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The method of smoke and flame target extraction facing UAV and remote sensing images LIU Xiaodong, ZHAO Chenmeng, REN Yinghua, YANG Liping, ZHAO Like, ZHANG Ka Bulletin of Surveying and Mapping    2025, 0 (12): 7-14.   DOI: 10.13474/j.cnki.11-2246.2025.1202 Abstract （396）      PDF（pc） （9810KB）（261）       Knowledge map    Save Addressing the bottlenecks in UAV and remote sensing-based smoke/flame detection task,such as insufficient multi-scale feature capture,complex background interference,and blurred edges,a method of smoke and flame target extraction based on the improved YOLOv12 model is proposed in this paper.The proposed method enhances multi-scale feature fusion through a mixed local-channel attention (MLCA)mechanism,improves detail retention in low-resolution images via an adaptive downsampling (ADown)module,and refines boundary regression accuracy with a customized adaptive loss function.Furthermore,by integrating the fine-tuned SAM2.1 model,the paper's method can realize pixel-level segmentation of targets within detection boxes.Experiments on the FASDD＿UAV,FASDD＿RS,and S-Firedata datasets shows that the proposed method achieves mAP50 scores of 93.1%,78.5%,and 68.2%,outperforming the baseline model YOLOv12 by 1.3%,1.5%,and 1.2%,respectively.The proposed method demonstrates significant advantages in detecting small targets,handling occluded scenarios and complex lighting conditions.Additionally,ablation experiments have confirmed the feature enhancement effects of the MLCA and Adown modules,as well as the optimization effect of Focaler-PIoU on model performance through dynamic gradient allocation. Reference | Related Articles | Metrics

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Integration of multi-view images and deep learning for automated restoration and application of realistic textures in 3D building models LIU Yawen, TIAN Qin, GUO bingxuan, LI Demin Bulletin of Surveying and Mapping    2025, 0 (12): 15-19.   DOI: 10.13474/j.cnki.11-2246.2025.1203 Abstract （357）      PDF（pc） （4233KB）（221）       Knowledge map    Save 3D building models with both geometric accuracy and realistic textures have become an important component of the national new infrastructure construction.Due to constraints such as UAV flight conditions and building layout,a large number of real texture occlusion problems occur in the texture mapping of 3D building models,which affect their visualization effects and the functions of applications such as query and measurement.Existing methods are based on a single texture image for repair and treat the occluded area as an unknown random variable,leading to possible deviations of texture repair from the real facade features of buildings.Based on the characteristic that the occlusion range of the same facade of a building varies in images from different perspectives,this paper proposes an automatic facade texture occlusion repair algorithm combining multi-view images and deep learning networks.The algorithm extracts the occluded area by using the structural similarity of multi-view textures after texture alignment,automatically synthesizes the real facade texture through the graph-cut method,and uses the DeepFill model to repair and optimize the synthesized texture.Experiments show that this method can repair the real texture of more than 40% of the occluded area,and the SSIM and PSNR values of the repaired facade texture are improved compared with existing methods. Reference | Related Articles | Metrics

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EVTOL vertiport site selection based on multi-factor overlay and bi-level optimization model: a case study of Chengdu WANG Lizhi, XIAO Dongsheng, ZHANG Yinghao Bulletin of Surveying and Mapping    2025, 0 (12): 58-64,70.   DOI: 10.13474/j.cnki.11-2246.2025.1210 Abstract （354）      PDF（pc） （2906KB）（154）       Knowledge map    Save Addressing the lack of systematic methodologies for vertiport site selection of electric vertical take-off and landing (eVTOL)aircraft in the context of the low-altitude economy,this paper proposes a site selection approach integrating multi-factor analysis with a bi-level optimization model.Using Chengdu as a case study,candidate sites are initially screened through overlay analysis of multi-source geographic data,including population density,land use,and traffic accessibility.A bi-level optimization model is then developed to maximize the total served population,incorporating both exponential and Gaussian distance decay functions,and solved using a hybrid strategy combining mixed-integer linear programming (MILP)and genetic algorithm (GA).By comparing two initial site selection schemes(new sites and sites incorporating existing general aviation airports),the study comprehensively evaluates theoretical served population,optimal value,and coverage rate.Results show that the normalized Gaussian distance decay model achieves better performance in terms of theoretical served population (390747 people)and population coverage rate (42.84%in the 0～100k population interval),while the exponential decay model demonstrates stronger small-area coverage capability.The final selected optimal solution is the “site selection scheme based on the normalized Gaussian distance decay model with existing general aviation airports,” which balances service capacity and coverage scope.The study reveals a nonlinear relationship between the number of vertiports and the population coverage rate,and indicates that the Gaussian decay model is more suitable for site selection in densely populated areas.The research outcomes provide a scientific basis for planning low-altitude transportation networks in Chengdu and offer a referential theoretical and practical framework for eVTOL vertiport site selection in other cities. Reference | Related Articles | Metrics

