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Application of multi-source point cloud fusion technology in urban renewal data acquisition MU Cuiwei, WANG Zhaoze Bulletin of Surveying and Mapping    2024, 0 (11): 151-155.   DOI: 10.13474/j.cnki.11-2246.2024.1126 Abstract （539）      PDF（pc） （1978KB）（205）       Knowledge map    Save Multi-source point cloud fusion technology is playing an increasingly important role in urban renewal data collection. This technology can achieve comprehensive and accurate perception of urban features by integrating point cloud data obtained from different devices and sensors. In the process of urban renewal, the use of multi-source point cloud data fusion technology can quickly and efficiently obtain urban basic spatial data, providing rich spatial data information for urban renewal planning, design, and decision-making. Through the fusion processing and analysis of point cloud data obtained from data collection equipment such as vehicle-mounted mobile measurement systems, motorcycle-mounted mobile measurement systems, drone-mounted mobile measurement systems, and stationary 3D laser scanners, various basic spatial data required for urban renewal can be quickly obtained. Furthermore, an in-depth analysis and research on the accuracy of the data in plane coordinates is carried out, thereby promoting the popularization and application of this technology in urban renewal data collection, and providing beneficial assistance for accelerating the intelligent and fine development of urban renewal work. 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 （396）      PDF（pc） （2931KB）（213）       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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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 （350）      PDF（pc） （2039KB）（118）       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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Monitoring and prediction of ground subsidence in mining areas using DS-InSAR and LSTM WANG Benhao, WANG Yanxia, XIANG Xueyong, HU Hong Bulletin of Surveying and Mapping    2024, 0 (11): 44-48.   DOI: 10.13474/j.cnki.11-2246.2024.1108 Abstract （331）      PDF（pc） （3558KB）（162）       Knowledge map    Save In response to the problem of low point density and uneven distribution in subsidence monitoring of mining areas using conventional InSAR technology, this paper uses 36 Sentinel-1A image data from August 2020 to August 2023 to obtain surface deformation information of Langyashan mining area in Chuzhou city, Anhui province using DS-InSAR technology. And the LSTM neural network model is used to predict the future settlement trend of the area with severe ground subsidence in the mining area, in order to understand the future development trend of ground subsidence in the mining area. The research results indicate that:①Compared with traditional PS-InSAR technology, DS-InSAR technology can significantly increase the number of monitoring points in mining areas and more comprehensively reflect surface subsidence information in mining areas. ②During the monitoring period, there are three deformation zones in the mining area, with a maximum settlement of 32.4 mm and a maximum settlement rate of 10.8 mm/a. ③By comparing with the GM (1,1) model and using the selected 6 settlement feature points, it is found that the LSTM neural network model exhibited higher prediction accuracy. ④For the area with the highest cumulative settlement, we use the LSTM model to predict the cumulative settlement of the 6 feature points in the area for the next 12 months. The prediction results show that the future settlement in the area fluctuates within a certain range, and no obvious settlement trend has been observed yet. 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 （325）      PDF（pc） （1953KB）（199）       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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Key technologies and applications of new energy site selection based on GIS+BIM WANG Lei, MIAO Chengguang, HAN Xiaoliang, CHEN Jiajing, HOU Peng, ZHAO Haifeng Bulletin of Surveying and Mapping    2024, 0 (10): 168-173.   DOI: 10.13474/j.cnki.11-2246.2024.1028. Abstract （323）      PDF（pc） （4123KB）（156）       Knowledge map    Save With the proposal of carbon neutrality goals, the development and utilization of new energy are growing. Due to the numerous factors involved in project design, it is difficult to select the location of new energy sources under the constraints of land and geography. Facing the explosive growth of new energy projects, traditional site selection design methods urgently need to improve efficiency to ensure the scientific and rational nature of new energy projects. Based on the analysis of the principles and application status of GIS+BIM technology, this article proposes a new solution for intelligent site selection of new energy sources. A 3D digital platform is constructed based on the integration of GIS+BIM technology. Through the platform, various resource conditions such as geographical location and environmental factors are fully analyzed, and comprehensive evaluation and optimization of new energy project locations are achieved. Through application analysis, the research results of the article can ensure the scientific and rationality of site selection, improve design efficiency by 15%, improve site selection accuracy, reduce costs, and promote the sustainable development of new energy projects. Reference | Related Articles | Metrics

