential of real-world 3D applications from geometric information, image features, micro topography, and other aspects. The results indicate that real-world 3D provides a new dimension for geological hazard interpretation, enhances the ability to identify geological hazards, and the interpretation results are more in line with reality, which helps to improve interpretation accuracy. The research results can provide inspiration for real-time 3D applications and provide reference value for high-quality identification of geological hazard hazards. Reference | Related Articles | Metrics

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Road extraction of UAV remote sensing image based on deep learning ZHANG Wei, ZHANG Chaolong, WANG Benlin, CAI Anning Bulletin of Surveying and Mapping    2024, 0 (6): 77-81.   DOI: 10.13474/j.cnki.11-2246.2024.0614 Abstract （198）      PDF（pc） （1821KB）（195）       Knowledge map    Save Aiming at the problems of high-resolution remote sensing images and road image datasets in the target scene in terms of difficulty in acquiring, high cost, etc., we explore the optimal image resolution of the network models to perform the extraction task at different scales, evaluate the applicability and reliability of each model on road extraction, and provide methodological reference and case study for the road recognition project. In this paper, three classical network models in the field of image segmentation are introduced, the models are trained using public datasets, and the unmanned aerial images of Chuzhou city, Anhui province are used as the test data to perform the road extraction work at different scales, to find out the optimal resolution and model applicability of each model in the new scene, and to evaluate the reliability. The experimental results show that the applicability of the D-LinkNet network model is more prominent in the road extraction task at different scales, the reliability of the DeepLabV3+ network model is poorer, and the optimal resolutions of the road extraction input images for the U-Net and D-LinkNet network models are 1.0 and 0.5 m, respectively. 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 （196）      PDF（pc） （5184KB）（138）       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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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 （190）      PDF（pc） （1739KB）（185）       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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Study on multisource data fusion methods and their application in comprehensive subsidence monitoring of mining area surface DU Yuzhu, LIANG Tao Bulletin of Surveying and Mapping    2024, 0 (11): 120-125.   DOI: 10.13474/j.cnki.11-2246.2024.1121 Abstract （190）      PDF（pc） （2039KB）（66）       Knowledge map    Save With the development of unmanned aerial vehicle (UAV), sensor, and data processing technologies, lightweight and low-cost UAVs can carry a variety of sensors to obtain diverse high-precision observation data. In response to the characteristics of mining-induced subsidence, this paper designs a lightweight and small-scale UAV mining area ground monitoring scheme that integrates aerial photography and LiDAR. It studies key technologies such as multi-period and multi-source data registration, selection of subsidence monitoring points, construction of surface rock movement observation lines, and proposes effective solutions. According to the research results, application tests have been carried out, and the results show that the lightweight UAV measurements using fused point clouds and imagery can obtain comprehensive mining area ground subsidence models with a precision better than 0.25 meters. Reference | Related Articles | Metrics

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3D scene reconstruction system and algorithm based on stereo vision and single-line LiDAR ZHONG Leisheng, XIA Hui, CHEN Jialin Bulletin of Surveying and Mapping    2024, 0 (5): 48-52,59.   DOI: 10.13474/j.cnki.11-2246.2024.0509 Abstract （188）      PDF（pc） （3581KB）（94）       Knowledge map    Save Stereo vision and LiDAR are two effective methods for 3D scene reconstruction, but they both have some limitations. As a result, it is meaningful to fuse visual sensor data and LiDAR data in order to conquer their weaknesses. In this paper, we address the uniqueness of the single-line spinning LiDAR device, and propose a modular visual-LiDAR SLAM algorithm based on the integration of image and range data. In the method, visual information is used to undistort the LiDAR point cloud and provide an initial pose estimation from the visual odometry (VO) module. After that, pose refinement is performed by a LiDAR SLAM (L-SLAM) module which is independent from the VO module, and then we obtain highly accurate 3D scene reconstruction results. Experiments show that our system and algorithm could increase the accuracy and adaptation of low-cost large-scale 3D scene reconstruction tasks. Reference | Related Articles | Metrics

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Inversion of soil moisture in the Yuanmou hot-dry river valley area based on the PSO_GA-RBF neural network model DU Jinming, LUO Mingliang, BAI Leichao, WU Qiusheng Bulletin of Surveying and Mapping    2024, 0 (11): 1-6.   DOI: 10.13474/j.cnki.11-2246.2024.1101 Abstract （187）      PDF（pc） （2083KB）（131）       Knowledge map    Save Soil moisture has a significant impact on hydrological and climatic processes. A comprehensive and accurate understanding of soil moisture status is of great research value for hydrological simulation, ecological governance, and other related fields. In response to the soil moisture inversion issue in the Yuanmou hot-dry river valley area, a new soil moisture inversion model is constructed using the PSO_GA-combined optimized RBF neural network. The experiment utilizes Sentinel-1 radar data and Sentinel-2 optical data, and employs the water-cloud model suitable for low vegetation cover types in the study area to correct the vegetation scattering effects. The obtained VV and VH polarized soil backscattering coefficients and cross-polarization differences are incorporated into the constructed model, enabling the remote sensing inversion of soil volumetric water content in the hot-dry river valley area of Yuanmou county, Yunnan province. Comparisons and validation against measured soil volumetric water content data show a root mean square error of 0.55% m 3/m 3 and a coefficient of determination ( R 2) of 0.855, demonstrating a significant improvement in accuracy compared to traditional RBF neural network models.Correlational analysis is conducted between the inversion results and NDVI values, revealing a coefficient of determination ( R 2) of 0.512 7 between the two. This verifies the high precision of soil volumetric water content inversion based on Sentinel-1 radar image data, utilizing the water-cloud model and PSO_GA-combined optimized RBF neural network, validating the feasibility of large-scale soil moisture monitoring in hot-dry river valley areas. Reference | Related Articles | Metrics

