Table of Content

25 September 2024, Volume 0 Issue 9

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Integrated the automated technical methods of 3D entities modeling for urban buildings

LIU Junwei, ZHANG Zhouping, GUO Dahai, YANG Wenxue, QU Guanchen, MA Xinrui, WANG Siyu, ZHU Qian

2024, 0(9):  1-7.  doi:10.13474/j.cnki.11-2246.2024.0901

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Urban architectural entities are one of the important elements of basic geographic entity production in the construction of 3D real scene China. For the 3D block models of buildings, the existing modeling algorithms and software based on point cloud data generally suffer from low automation, insufficient refinement, and limited scale production,et al. In this paper, we propose an automated 3D reconstruction method for urban building entities. Firstly, the point cloud of independent buildings is identified using the point cloud segmentation instance algorithm, and the bottom surface contour information of the building is automatically extracted and optimized. Then, the segmented point cloud data is managed as a compact structure, and each top surface component point set is obtained through Euclidean clustering and mapped and fitted into polygons. At the same time, the bottom surface contour is introduced as a hard constraint, and the post-processing refinement and merging of the fitted polygons. Finally, the automated 3D reconstruction software for urban building entities is developed and realized, which integrates the capabilities of large-scale modeling, editing, visualization and quality control. The above key technologies and methods have been verified in three 3D real scene construction projects in Ningbo, Nanchang and Chuxiong. The validation results show that it supports the rapid reconstruction of 3D models of building blocks above 10 000 levels, and the modeling speed is better than 16 buildings per second, which can provide strong support for the large-scale production and construction of the building entities of the 3D real scene construction.

A monitoring method for large-scale surface subsidence in mining areas using InSAR and measured data

WANG Daoshun, BO Huaizhi, SUN Jian, ZHANG Juan, CHEN Yanhong

2024, 0(9):  8-13,18.  doi:10.13474/j.cnki.11-2246.2024.0902

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Aiming at the problem that interferometric synthetic aperture radar (InSAR) technology is difficult to obtain the information of large-scale subsidence in mining area, an InSAR monitoring method for large-scale subsidence by integrating the measured data proposesed.Firstly, the time-series InSAR accumulated subsidence basin in the mining area is calculated. Then the fusion boundary is determined based on the maximum deformation monitoring gradient of InSAR. Finally, the inverse distance weighting interpolation method is used to integrate the measured data and the InSAR monitoring results to obtain the large number of subsidence basins in the mining area. Taking the 3308 working face of a mine in Shandong province as the research area verify the feasibility and accuracy of the method. The results show that, the large-scale subsisional basins in the mining area extracted by this method are continuous and complete, and the spatial location and morphological characteristics are consistent with the actual mining situation. Compared with the horizontal measurement data, the average error and root mean square error of the fusion results are 73.2 and 111.6 mm respectively, which can obtain more complete and accurate subsisional information in the mining area.

Shanghai exploration in the construction of digital spatial and temporal base

ZHAO Jingwen, XU Hui, GU Jianxiang

2024, 0(9):  14-18.  doi:10.13474/j.cnki.11-2246.2024.0903

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Under the wave of global digital transformation, countries are exploring cutting-edge models and future development forms of continuous innovation in urban intelligent operation. As the carrier system of the city, the digital spatial and temporal base empowers the digital transformation of the city through the aggregation, analysis, and application support of spatial information and digital assets. This paper clarifies the connotation and characteristics of urban spatial and temporal base. Taking the mega city of Shanghai as an example, it introduces the development process of its spatial and temporal base construction, and elaborates on the practical situation of Shanghai's spatial and temporal base construction from the perspectives of data, capability and application. Finally, the future measures and role of Shanghai's spatial and temporal base construction are discussed.

Skyline extraction and analysis using airborne LiDAR and building data

LIU Yanxia, LIU Xiao, BAI Jie, ZHENG Fengjiao, LI Xianju

2024, 0(9):  19-22,27.  doi:10.13474/j.cnki.11-2246.2024.0904

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As an important manifestation of 3D urban landscape, the extraction and quantitative evaluation analysis of skylines are of great significance for urban planning and management. This article is based on airborne laser LiDAR point cloud and building vector data to extract the skyline of some areas along the right bank of the Yangtze River in Wuhan. Three indicators, including contour shape, degree of building height change, and average number of contour turning points, are selected to quantitatively evaluate and analyze the skyline results. The results show that both extraction results are in good agreement with the actual distribution characteristics of land features and landforms. The development and construction on the right bank of the Yangtze River are relatively concentrated, and high-rise buildings have a certain scale and overall height, forming a preliminary waterfront urban skyline with urban silhouettes standing by the water. According to the consequences of skyline, it is divided into multiple block segments according to height, and compared and analyzed based on the average height and mutual difference with the relevant indicator requirements in the Wuhan City Building Management Approval Guidelines, except for a few plot segments, all others meet the requirements.

