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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 （318）      PDF（pc） （1871KB）（280）       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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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 （262）      PDF（pc） （2039KB）（74）       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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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 （241）      PDF（pc） （6633KB）（219）       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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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 （216）      PDF（pc） （1821KB）（205）       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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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 （213）      PDF（pc） （9379KB）（179）       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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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 （202）      PDF（pc） （2931KB）（137）       Knowledge map    Save Lutan-1(LT-1) is the first L-band differential interferometric SAR satellite in China. Jinan has established a satellite-based monitoring network to deepen the construction of comprehensive monitoring and early warning system for urban safety risks since 2024. The SAR satellites are designed for monthly deformation monitoring of major infrastructures such as bridges, super high-rise buildings, mines, and geological hazards. This paper conducted a surface deformation study based on LT-1 with LandSAR software for a 2800 km 2 area in the southern region the LT-1 is effective for geological hazard deformation survey and monitoring. At the same time, of Jinan. Research results showed that the obvious subsidence in some mining areas is also detected, which could provide monitoring basis for the supervision of production safety in mining areas. Reference | Related Articles | Metrics

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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 （201）      PDF（pc） （2083KB）（135）       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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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 （200）      PDF（pc） （3581KB）（108）       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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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 （199）      PDF（pc） （2305KB）（141）       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 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 （196）      PDF（pc） （5942KB）（114）       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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Optimization and application of deep learning model-based subway tunnel defect detection YOU Xiangjun, ZHAO Xia, LONG Sichun, WANG Jiawei, ZHENG Ying, KUANG Lijun Bulletin of Surveying and Mapping    2024, 0 (8): 96-101.   DOI: 10.13474/j.cnki.11-2246.2024.0817 Abstract （192）      PDF（pc） （3414KB）（125）       Knowledge map    Save Aiming at the four common defects of subway tunnel, such as leakage, crack, structural plaster cracking and spalling, a defect detection method of subway tunnel based on laser radar scanning point cloud data and deep learning is studied.Firstly,the ACmix attention module is introduced into the YOLOv8 model to make the network take into account both global and local features, and improve the detection effect of small targets such as cracks and cracks.Then,the regression loss function is optimized, the convergence stability and regression accuracy are improved, and the detection error is reduced. Finally,the complete process of orthographic projection image preprocessing, batch detection and result fusion, and report generation of detection results is realized, and the defect detection of large-scale orthographic projection is efficiently realized. The experimental results show that under the condition that the IoU threshold is 0.5, the mAP of the improved YOLOv8 algorithm on the tunnel defect test set increases from 90.65% to 91.18%, and the AP of cracks increases from 77.89% to 78.70%. The intelligent detection of four common defects of subway tunnel based on LiDAR scanning is solved, and has been successfully applied in actual tunnel operation and maintenance engineering. Reference | Related Articles | Metrics

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Research on groundwater storage and surface subsidence in Huangshui Valley based on GRACE and Sentinel-1A HU Xiangxiang, KE Fuyang, SHI Yaya, WU Tao, LIU Baokang, PANG Dongdong, ZHANG Lili, SONG Bao Bulletin of Surveying and Mapping    2024, 0 (6): 46-52.   DOI: 10.13474/j.cnki.11-2246.2024.0609 Abstract （189）      PDF（pc） （8288KB）（164）       Knowledge map    Save GRACE/GRACE-FO and GLDAS data are used to invert the groundwater changes in 2019—2022 in Huangshuang Valley area. And SBAS-InSAR technology is used to obtain the simultaneous rate of surface subsidence in the region, which is combined with the precipitation data to study the correlation between surface subsidence and groundwater changes in Huangshuang Valley area. The results show that: ① The overall direction of groundwater loss in Huangshui Valley is from northwest to southeast. ②Groundwater changes have a greater impact on the more serious surface deformation in the region. ③The greater the surface deformation (uplift), the more groundwater reserves are lost. The upper reaches of the Yellow River have the greatest uplift, and the loss of groundwater reserves is the greatest. ④ The surface deformation in the northern part of the Huanghe Valley is not sensitive to changes in groundwater reserves, while the surface deformation in the southern part is more sensitive to changes in groundwater reserves. The conclusions of this paper can provide important scientific reference for local geological disaster warning, sustainable utilization of water resources, ecological protection and high-quality green development. 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 （189）      PDF（pc） （11904KB）（135）       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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Application of topographic survey of island (reef) based on multi-beam sounding system SUN Dong, DING Shijun, LI Xiaohong, LIU Yuan, LIU Haibin Bulletin of Surveying and Mapping    2024, 0 (11): 90-96.   DOI: 10.13474/j.cnki.11-2246.2024.1116 Abstract （181）      PDF（pc） （2142KB）（77）       Knowledge map    Save With the deepening of marine comprehensive survey, islands (reefs) as an important element of the ocean, its complete surface and underwater terrain data is the basis of understanding and planning islands and reefs. The multi-beam sounding system combined with 3D laser is used to obtain the integrated topographic data of islands and reefs above and below water. The experiment is carried out in combination with a typical island in eastern Shandong province, focusing on the application of the multi-beam sounding system in the topographic data acquisition of islands and reefs, the accuracy assessment is carried out, and the data results are displayed. The results of the study area show that the ship-borne measurement system combined with the unmanned ship measurement system can obtain the land and water interface area of the reef completely at one time by using the high-low tidal range, and the data is complete and high precision. The full coverage data results can truly reflect the topography of islands and reefs and their surrounding areas, which provide the data support for planning and construction and ecological monitoring. Reference | Related Articles | Metrics