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Dynamic monitoring for open-pit mine reclamation based on UAV oblique photogrammetry ZHONG Weihua, LIU Jingkuang Bulletin of Surveying and Mapping    2025, 0 (3): 21-26.   DOI: 10.13474/j.cnki.11-2246.2025.0304 Abstract （318）      PDF（pc） （8280KB）（212）       Knowledge map    Save Traditional open-pit mine reclamation monitoring relies on satellite remote sensing and on-site investigation, but suffers from low precision and efficiency. To enhance dynamic monitoring and improve oversight, this paper explores UAV oblique photogrammetry. Using an open-pit mine in Guangzhou as a test case, the technology generated a real-life 3D Mesh model across four phases, synchronously outputting DEM and producing DLG through stereo acquisition. Monitoring indices such as earth backfill elevation, volume, and building demolition were analyzed using DEM and DLG data. Results indicate that UAV data collection over 1.56km 2 took about one hour, three minutes, and fifty-five seconds, with a GSD of 2.51cm/pixels, marking an improvement in efficiency and accuracy over traditional methods. Additionally, the DLG data helped count a demolition area of 16442.36m 2, and DEM data allowed for the calculation of a total earth backfill volume of 0.017km 3, demonstrating the technology's digital and visual analysis capabilities. This study offers technical support for regional authorities in dynamically monitoring open-pit mine reclamation and provides references for monitoring other mines. Reference | Related Articles | Metrics

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Application of time series InSAR technology in surface subsidence monitoring and spatio-temporal evolution analysis in mining area ZHANG Yuxin, YUAN Xiping, GAN Shu, PENG Xiang, WANG Song Bulletin of Surveying and Mapping    2025, 0 (3): 15-20.   DOI: 10.13474/j.cnki.11-2246.2025.0303 Abstract （317）      PDF（pc） （3246KB）（240）       Knowledge map    Save In view of the hazards caused by surface subsidence to the safety, environment, socio-economic development and sustainability of resource utilization in the mining area, 63 Sentinel-1A data from December 31, 2021 to March 2, 2024 were first obtained, and SBAS-InSAR (time series interferometry) technology was adopted to monitor the surface deformation of Baicao mining area. The results of surface settlement rate and cumulative settlement in the mine area are obtained, and then the reliability analysis of the monitoring results is carried out by using the measured data. Finally, the settlement of the mine area is predicted based on the LSTM model, and the spatiotemporal variation characteristics and evolution rules of the settlement of the mine area are analyzed in detail.The final conclusions are as follows: ① Spatially, the surface subsidence of Baichuang Mining area is mainly concentrated in the west of the mining area, with the maximum subsidence of -316.86mm and the maximum annual average subsidence rate of -148.4mm/a, and the total subsidence area of 0.6236km 2, of which the heavy and extremely heavy subsidence area of 0.2804km 2 needs to be monitored.②In time series, the area with severe subsidence starts to settle from the monitoring starting point, and the subsidence rate tends to be uniform. If no protection is taken, the area will continue to settle in the future, and the settlement may be intensified.③The fitting degree of measured data and monitoring data is high, and the coefficient of determination R 2 is up to 0.994. The prediction effect of LSTM prediction model on monitoring data is good, and the linear fitting coefficient of determination R 2 of predicted value and monitoring value can reach more than 0.946, indicating that the prediction of surface settlement by LSTM model can meet the requirement of accuracy. The experimental results can provide technical support for disaster prevention and control in mining areas, and provide strong support for more accurate surface deformation evaluation in mining areas. Reference | Related Articles | Metrics