Coastal erosion monitoring before and after storm surge based on airborne LiDAR

LIU Lei, ZHANG Shanyu, LIU Weibin, CHEN Gedun, LUO Liang

2024, 0(9):  23-27.  doi:10.13474/j.cnki.11-2246.2024.0905

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The study focuses on Jinshiwan Beach in Raoping county, Guangdong province. Using airborne LiDAR measurement equipment, the study collected data on the beach and tidal flats before and after storm surges. The accuracy of the LiDAR point cloud data is assessed, and a digital surface model (DSM) is generated from the acquired point cloud data. The DSM is processed to obtain the comparative elevation difference of the coastal tidal flats, and to analyze the erosion changes of the coastal tidal flats before and after the storm surge. The results indicate that the elevation errors in the LiDAR point cloud data are 2.8 and 5.4 cm for the two measurements. Following the storm surge, the western part of the coastal tidal flats exhibit minor erosion, while other areas experience sediment deposition. This research provides reliable and accurate data support for investigating marine disasters such as storm surges and coastal erosion at the study site.

Remote sensing assessment and spatio-temporal analysis of the impact of land use changes on ecological quality based on PIE-Engine

LIU Heng, ZUO Tao, JIA Kang, CHAI Chengfu, LIU Yixuan, CHEN Hengheng

2024, 0(9):  28-31,37.  doi:10.13474/j.cnki.11-2246.2024.0906

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Aiming at the problems of ecological quality caused by land use changes in Lianxi District, this paper is based on the PIE-Engine Studio remote sensing cloud platform, using multi-temporal satellite data combined with three surveys data to deeply analyze the characteristics of the spatial and temporal evolution of land cover and ecological environment and its driving factors from 2008 to 2023 on the basis of land use/cover changes. The results show that: ①The overall ecological quality of Lianxi District has improved during this period, especially on the excellent ecological quality grade (0.8～1.0), and the area and proportion of the transformation of cropland to grassland have increased significantly, but there are still local degradation phenomena. ②The area of good grade (0.6～0.8) has decreased, which may be due to the decrease of ecological quality caused by the increase of building land in certain areas. Strengthening ecological protection and restoration through the implementation of effective land management can further improve the quality of the ecological environment.

Quality evaluation of CYGNSS wind speed products in typical tropical ocean regions

SANG Wengang, ZHANG Guowei, WANG Tianyu, LI Bin, NING Yipeng

2024, 0(9):  32-37.  doi:10.13474/j.cnki.11-2246.2024.0907

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In order to evaluate the accuracy and stability of the sea surface wind speed product of the Cyclone Global Navigation Satellite System version 3.1, the paper analyzes the different versions and levels of data products and their generation processes, spatiotemporal resolution, applicability, etc. Based on the global tropical buoy array and the measured wind speed data provided by the National Hurricane Center in the United States, focus on evaluating the quality of the new version 3.1 and the current mainstream 2.1 version of level 2 wind speed products from various aspects such as effective data volume and different precision evaluation indicators. Experimental verification was conducted in typical regions of the Atlantic, Indian Ocean, and central and eastern tropical regions of the Pacific Ocean. Experimental results showed that compared to version 2.1, the effective data volume of medium and low wind speed data in each study area increased by an average of about 40% in version 3.1, and the root mean square error with the buoy reference data decreased by more than 43%; The root mean square error between high wind speed data and NHC hurricane reference data decreased by more than 27%, and wind speed data under different sea conditions showed higher correlation with the reference data. The results of this study can provide reference for users in accurately selecting CYGNSS data products for research and application in the field of sea surface wind field detection.

An improved algorithm for detecting components of power transmission lines from aerial inspection images

LAN Guiwen, XU Zirui, REN Xinyue, ZHONG Zhan, GUO Ruidong, FAN Donglin

2024, 0(9):  38-43,49.  doi:10.13474/j.cnki.11-2246.2024.0908

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The YOLO algorithm has been widely applied to process images obtained by aerial inspection of power transmission lines. However, these UAV images often contain a large number of dense small-sized power component targets. It is difficult to detect these targets in real-time and efficiently from complex backgrounds using the YOLO algorithm alone.In this paper,we make some lightweight improvements to YOLOv8n to enhance the accuracy and speed of power component recognition. Deformable convolution sare inserted into the C2f modules of the YOLO backbone and neck,to make our method adaptively adjust the size and shape of the receptive field based on features of different scales,and obtain global and local feature representation. GSConv convolutions are integrated into the YOLO backbone,to reduce the number of model parameters and improve the detection speed. Experimental results demonstrate that our proposed method improves the recognition accuracy and speed compared to YOLOv8n, and meets the requirements of precision, lightweight, and real-time inspection of transmission line components. Specifically,the mAP50 is improved by 5.7%, the F1-score is improved by 6.0%, the number of model parameters is reduced by 7%, and the detection speed reaches 107.5 fps.