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Farmland soil moisture monitoring based on UAV multispectral imagery ZHAO Guiping, XU Fajun Bulletin of Surveying and Mapping    2024, 0 (11): 177-182.   DOI: 10.13474/j.cnki.11-2246.2024.1131 Abstract （179）      PDF（pc） （1953KB）（114）       Knowledge map    Save Using drones to monitor soil moisture is low-cost, convenient, fast and accurate, and has important practical significance for intelligent management of farmland areas. This study selected Liangfeng Farm as the research area, where a drone equipped with a multispectral camera is used to monitor soil moisture. Through gray correlation screening, soil moisture sensitive spectral data are selected, and regression analysis was performed with the measured soil moisture data to construct a soil moisture inversion model based on UAV multispectral remote sensing. Through comparative analysis of the results of the NIR-RE-G model and the B-R-G-RE-NIR model, it is found that the determination coefficient R 2 is both greater than 0.77. The B-R-G-RE-NIR model is better than the NIR-RE-G model in terms of accuracy evaluation results of R 2 and RMSE, so the overall inversion results of both models have higher accuracy. Therefore, this study verified the effectiveness and feasibility of the NIR-RE-G model and the B-R-G-RE-NIR model in soil moisture monitoring in this region, which provides an effective method and reliable reference for rapid monitoring of soil moisture in large-scale farmland. Reference | Related Articles | Metrics

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Application of hyperspectral remote sensing technology in the inversion cultivated land quality WANG Chenzhe, LIU Zhaoxian, FU Lizhao Bulletin of Surveying and Mapping    2024, 0 (5): 127-132.   DOI: 10.13474/j.cnki.11-2246.2024.0522 Abstract （176）      PDF（pc） （4552KB）（92）       Knowledge map    Save This article estimates the related indicators of farmland quality based on hyperspectral image. The study area is in the northeastern plain of Shijiazhuang city,Hebei province. Based on the preprocessed ZY-1 02D hyperspectral remote sensing image,which underwent radiometric and geometric correction,a total of 110 surface soil samples within the image coverage were collected and subjected to physical and chemical analysis to obtain data on the content of farmland quality-related indicators. The inversion models for farmland quality-related indicators are established using the Hapke model,CARS method,and SVM model. Validation with 70% of the modeling samples and 30% of the prediction samples showed relatively high accuracy for hydrolyzable nitrogen and organic matter,confirming the application value of hyperspectral remote sensing technology for the inversion of soil organic matter and nutrient indicators. Reference | Related Articles | Metrics

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Spatial characteristics analysis of urban thermal diurnal environment based on ECOSTRESS PENG Min, YAO Na, SUN Peilei, ZOU Bowen, WANG Wenshuo Bulletin of Surveying and Mapping    2024, 0 (6): 151-156,181.   DOI: 10.13474/j.cnki.11-2246.2024.0626 Abstract （176）      PDF（pc） （5615KB）（74）       Knowledge map    Save Land surface temperature (LST) is an important index to characterize the change of urban thermal environment, and its distribution information is of great significance for monitoring urban thermal environment. Based on ECOSTRESS data from June to August from 2018 to 2023, diurnal LST in the fourth ring road of Shenyang is obtained through the correction of LST. Mean-standard deviation method and spatial autocorrelation analysis are used to extract diurnal spatial characteristics of LST, and combined with land use data, the contribution degree of different land types to the spatial distribution of land surface temperature is analyzed. As indicated by the results, the LST in the fourth ring road of Shenyang is high in the north and low in the south, and high in the west and low in the east. There is a large difference in LST between day and night. The high temperature area is mainly concentrated in Huanggu district, Dadong district, western Shenhe district and eastern Tiexi district, while the low temperature area and the middle temperature area are mainly concentrated at the edge of the fourth ring road, the artificial LST is mostly higher than the natural LST, and the high temperature area of building land category accounted for the largest proportion, which is the main factor for the warming of LST, while the natural LST is the main factor for the cooling of LST. There are significant clustering and hot spots in the fourth ring road of Shenyang, and the diurnal variation of LST is consistent with the aggregation distribution characteristics of LST. Reference | Related Articles | Metrics