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Improved object detection algorithm HCAM-YOLO in traffic scenes based on YOLOv5 WANG Zhitao, ZHANG Ruiju, WANG Jian, ZHAO Jiaxing, LIU Yantao Bulletin of Surveying and Mapping    2024, 0 (11): 61-67.   DOI: 10.13474/j.cnki.11-2246.2024.1111 Abstract （291）      PDF（pc） （2352KB）（154）       Knowledge map    Save The rapid and precise detection of targets in traffic scenarios is crucial for intelligent traffic management and driving path decision-making. Traditional target detection models often grapple with issues such as inadequate detection accuracy, high rates of leak detection and false detection due to the complexity and variability of the traffic environment, and the diversity and sparsity of target features. To address these challenges, this paper introduces a YOLOv5 target detection model, HCAM-YOLO, which leverages the HcPAN feature fusion network. The crux of this approach lies in addressing the issue of local information being easily lost during the PAN network's feature fusion process. A hybrid convolutional attention mechanism(HCAM) is designed to enhance multi-scale information extraction in feature fusion networks. By integrating the HCAM module into the PAN's underlying structure, the sensitivity of key local features is enhanced, while the fusion effect of deep semantic information and shallow positional data is strengthened. This method's novelty lies in its use of an attention mechanism to optimize the feature fusion process, thereby improving the model's detection performance of pedestrians, motor vehicles, and other targets in complex traffic environments. The experimental dataset comprises the Rope 3D dataset, Road Veh dataset, and Road Ped dataset. The results demonstrate that the HCAM module is more suitable for integration into the underlying PAN network than other attention mechanisms. When compared to the basic YOLOv5 model, the precision and recall of the final HCAM-YOLO algorithm model increased by 3.4% and 3.2%,respectively, and mAP@0.5/% by 3.8%. The HCAM-YOLO algorithm model proposed in this paper exhibits strong adaptability to target detection tasks in traffic scenes with complex backgrounds. Reference | Related Articles | Metrics

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Application of landslide geohazard investigation based on realistic 3D and GIM technology MA Jianxiong, MING Jing, ZHOU Chengtao, GAN Ze Bulletin of Surveying and Mapping    2024, 0 (11): 74-77,161.   DOI: 10.13474/j.cnki.11-2246.2024.1113 Abstract （282）      PDF（pc） （3612KB）（115）       Knowledge map    Save Accompanied by the accelerating pace of infrastructure construction and the continuous improvement of the quality requirements of engineering construction, the accuracy of landslide engineering survey also puts forward higher requirements, this paper takes a project on the right bank of the Yangtze River in Liangjiang New Area of Chongqing Municipality as an example, and adopts the geological fine investigation method based on the realistic 3D and GIM technology, realizing a breakthrough of the application technology of three-dimensional fine survey of landslide engineering in the mountainous city, which provides support for the emergency disposal of landslide and the implementation of engineering. The results show that:① Based on the real-life 3D model realized the interpretation of the key elements of landslide geohazards, which provides a powerful auxiliary decision-making and basis for geohazard investigation; ② Through the 3D geologic modeling method with GIM technology as the core, the construction of a refined 3D geologic information model of the study area is realized, which has the characteristics of rapidity, accuracy, and reliability; ③ Through the integrated integration and fusion application of the real-life 3D model and GIM technology, the survey results are more refined, providing effective data support and scientific decision-making for the emergency disposal of landslides and the implementation of he project. 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 （273）      PDF（pc） （2142KB）（101）       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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Target detection method of railway catenary components in UAV images based on improved YOLOv7 SONG Zongying, WANG Xingzhong, ZENG Shan, ZHANG Zhengjun, YIN Taijun, LIU Hongli Bulletin of Surveying and Mapping    2024, 0 (11): 108-114.   DOI: 10.13474/j.cnki.11-2246.2024.1119 Abstract （271）      PDF（pc） （2863KB）（79）       Knowledge map    Save The catenary system, as an essential component of electrified railways, provides energy to trains and ensures their normal operation. Damage to catenary system components poses a threat to train safety, making it crucial to regularly inspect the condition of these components. In recent years, UAVs have been widely used in monitoring the condition of critical catenary components. However, due to the complex and variable backgrounds, significant scale changes, and the presence of many small targets in the catenary images captured by UAVs, existing detection algorithms frequently suffer from false detections and missed detections of catenary components. To address this issue, this paper proposes a catenary component target detection method based on an improved YOLOv7 algorithm. By introducing an enhanced receptive field module, the network's feature extraction capability is strengthened, leading to more discriminative target feature representations. Additionally, an improved coordinate attention mechanism is incorporated during the fusion of adjacent scale feature maps to highlight the target features of catenary components and suppress redundant background information. The bounding box loss function is optimized using the Wasserstein distance, effectively improving detection accuracy. Experiments on the catenary component dataset show that the improved YOLOv7 algorithm can accurately detect various catenary components in drone-captured images, achieving a mean average precision of 97.27%, which is 3.83% higher than before the improvement. The proposed algorithm enhances the high-precision and rapid detection capabilities of drones for critical catenary components, providing technical support for achieving more intelligent drone inspections. 