Fusion of InSAR and GWO-LSTM for deformation monitoring and prediction of high and steep mountains: a case study of a slope in the Jinsha River basin

PENG Xiang, GAN Shu, YUAN Xiping, ZHU Zhifu, LI Xuan, GONG Weizhen

2024, 0(9):  44-49.  doi:10.13474/j.cnki.11-2246.2024.0909

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Due to the fact that the downstream of the Wudongde hydropower station is located in the Jinsha River basin, the flood discharge of the hydropower station will cause changes in the water level in the downstream area and frequent geological activities near the fault zone, thereby changing the stability of the slopes around the basin. It is highly prone to landslide disasters and causing river blockage. In order to gain the understanding of the slope conditions in high and steep mountainous areas, this experiment utilizes 81 Sentinel-A images from January 2021 to October 2023, and obtains surface deformation characteristics using SBAS-InSAR technology. By combining deformation rate maps and remote sensing images, potential landslide risk areas are identified. A1, A2, and A4 are selected as typical areas and analyzed through cumulative temporal deformation. A LSTM neural network settlement prediction model is constructed, and then the GWO algorithm for hyperparameter optimization is used. The settlement data obtained from the final point selection is divided into a training set and a testing set, and compared with the accuracy of the traditional prediction model SVR. The results show that the GWO-LSTM model has high accuracy in predicting landslide deformation in complex mountainous areas. Among the 9 test points, the maximum mean absolute error is 1.080 8 mm and the maximum root mean square error is 1.194 2 mm. This paper provides a theoretical basis for landslide disaster warning and management.

Characteristics of carbon storage change in Kekeya greening project area under multi-scenario simulation

HUO Yanling, WANG Ranghui, LIU Chunwei, ZHOU Limin

2024, 0(9):  50-54,73.  doi:10.13474/j.cnki.11-2246.2024.0910

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Taking the Kekeya greening project area as the research object,the MCCA-InVEST model is coupled to analyze and study the characteristics of carbon storage under multiple land use scenarios in this paper. The results show that: ①The MCCA model has good simulation accuracy and is suitable for future land use simulation. ②In the three development scenarios in the future,except for the current development scenario in 2050,the carbon storage shows an increasing trend,and the ecological priority scenario has the largest increase. This paper is of great significance for regional ecological security and the improvement of land use efficiency under the “carbon peaking and carbon neutrality goals”.

Research on the spatio-temporal variation of vegetation carbon sink and its correlation with climate in the Qinling Mountains of Shaanxi

ZHAO Xuan, ZHANG Xiangyang, LIU Guofa, WANG Tenglong

2024, 0(9):  55-61.  doi:10.13474/j.cnki.11-2246.2024.0911

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The Qinling Mountains in Shaanxi province are an important ecological security barrier in China, studying the spatio-temporal changes in vegetation net ecosystem productivity and its correlation with climate is of great significance for explaining changes in vegetation carbon sinks.The article is based on the google earth engine cloud computing platform, using net primary productivity data, temperature data, precipitation data, and solar radiation data, and Theil-Sen Median regression analysis, coefficient of variation, partial correlation analysis are used to analyze the spatio-temporal changes in vegetation net ecosystem productivity and the correlation between climate factors in Qinling Mountains of Shaanxi province from 2000 to 2021.The results indicate that:①From 2000 to 2021,the net ecosystem productivity of vegetation in Qinling Mountains of Shaanxi province showed a spatial distribution pattern of high in the west and low in the east,gradually decreasing from the west to the east.The average annual net ecosystem productivity is 595.91 gC/(m²·a), and most of it is in carbon sink areas(NEP>0).②From 2000 to 2021, the average trend coefficient of net ecosystem productivity of vegetation in Qinling Mountains of Shaanxi province was -0.007, and the trend change showed a stable state.③From 2000 to 2021, the stability of vegetation net ecosystem productivity in Qinling Mountains of Shaanxi province showed significant heterogeneity in spatial distribution, with 92.03% of the area continuing to serve as a carbon sink.④From 2000 to 2021, 92.62% of the vegetation area in Qinling Mountains of Shaanxi province showed a positive correlation between regional net ecosystem productivity and precipitation, 54.34% of the vegetation area showed a positive correlation with solar radiation, and 96.63% of the vegetation area showed a positive correlation with temperature, indicating that temperature is the most important climate factor affecting the region. This article has positive significance for improving the carbon sink value of ecosystems and achieving China's “dual carbon” goals as soon as possible.