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Application of improved 3D-BoNet to segmentation and 3D reconstruction of point cloud instances GUO Baoyun, YAO Yukai, LI Cailin, WANG Yue, SUN Na, LU Yihui Bulletin of Surveying and Mapping    2024, 0 (6): 30-35.   DOI: 10.13474/j.cnki.11-2246.2024.0606 Abstract （181）      PDF（pc） （5942KB）（109）       Knowledge map    Save In order to better utilize point cloud data to reconstruct indoor 3D models, this paper proposes a 3D reconstruction method for indoor scenes based on 3D-BoNet-IAM algorithm. The method improves the instance segmentation accuracy of the point cloud data by improving the 3D-BoNet algorithm.For the problem of missing point cloud data, a method based on plane primitive merging optimization is proposed to fit the plane, and the new plane obtained from the fitting is used to reconstruct the building surface model. The improved effect of 3D-BoNet algorithm is verified on S3DIS and ScanNet V2 dataset, and it is proved through experiments that the algorithm of 3D-BoNet-IAM proposed in this paper improves the segmentation accuracy by 3.3% compared with the original algorithm; the modeling effect of this paper is compared with other modeling effects, and it is proved through comparisons that this paper’s modeling effect is more accurate. The method in this paper can improve the instance segmentation accuracy of indoor point cloud data, and at the same time obtain high-quality indoor 3D models. 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 （179）      PDF（pc） （2330KB）（172）       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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Progress and perspectives of urban functional region identification CHENG Penggen, QI Guangyu, ZHONG Yanfei Bulletin of Surveying and Mapping    2024, 0 (5): 90-95.   DOI: 10.13474/j.cnki.11-2246.2024.0516 Abstract （178）      PDF（pc） （2305KB）（137）       Knowledge map    Save With the rapid development of the economy and society, the urban development boundary has rapidly spread from the center to the outside. Identifying urban functional areas can provide reference basis for urban construction and planning, and it is of great significance for the rational allocation and utilization of urban space and resources. Based on the literature review of urban functional area division and identification at home and abroad, this article summarizes the research status of urban functional area identification. Firstly, various data sources used for urban functional area identification are introduced, and their advantages and disadvantages are analyzed and compared. Secondly, it summarizes four types of method for urban functional area identification, focuses on analyzing the application of deep learning methods in urban functional area identification, and conducts case analysis and comparison to illustrate the effectiveness of different data sources and methods for urban functional area identification. Finally, the problems and research trends in the field of urban functional area division and identification are pointed out. Reference | Related Articles | Metrics

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Application of drone tilt photography technology in identification and stability evaluation of high and steep slope dangerous rock bodies CHEN Fuqiang Bulletin of Surveying and Mapping    2024, 0 (10): 132-137.   DOI: 10.13474/j.cnki.11-2246.2024.1022. Abstract （178）      PDF（pc） （11904KB）（121）       Knowledge map    Save Drone tilt photography technology, with its unique advantages of high precision and multi perspective restoration of real landforms, it has been widely applied in fields such as terrain and geomorphology surveying, urban 3D modeling, engineering survey and construction, and land use planning.This study adopts a comprehensive research evaluation method of “unmanned aerial vehicle oblique photography+remote sensing comprehensive interpretation+rockfall trajectory simulation”,based on the 3D slope model of high and steep slopes on both sides of a new highway in Xizang, a detailed interpretation analysis is conducted on the dangerous rock bodies developed in the region, identifying a total of 67 dangerous rock bodies. Through stability analysis and its threat to the road below, it indicates that the dangerous rock mass poses a significant threat to the western central part of the area, the eastern part of the northern slope, and some areas at the foot of the southern slope, which can easily pose a threat to pedestrians and vehicles traveling on the highway.The research results provide important technical basis for the cleaning and protection of hazardous rock masses on site, effectively compensating for the shortcomings of on-site personnels inability to reach and difficult survey operations, and have important theoretical and practical significance. Reference | Related Articles | Metrics

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Research and application of Lutan-1 SAR satellite in survey and monitoring of catastrophic geohazards YU Zhonghai, YAN Libo, LIU Qian, LU Guangbo, LIU Rui Bulletin of Surveying and Mapping    2024, 0 (11): 97-101,176.   DOI: 10.13474/j.cnki.11-2246.2024.1117 Abstract （178）      PDF（pc） （2931KB）（118）       Knowledge map    Save Lutan-1(LT-1) is the first L-band differential interferometric SAR satellite in China. Jinan has established a satellite-based monitoring network to deepen the construction of comprehensive monitoring and early warning system for urban safety risks since 2024. The SAR satellites are designed for monthly deformation monitoring of major infrastructures such as bridges, super high-rise buildings, mines, and geological hazards. This paper conducted a surface deformation study based on LT-1 with LandSAR software for a 2800 km 2 area in the southern region the LT-1 is effective for geological hazard deformation survey and monitoring. At the same time, of Jinan. Research results showed that the obvious subsidence in some mining areas is also detected, which could provide monitoring basis for the supervision of production safety in mining areas. Reference | Related Articles | Metrics