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 （265）      PDF（pc） （1834KB）（102）       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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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 （252）      PDF（pc） （11904KB）（220）       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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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 （252）      PDF（pc） （2083KB）（184）       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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Monitoring of deformation in mining buildings based on GNSS and InSAR technologies ZHAO Qi Bulletin of Surveying and Mapping    2024, 0 (11): 126-132,166.   DOI: 10.13474/j.cnki.11-2246.2024.1122 Abstract （252）      PDF（pc） （2387KB）（129）       Knowledge map    Save Mining area buildings are critical components of coal mining production. Dangerous deformations can severely threaten normal production and may even lead to safety incidents. In this study, the GNSS-InSAR fusion method is proposed to achieve high-precision deformation monitoring of mining area buildings. Taking the northern suburbs mining area of Xilinhot city,Inner Mongolia, as the study area, the dynamic deformation of mining buildings is obtained based on the GNSS,SBAS-InSAR, and GNSS-InSAR fusion methods with 30 scenes of Sentinel-1A image data and 35 sites GNSS data. The results show that the GNSS-InSAR fusion method is 43.9% more accurate than the SBAS-InSAR,which indicates that the proposed method provides better support for the deformation monitoring and safety assessment of mining area buildings. The combination of rainfall,temperature,and time-series deformation results inferred that temperature is the primary cause of building deformation,and the impact of surrounding mining activities on the buildings is negligible. Moreover,during the monitoring period, all deformation values of the mining area buildings are below the permissible deformation thresholds.The results indicate no dangerous deformations and confirm that the buildings can continue safe operations. Reference | Related Articles | Metrics

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Research on digital review of high-precision maps for intelligent driving WU Jiatong, DI Lin, HUANG Long Bulletin of Surveying and Mapping    2024, 0 (10): 174-178.   DOI: 10.13474/j.cnki.11-2246.2024.1029. Abstract （247）      PDF（pc） （1335KB）（197）       Knowledge map    Save As a new form of map industry, smart driving high-precision maps contain high-rich, high-precision and high-freshness geographical information data, which are related to national sovereignty, security and interests. The current high-precision map review work lacks effective automated review technology, standard databases and institutional guarantees, making it difficult to meet the demand for map freshness by smart driving, which will have a certain impact on smart driving. This article summarizes the development status of digital review of high-precision maps for smart driving, sorts out relevant policy trends, systematically analyzes the key technologies of digital map review and the difficulties in large-scale application, and combines the map review business model to propose solutions for the digital review technology route for high-precision maps for smart driving aims to provide a reference for efficient and reliable high-precision map review. Reference | Related Articles | Metrics

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Automatic extraction of water body using multi-feature index ZHANG Baowen, ZHAO Zhan Bulletin of Surveying and Mapping    2024, 0 (11): 21-26.   DOI: 10.13474/j.cnki.11-2246.2024.1104 Abstract （241）      PDF（pc） （3268KB）（133）       Knowledge map    Save At present, water extraction is still a semi-automatic method, which has low efficiency and is prone to omissions and extraction errors. In this paper, an automatic water extraction method using multi-feature index is proposed. Vegetation index, water index, improved water index and building land index are used to automatically extract water and non-water samples, and the classifier is trained to realize high-precision and automatic urban water extraction. Four typical experimental areas with different geographical conditions at home and abroad are selected for comparison and analysis with the traditional extraction method. Under this method, high extraction accuracy is achieved in all the four experimental areas, and Kappa coefficient reached above 0.94. Compared with single-band threshold method, Kappa coefficient increases by 7.2% on average. Compared with the water index method, Kappa coefficient increased by 3.8% on average. Reference | Related Articles | Metrics