Urban road extraction of vehicle point cloud considering local features of neighborhood

LUO Jun, ZHANG Chunkang, LUO Qixiong

2024, 0(9):  62-66,73.  doi:10.13474/j.cnki.11-2246.2024.0912

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Aiming at the problem of over-segmentation of point cloud in the process of urban road extraction caused by region growing algorithm, an improved region growing algorithm is proposed to extract ground point cloud by combining the spatial neighborhood feature information of point cloud. Firstly, data preprocessing is carried out to remove outliers far from the urban environment. Secondly, a two-dimensional spatial virtual grid is established to make rational use of the spatial locality of point cloud and reduce the scale of operation. Then, the urban road point cloud is clustered by the growth range of the mean curvature constraint region growth and the angle constraint of the fitting plane. Finally, two urban road point clouds are used for experiments, and compared with the existing region growing algorithm. The experimental results show that the proposed method can well balance the integrity and accuracy of extraction, and is practical in complex urban road point cloud extraction and urban road modeling.

Remote sensing image change detection based on slow feature analysis guided multi-level attention autoencoder

LIU Jinling, CHEN Hongzhen, LI Chenzheng, NIE Hongbin, LI Ligang

2024, 0(9):  67-73.  doi:10.13474/j.cnki.11-2246.2024.0913

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Remote sensing image change detection is an important way to identify and quantify surface changes, and is one of the main applications of remote sensing technology. However, in remote sensing images obtained under different imaging conditions such as lighting and seasons, the same object may exhibit different appearances, making it difficult for change detection algorithms to accurately distinguish real ground changes. A remote sensing image change detection method based on slow feature analysis guidance multi-level attention autoencoder (SFAMAA) is proposed to address this issue. Firstly, a U-shaped convolutional autoencoder is designed and a multi-level channel attention mechanism is introduced to expand the network's receptive field while focusing on important channel information, enhancing the network's perception of global and changing information; In addition, a slow feature analysis loss function is designed to guide network training, enabling the network to effectively suppress pseudo changes caused by differences in imaging conditions. Experimental verification is conducted on the public dataset SZTAKI, and the results show that the proposed method can effectively suppress noise and pseudo changes, and has high accuracy and good robustness for remote sensing images obtained under different imaging conditions such as lighting and seasons.

Landslide susceptibility assessment considering multi-method integrated feature selection and negative sample optimization

LIU Yiming, XU Shenghua, LIU Chunyang, MA Yu

2024, 0(9):  74-79.  doi:10.13474/j.cnki.11-2246.2024.0914

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In view of the problem that the selection of characteristic factors in landslide susceptibility evaluation is highly subjective and the selection of landslide negative samples is highly random, resulting in low prediction accuracy, this paper proposes a landslide susceptibility evaluation method that uses multi-method integration to select characteristic factors and combines the information volume method to optimize the extraction of negative samples. Taking Bazhong city, Sichuan province as an example, the results of feature selection by five methods, namely maximum relevance minimum redundancy (mRMR), gradient boosting trees (GBT), extreme gradient boosting (XGBoost), ordinary least squares (OLS), and information gain (IG) are first normalized and accumulated to obtain a comprehensive score. Secondly, negative samples are selected by the information volume method to construct a sample data set. Then, the support vector machine (SVM) model is used to analyze landslide susceptibility, and a comparative experiment is conducted with the logistic regression (LR) model. Finally, the accuracy of the prediction results is verified from three aspects: landslide susceptibility zoning map, point density statistics, and ROC curve. The experimental results show that the multi-method integrated feature selection proposed in this paper and the application of information volume method for negative sample optimization can effectively improve the prediction accuracy of the model, and the susceptibility evaluation results are more accurate and reliable.

Automatic identification and monitoring of deformation areas in the Pearl River Delta based on time-series InSAR

WU Peihong, ZHONG Shaozhong, LUO Shuran, XIE Rongan

2024, 0(9):  80-86,95.  doi:10.13474/j.cnki.11-2246.2024.0915

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The interferometric synthetic aperture radar (InSAR) technology has been widely applied in surface deformation monitoring. However, when the monitoring range is wide and there are many surface deformation areas, visual interpretation of deformation areas requires a large amount of work and is prone to misjudgment and omission. Based on this, this article proposes an InSAR deformation area automatic recognition algorithm for surface deformation area recognition and monitoring. In this paper, the the Pearl River Delta, which has many geological hazards, is taken as the research area. Firstly, InSAR technology is used to obtain the surface deformation of this area from June 2015 to November 2020. Then, using the proposed algorithm, the deformation area is automatically delineated, and a total of 630 suspected geological hazard points are identified. Finally, based on field investigations and historical optical images, the spatio-temporal development characteristics and causes of deformation in some key monitoring areas are analyzed.The application of the research results in this article to the identification and monitoring of ground subsidence in the Pearl River Delta has a good demonstration effect, which can greatly reduce the intensity of interpretation work, improve interpretation accuracy and efficiency, and provide technical support for large-scale InSAR surface deformation monitoring, analysis of deformation characteristics in the Pearl River Delta region, and risk prevention and control of ground subsidence geological hazards.