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Color restoration of underwater images using color compensation and convolutional neural network based defogging model MA Zhenling, CHEN Yuan, FAN Chengcheng, PAN Yan Bulletin of Surveying and Mapping    2024, 0 (11): 68-73.   DOI: 10.13474/j.cnki.11-2246.2024.1112 Abstract （176）      PDF（pc） （1834KB）（88）       Knowledge map    Save Underwater vision measurement has important applications in marine surveying, underwater engineering surveying, underwater archaeology and underwater environmental monitoring. However, underwater images suffer from color distortion, image blurring and low contrast, which limits the application of underwater visual measurement technology in practical environments. A color restoration method for underwater images based on color compensation and convolutional neural network (CNN) defogging model is proposed in this paper, in which the image enhancement is carried out step-by-step.Firstly,the color deviation of underwater images is analyzed, and then an adaptive color compensation strategy combined with the grayscale world white balance algorithm is used to correct underwater image color. Secondly, a CNN based dehazing model was designed to achieve dehazing processing of underwater images. Finally, the adaptive histogram equalization CLAHE method is used to enhance the contrast of underwater images. In order to prove the applicability and superiority of the proposed method, two image datasets are combined to study, and several known underwater image enhancement and restoration methods are compared. The proposed method and several compared methods are evaluated in two aspects of subjective visual effect and quantitative evaluation index. The comparison results show that compared with other enhancement algorithms, the proposed method successfully improves the clarity of the image and reduces the color deviation of the damaged underwater image when processing underwater images in various environments and has superior image color recovery compared with existing enhancement methods. Reference | Related Articles | Metrics

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A distributed storage and index method of trajectory big data based on the Hilbert curve CHEN Kai, SONG Weiwei, JIN Baoxuan, LI Yongning, PU Hongxun Bulletin of Surveying and Mapping    2024, 0 (6): 109-114,138.   DOI: 10.13474/j.cnki.11-2246.2024.0619 Abstract （175）      PDF（pc） （1566KB）（111）       Knowledge map    Save In response to the rapid growth of trajectory big data with spatio-temporal characteristics and the urgent need for its fast query, traditional relational databases have certain limitations on the storage of massive trajectory data and specific query requirements, while non-relational databases are difficult to meet the efficient indexing requirements of massive data, and the efficiency of the storage and indexing of trajectory data is still in urgent need of improvement. In this paper, a framework for storage and retrieval based on HBase database is designed and implemented to cope with the efficient management of spatio-temporal trajectory data. Firstly, a novel Rowkey structure is designed, and the GeoMesa-HBase underlying storage model is constructed by combining spatio-temporal indexing tools. Secondly, a Hilbert curve-based coding technique is integrated to construct the spatial index, which improves the storage and retrieval efficiency of trajectory data. In order to evaluate the effectiveness of the proposed method, this paper compares its storage and query performance with traditional storage databases (HBase and MySQL) and Geohash index. The experimental results show that the scheme is able to achieve effective storage of trajectory data and improve the retrieval efficiency, which is of great practical significance in addressing the challenges associated with trajectory big data management. Reference | Related Articles | Metrics

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LSTM goaf surface subsidence prediction method combining convolutional neural network and attention mechanism GAO Motong, YANG Weifang, LIU Zuyu, CAO Xiaoshuang, ZHANG Ruiqi, HOU Yuhao Bulletin of Surveying and Mapping    2024, 0 (6): 53-58,170.   DOI: 10.13474/j.cnki.11-2246.2024.0610 Abstract （174）      PDF（pc） （2428KB）（151）       Knowledge map    Save In order to solve the problem of difficult extraction of spatial features of monitoring points in the time series prediction of surface collapse areas in the mining zone, a CNN-Attention-LSTM combined neural network model that can extract key spatial features of monitoring points is proposed. Firstly, the number of neighbouring monitoring points as feature input is increased, and the spatial features of the multidimensional time series composed of multiple monitoring points are extracted using convolutional neural network (CNN). Secondly, the extracted multidimensional feature time series are input into the multilayer perceptron (MLP) to calculate the attention weights and make Hadamard product with the feature inputs to achieve the allocation of the attention weights of the feature inputs. After that regression prediction is performed using long short term memory neural network (LSTM). Finally, through the fully connected layer, the predicted values of the target monitoring points are integrated and output. In this paper, we take the surface collapse area in the west second mining area of Longshou mine as an example to give the prediction results of its surface subsidence monitoring data and compare them with the actual collected data. The results show that the combined CNN-Attention-LSTM model with the introduction of the attention mechanism is more accurate than the CNN-LSTM model and the LSTM model respectively, and the addition of effective feature inputs can significantly improve the prediction accuracy of the CNN-Attention-LSTM model. Reference | Related Articles | Metrics