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Comparison of terrain correction methods for high spatial resolution remote sensing images WANG Yan, LIU Yingjie, WU Jinwen, SUN Longyu, LIU Jingnan, XU Changhua Bulletin of Surveying and Mapping    2024, 0 (10): 91-97.   DOI: 10.13474/j.cnki.11-2246.2024.1015. Abstract （240）      PDF（pc） （10205KB）（145）       Knowledge map    Save In mountainous areas with complex terrain, terrain shadows have a great impact on the extraction of remote sensing image information. Therefore, terrain correction should be carried out on remote sensing images to eliminate terrain effects and restore the surface reflectance of terrain shadow areas. This article takes the eastern forest area of Liaoning province (Liaodong Forest Area) as the research area, and uses GF-1 WFV remote sensing images with a spatial resolution of 16m. SCS+C, Minnaert+SCS and SCEDIL correction models are used to perform terrain correction on the original images. Visual analysis, spectral retention effect, terrain correction effect, classification accuracy verification and consistency of spectral reflectance on cloudy and sunny steep slopes are used to compare the images before and after correction, Finally determine the optimal terrain correction model suitable for forest areas. The research results indicate that: ①for forest areas with continuous mountainous and hilly terrain and significant undulations, SCS+C has better spectral retention compared to Minnaert+SCS and SCEDIL models, with a difference of less than 4.32 in the mean reflectance of each band before and after calibration, and there is no overcorrection phenomenon. The terrain correction effect of the three models is judged by the correlation between the corrected near-infrared reflectance and the cosine of the solar incidence angle. The SCS+C model has the smallest correlation, the best terrain correction effect, the Minnaert+SCS model has a slightly larger correlation and the SCEDIL model has overcorrection phenomenon. The image classification accuracy of the SCS+C model after correction has improved by nearly 3% compared to before correction, and is nearly 2% higher than the SCEDIL models of Minnaert+SCS. ②Based on the principle of terrain correction, a new evaluation method for the consistency of spectral reflectance on steep slopes of yin and yang has been added. The impact of NDVI on steep slopes of yin and yang before and after correction is used as the evaluation index for terrain correction effect. The SCS+C correction effect is the best, and the absolute deviation (10 -2) of the mean spectral reflectance on steep slopes of yin and yang before and after correction in two typical areas is reduced from 1.14 to 0.58 and from 1.67 to 0.49, respectively. After correction, the consistency of steep slopes of yin and yang is improved. In summary, the SCS+C model is superior to Minnaert+SCS and SCEDIL, which is more suitable for terrain correction in forest areas. Reference | Related Articles | Metrics

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Cross-category few-shot segmentation for farmland recognition in remote sensing images WANG Xing, NI Huan Bulletin of Surveying and Mapping    2024, 0 (10): 77-83.   DOI: 10.13474/j.cnki.11-2246.2024.1013. Abstract （231）      PDF（pc） （1894KB）（170）       Knowledge map    Save Deep learning-driven semantic segmentation methods for remote sensing images rely heavily on a large number of manually labeled samples and exhibit poor generalization for unknown tasks, especially in the fine-grained semantic segmentation task where the category system is constantly updated, and the recognition accuracy of the unknown categories (the categories that don't exist in the training samples) needs to be urgently improved. Based on this, the paper proposes a cross-category few-shot segmentation method aimed at multiple farmland categories. The method designs a dual-branch structure, comprising a support branch and a query branch, where the support branch is used for the extraction of segmentation prior, and the query branch is used to complete the propagation of segmentation prior and obtain the segmentation results of the query image. Additionally, the method applies query features to generate self-supporting query prototypes, which significantly improves the expressive ability of the prototypes; a regularization mechanism for prototype alignment between the support and query set is introduced, which makes full use of the knowledge from the support set and improves the discriminative ability of the segmentation. The experiments simultaneously introduce high spatial resolution and hyperspectral image land cover datasets to fully validate the performance of the proposed method. The experimental results show that compared with the existing few-shot segmentation methods, the proposed method can obtain more excellent cross-category farmland recognition results under few-shot conditions. Reference | Related Articles | Metrics