Visual/inertial/ultra-wideband dataset based on unmanned platform in complex scenes

LUO Haolong, YANG Zidi, LI Xueqiang, ZOU Danping, LI Jiansheng, LI Guangyun

2024, 0(9):  87-95.  doi:10.13474/j.cnki.11-2246.2024.0916

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The navigation and SLAM technologies based on multi-sensor fusion are currently the mainstream direction of development, and their research and applications in complex scenes have increasingly attracted widespread attention. Nevertheless, there exists a relative dearth of multi-sensor datasets specifically designed for complex scenes, particularly those that incorporate ultra-wide band (UWB) sensors. To facilitate users to test and verify multi-source sensor fusion algorithms in complex scenes and explore the shortcomings and potential development directions of multi-source sensor fusion and SLAM technology,both unmanned vehicles and drones are utilized to gather visual, inertial, and UWB data in seven diverse scenes, including dynamic scenes, non-line-of-sight scenes, and large-scale scenes. Furthermore, a high-precision optical motion capture system is employed to provide real-time six-degree-of-freedom true positions and orientations for the dataset. Finally, four state-of-the-art open-source algorithms, namely VINS-MONO, VINS-FUSION, VIR-SLAM and ORB-SLAM3, are utilized to conduct experimental verification and analysis on all scene sequences. The experimental results demonstrate that the data from all scene sequences are effective and usable.

Constructing a short term heavy rainfall model based on BeiDou/GNSS PWV data and its application

DENG Nanshan, LIU Yang, LI Xiaowei, SI Xiaohua, LEI Lei

2024, 0(9):  96-100.  doi:10.13474/j.cnki.11-2246.2024.0917

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The traditional rainfall warning model is only limited to warning whether rainfall events have occurred, ignoring the warning of short-term and imminent heavy rainfall events. Based on the significant increase in precipitable water vapor (PWV) before rainfall occurs, this paper proposes a short-term and imminent heavy rainfall warning model based on BeiDou/GNSS PWV. The model includes three predictive factors: PWV value, PWV change amount and PWV change rate, and introduces the percentile method to determine the optimal threshold for key parameters of the predictive factors. Selecting five GNSS stations in Yichang city, Hubei province for hourly PWV and rainfall data validation in 2022, the statistical results show that the proposed short-term and imminent heavy rainfall warning model can predict 94% of heavy rain events in the next 2～6 hours, with a false alarm rate of only 32.84%.

Research and application of improved particle swarm optimization algorithm in fault sliding parameter inversion

LIU Jie, WANG Hongyu, WU Yanping

2024, 0(9):  101-105.  doi:10.13474/j.cnki.11-2246.2024.0918

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One of the main research issues in geodesy is to use ground geodetic data to invert dynamic parameters such as fault sliding rate.We propose an improved particle swarm optimization algorithm to compensate for the shortcomings of the standard particle swarm algorithm, which may have local optimal solutions, and conduct experimental verification through simulated data.Later, taking the two main faults in the Weihe Basin as research objects, the three-dimensional sliding rates of the northern Qinling Fault and the Lintong Chang'an Fault are inverted using ground GPS observation data, and the running time of the two algorithms is analyzed.The results show that the improved particle swarm optimization algorithm reduces the time consumption compared to the standard particle swarm optimization algorithm, and converges faster.The fault parameters obtained by the algorithm proposed in this article are more in line with real fault conditions and have certain practical application value.

Monitoring and analysis of coal mining subsidence in large-scale complex mountainous areas based on utilizing ascending and descending track InSAR

HUANG Guangcai, DONG Jihong, ZHAO Zilong, XI Wenfei, GUO Junqi, AN Quan, ZHU Yuhua, WEI Jin

2024, 0(9):  106-111,122.  doi:10.13474/j.cnki.11-2246.2024.0919

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Large-scale coal mining in complex mountainous areas frequently causes ground subsidence and geological disasters. Effective monitoring using single-track data is challenging. Thus, it requires the combined use of ascending and descending track data. This study employs the SBAS technique, using Sentinel-1 data, to gather surface deformation data in Panzhou city between January 2019 and May 2022. The findings show severe subsidence in coal mining areas, with maximum levels of -385 mm and rates reaching -127 mm/a. Surface deformation in mined mountains follows a linear decline, independent of rainfall. In contrast, closed mines show periodic deformation tied to rainfall patterns intensifying in the rainy season and shifting between creeping and lifting in the dry season. Using both ascending and descending track data reduces misidentification in side-looking radar images, enhancing subsidence detection in karst mountainous mining areas. The results offer a scientific foundation for safe mining practices, post-closure restoration, and disaster prevention in karst mountainous regions.

Research on the temporal change and development of the secondary industry based on multi-source data

YUAN Debao, WU Yuyang, GUO Wei, PAN Xing

2024, 0(9):  112-116.  doi:10.13474/j.cnki.11-2246.2024.0920

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In response to the challenge of inadequately explaining the spatial layout of the secondary industry using nighttime light data, this paper proposes a novel method suitable for spatializing the added value of the secondary industry. The approach involves combining selected points of interest (POI) data with land surface temperature data to construct the secondary industry surface temperature-POI index (STPI Index). This index is then coupled with nighttime light data from rural residential areas, and the study is conducted in the core urban cluster of the Huaihai economic zone. Results show that, compared to methods coupling land use data with nighttime light remote sensing data, the proposed spatialization model for the secondary industry consistently demonstrates superior goodness of fit in the years 2014, 2016, 2018, and 2020 (0.926, 0.882, 0.907, 0.896, respectively) compared to the former (0.859, 0.805, 0.880, 0.849, respectively). The average relative error each year is lower than the former, maintaining around 10%. Using Xuzhou city as an example, a local comparison of the spatialized results for the added value of the secondary industry reveals that the proposed method significantly enhances modeling accuracy and spatialization effectiveness. The spatial distribution pattern is more aligned with reality. The results of this study can provide valuable references for relevant departments in formulating regional economic development plans.

Underground space LiDAR point cloud segmentation based on multi-dimensional feature GS-SVM

WEI Mengsha, GONG Yun, ZHANG Xiaoyu, LIU Tengfei

2024, 0(9):  117-122.  doi:10.13474/j.cnki.11-2246.2024.0921

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Aiming at the problem that point cloud overfitting in point cloud segmentation in low light and low feature point environment,this paper proposes a GS-SVM point cloud segmentation algorithm that based on polynomial kernel least squares support vector machine and grid search. It's extracted to classify multi-dimensional features such as vehicles,lane lines and garage obstacle pillars. The multi-scale accuracy evaluation index is used to verify the effectiveness of feature selection and evaluate the segmentation algorithm. The results show that compared with the traditional classification algorithm in the literature,the column recognition rate is 80% and the vehicle recognition rate is 65%. The classification algorithm based on polynomial kernel function has a recognition rate of 75% and 73% for columns and vehicles,respectively,which is increased by 5% and 8%. When using the other two kernel function,GS-SVM also maintains the advantages,comparing with the conventional algorithm,the proposed algorithm has strong robustness,which provides a solution to the problem of point cloud segmentation in weak light and geographical feature points environment. It also enriches the use scene of LiDAR 3D scanning.

On the coupling and coordination relationship between water resources and other natural resources in the Yellow River basin (Gansu section)

HU Xiaojuan, LI Xia, WEI Wei, YAGN Shilong

2024, 0(9):  123-128,144.  doi:10.13474/j.cnki.11-2246.2024.0922

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This article is based on panel data from 57 county-level administrative regions in the Yellow River basin (Gansu section) in 2009, 2012, 2015, 2018 and 2021. The coupling coordination degree model is used to calculate and analyze the coupling coordination relationship and spatio-temporal evolution law between water resources and farmland, gardens, forests, grasslands and wetlands. The results indicate:①During the research period, the coupling and coordination relationship between water resources in the Yellow River basin (Gansu section) and cultivated land, gardens, forests, grasslands, and wetlands showed different development patterns and spatial differentiation characteristics. Overall, the coupling coordination degree between water resources and grasslands, cultivated land, and gardens is relatively high, while the coupling coordination degree with forests and wetlands is relatively low. ②The overall trend of coupling and coordinated development between water resources and cultivated garden grass moisture shows a distribution pattern of low in the northeast and high in the southwest in space, with no significant fluctuations in time. ③There was no obvious trend of high coupling high coordination evolution during the research period, indicating that the spatial and quantitative distribution of various natural resources in the Yellow River basin (Gansu section) is uneven. In the future, attention should be paid to designing differentiated natural resource coordination development paths, guiding various natural resources to develop in a more orderly and coordinated direction.

Geographic named entity recognition based on multi-dimensional feature learning and model fusion

MA Haoran, WANG Jinhua

2024, 0(9):  129-134.  doi:10.13474/j.cnki.11-2246.2024.0923

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Geographical named entity recognition is the core task of geographic information extraction, which supports the construction of geographic information systems. However, current research on geographic named entity recognition faces two core challenges: Firstly, the scarcity of annotated data in geographic texts makes it difficult for traditional generic models that rely heavily on annotated data to fully capture and recognize all potential named entities in geographic texts.Secondly, the label density of geographic data is relatively sparse, and models often can not distinguish the boundaries of different geographic named entities, thus unable to accurately locate them. In response to the above issues, this study proposes a named entity recognition algorithm AM-NER for geographic text features. Firstly, using Albert for word vector training, this model is a lightweight pre training model for small samples, which can comprehensively learn semantic information in the geographic field.Secondly, a neuron structure named MNER is designed, which is based on the idea of model fusion and utilizes multiple models to learn semantic features from different dimensions, accurately identifying the boundaries of named entities. Compared to previous studies, AM-NER has improved various indicators in the geographic dataset by 2.05%～2.67%.

Pnts tile algorithm for full attribute conversion of LiDAR point cloud to Cesium

CHANG Jiang, LIAN Xugang

2024, 0(9):  135-139.  doi:10.13474/j.cnki.11-2246.2024.0924

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As one of the 3D front-end visualization frameworks with excellent performance, Cesium has a corresponding pnts tile format for displaying point cloud data.With the continuous expansion of LiDAR point cloud acquisition methods and application requirements, there is a huge demand for a more complete display of LiDAR point clouds at the front-end. The industry relies heavily on the data conversion method from LiDAR point clouds to pnts tiles. The conversion of simple attributes such as point cloud coordinates and colors limits the direct application of point cloud algorithms involving LiDAR point cloud scanning angle attributes in the front-end. This paper focuses on the preservation of the scanning angle attributes of LiDAR point cloud in pnts tiles, proposes solutions and completes the conversion algorithm research, obtains pnts tiles mounted with LiDAR point cloud scanning angle attributes, and makes corresponding adaptations to the algorithm of important attributes affecting its loading performance and the strategy of LOD organization and scheduling. The final pnts tile meets the official loading requirements of Cesium, and can realize the direct application of the point cloud algorithm in the front-end that requires the participation of scanning angle attributes.

Research and practice of UAV network monitoring for cultivated land change

WEI Zhongyang, HUANG Jingjin, GUO Weili, FENG Yijun, LI Zhenghong, WEI Qiulian, PAN Yifeng, XIE Hong

2024, 0(9):  140-144.  doi:10.13474/j.cnki.11-2246.2024.0925

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This paper analyzes the advantages of UAV remote sensing compared with traditional ground inspection, satellite remote sensing and ground-based video monitoring in the monitoring of cultivated land change. Aiming at the problems such as limited remote sensing capability of UAV, long processing time of image and insufficient automatic image information extraction ability, this paper presents the technology architecture and key technologies of UAV network monitoring for cultivated land change based on UAV network collaborative aerial photography, parallel image processing, image intelligent interpretation classification and other technologies. Finally, taking the construction of Guangxi UAV linkage service platform, UAV image AI interpretation system and the application in cultivated land change monitoring as examples, the construction achievements of UAV network monitoring technology architecture are illustrated.

Deformation analysis of tunnel section after earthquake based on 3D scanning

ZHANG Shaohua

2024, 0(9):  145-150.  doi:10.13474/j.cnki.11-2246.2024.0926

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3D laser scanning technology has been widely used in tunnel deformation detection, but there are some problems such as large amount of point cloud data, low efficiency in section extraction, and poor universality of different types of tunnel deformation detection algorithms. This article proposes a fast search algorithm for cutting local range point clouds of tunnel cross-sections,an octree index is established for point cloud data, and in order to reduce the complexity of the algorithm and simplify the search process, the query point coordinates, cutting thickness, and offset are given to define the range plane polygon of the search area, and it performs intersection detection with the node polygon of octree index to quickly locate and extract tunnel cross-sections.Divide the contour line of the tunnel design section equally and complete the numerical calculation of deformation of any shaped tunnel based on laser point cloud measurement. Finally, taking the Daliang tunnel of the Lanxin Passenger Dedicated Line affected by the earthquake as an example, the setting accuracy of scanning station is statistically analyzed,the extraction of tunnel sections and deformation detection analysis are completed, and the offset patterns of different mileage intervals are provided, providing data support for post earthquake repair design.

Image quality evaluation of Lutan-1 based on point target

ZHANG Yifan, XU Hang, FAN Wenfeng, ZHAI Haoran, LIANG Xueying

2024, 0(9):  151-155.  doi:10.13474/j.cnki.11-2246.2024.0927

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In order to measure the imaging quality of Lutan-1 satellites in the phase of orbital detection, objective point target-based evaluation indicators are used to evaluate the satellites, including spatial resolution, peak side lobe ratio and integrated side lobe ratio. The images of satellites from March to August 2023 are selected to evaluate the image quality based on artificially arranged corner reflectors. The analysis results show that both LT-1 A and LT-1 B can meet the requirements of satellite imaging indicators in orbital detection, the spatial resolution in the range direction is better than in the azimuth direction, the peak side lobe ratio and integrated side lobe ratio in the azimuth direction is better than the range direction. In the imaging quality, LT-1 A is better than LT-1 B. In the stability, the indicators of satellites in the range direction are more stable. The imaging quality of LT-1 data can well support subsequent related applications of the satellites.

Noise data visualization based on LOD1 city model

REN Ping, CHEN Xueye, JIANG Yi, XIAO Haibo, LIANG Changde, HE Biao, LI Sheng

2024, 0(9):  156-160.  doi:10.13474/j.cnki.11-2246.2024.0928

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In recent years, noise pollution has become increasingly serious and has become the main content of ecological and environmental complaints. In order to more intuitively view and analyze noise pollution, improve noise control and management capabilities, this article is based on urban information modeling (CIM) and noise monitoring data, combined with data superposition and 3D rendering technology, to study large-scale 3D noise map technology, and achieve 3D visualization by integrating oblique photography and white film noise data. The aim is to optimize decision-making in noise pollution supervision and control through the noise visualization analysis of this technology, add momentum to the construction of smart cities.

Data analysis and problem response of the map examination service from 2015 to 2023

LIU Jie, WANG Jinyue, ZOU Huidong, DEMG Guochen, CHEN Huixian, HUANG Long, YANG Mengmeng

2024, 0(9):  161-165.  doi:10.13474/j.cnki.11-2246.2024.0929

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This article focuses on how to further improve the approval rate of map examination, and conducts statistical analysis and research on the number of accepted maps, map types, and map approval rates from 2015 to 2023,The results show that in recent years, both the number of accepted maps and the pass rate have significantly increased. However, there are still 10%～20% of maps that do not meet the requirements of map examination each year.These issues mainly focus on the two aspects of map submission and map content technical examination, which restrict the further improvement of map approval rate. This article explores how to effectively reduce the incidence of wrong maps and improve the approval rate of maps by avoiding common problems in the map acceptance process, mastering key error prone points in maps, and standardizing the use of map reference materials. These suggestions and measures can provide reference for the submitting units and map users, also improve the quality of maps.

Cultivating innovative practical abilities in ocean surveying and mapping based on “laboratory+experimental field+marine surveying site”

LIU Shangguo, YANG Fanlin, ZHANG Kai, LU Xiushan, ZHOU Xinghua

2024, 0(9):  166-170.  doi:10.13474/j.cnki.11-2246.2024.0930

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With the implementation of the maritime power strategy of China, the connotation of the marine surveying and mapping discipline has undergone profound changes. The new engineering characteristics of the discipline have been significantly enhanced, which raises higher requirements for the practical ability cultivation of the discipline. In order to solve the problem of lack of practical and scientific innovation ability of marine surveying and mapping students, which does not meet the needs of production and scientific research, based on the construction of a three-step progressive training process of laboratory+experimental field+marine survey site, we advocate practice docking practical combat, scientific and technological innovation facing the forefront, strengthen integration of production and education, science and education integration, and carry out “undergraduate leading+graduate counselling+teacher guiding+production supervising” four guide task driven practical teaching, practice a new model of scientific and technological innovation and education through the tripartite collaboration of “school+institute+enterprise” and the four level linkage of “teacher+doctor+master+undergraduate”, effectively improving the practical innovation ability of marine surveying and mapping students.

Refraction correction of tunnel structure monitoring data based on path meteorological information

BAI Wenfeng, MIN Xing, LUO Haitao

2024, 0(9):  171-174.  doi:10.13474/j.cnki.11-2246.2024.0931

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The inconsistencies and changes of meteorological conditions in tunnel will adversely affect the monitoring accuracy of tunnel structural deformation based on intelligent total station. Combined with the linear terrain characteristics of tunnel, through the uniform deployment of several meteorological sensors along the monitoring area to collect real-time temperature, pressure and other information, and fitting to obtain the functional relationship with distance. Then obtain the average meteorological information of propagation path of electromagnetic wave to each monitoring section for correction of the corresponding observed values. The experimental results of utilizing a tunnel environment show that the correction of observation values by average meteorological information is helpful to reduce the representative error of meteorological correction and improve the reliability of tunnel structural deformation monitoring by intelligent total station.

County level real-scene 3D production technology based on multi-equipment coordination

CHEN Juping, CHEN Yiping, DING Jianxun, ZHANG Pengfei, DONG Fengbin

2024, 0(9):  175-181.  doi:10.13474/j.cnki.11-2246.2024.0932

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To address the limitations of single-platform and single-sensor approaches in capturing accurate, comprehensive and efficient 3D reality data in complex environments, this study introduces key technologies for multi-platform collaborative data acquisition, processing, and quality control. The proposed method is validated in the Zhuhai Jinwan District project, demonstrating its effectiveness in overcoming technical challenges, ensuring high-quality data models, diverse outputs, and controlled quality, offering significant potential for widespread application.