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Research on multi-dimensional intelligent site selection and route planning application based on AI+GIS DAI Zhimin, WANG Haiyan, ZHANG Xing, TANG Hao, TANG Ming, ZHONG Yong, WU Baoyou Bulletin of Surveying and Mapping    2026, 0 (2): 174-179,186.   DOI: 10.13474/j.cnki.11-2246.2026.0228 Abstract （350）      PDF（pc） （2887KB）（140）       Knowledge map    Save To address issues such as the reliance on manual experience,low data integration efficiency,and lagging multi-dimensional evaluation in traditional power grid site selection and route planning,this paper proposes and studies a multi-dimensional intelligent site selection and route planning application system based on AI+GIS.With GIS technology as the spatial data support carrier,the system integrates a remote sensing interpretation module to realize the automatic extraction and high-precision analysis of geographical information such as terrain,vegetation,and buildings.It also integrates artificial intelligence algorithms to construct an evaluation model covering dimensions including environmental impact,project cost,and power grid security,enabling intelligent calculation and optimized ranking of site selection and route planning schemes.Verified through application in actual power grid projects,the platform can replace over 70% of repetitive work in traditional manual surveys,shorten the site selection and route planning cycle by 40%,and increase both the scheme compliance rate and the accuracy of economic evaluation to over 90%.The research shows that the in-depth integration of AI and GIS can effectively break through the limitations of traditional methods,provide scientific and efficient technical support for power grid planning,site selection,and route planning,and is of great significance for promoting the intelligent transformation of power grid planning. Reference | Related Articles | Metrics

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Fine-grained real-scene 3D modeling of Luoxing pagoda via aerial-ground collaboration and application in ancient pagoda conservation LI Lin, LI Pingping, LI Liangliang Bulletin of Surveying and Mapping    2025, 0 (12): 173-177.   DOI: 10.13474/j.cnki.11-2246.2025.1230 Abstract （347）      PDF（pc） （3251KB）（127）       Knowledge map    Save To address the challenge that single-technology approaches can not simultaneously cover high-altitude blind zones and near-ground intricate components in digital preservation of ancient pagodas,this study develops an aerial-ground collaborative technical framework for fine-grained real-scene 3D modeling,enhancing the accuracy and completeness of historical building digitization.Innovatively integrating coarse-model-guided intelligent route optimization with multi-source aerial-ground data synergy: UAV photogrammetry constructs a coarse model via orthophotography to guide encircling close-range photogrammetry,enabling high-precision data acquisition of pagoda tops and facades; ground/handheld LiDAR supplements millimeter-level point clouds of base structures; ICP point cloud registration,bilateral filtering denoising,and RANSAC fusion algorithms achieve seamless integration of aerial-ground data.Applied to Luoxing pagoda,the collaborative model demonstrates significant improvements: aerial triangulation achieves a georeferencing RMSE of 0.038m,point cloud registration precision reaches 5mm,and texture ghosting is improved obviously.Structural deformations and voids at the base are fully resolved.This method overcomes technical barriers in coordinated high-altitude/near-ground data acquisition,providing an efficient,non-contact,high-precision solution for digital conservation of cultural relics,thereby robustly supporting China's national cultural digitalization strategy. Reference | Related Articles | Metrics

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Spatio-temporal variation characteristics and influencing factors of snow cover and snow line in Qilian Mountains SU Xuewu, WANG Yonghong, QIN Kun, CHENG Wangyu, WANG Guoxi Bulletin of Surveying and Mapping    2025, 0 (12): 150-157.   DOI: 10.13474/j.cnki.11-2246.2025.1226 Abstract （343）      PDF（pc） （4117KB）（121）       Knowledge map    Save The Qilian Mountains is located on the northeastern edge of the Qinghai-Xizang Plateau.It has always been known as the ice source reservoir and is the source of life that supports the water resources of the Hexi Corridor.Based on MODIS snow cover products,the temporal and spatial variation characteristics of snow cover and snow line in Qilian Mountains from 2011 to 2020 were analyzed,and the dominant climatic factors affecting their changes were analyzed in combination with temperature and precipitation data.Research results show that: ①The Qilian Mountains are dominated by short-day snow areas,and the annual snow areas are mainly distributed in the western part of the Qilian Mountains.②The spatial distribution of the whole year snow area and the permanent snow line is obviously different.The western section is larger than the eastern section in the basin,and the altitude shows the characteristics of normal distribution.The area of the whole year snow area in the northeast slope direction is higher than that in the southwest slope direction,and the height of the permanent snow line is lower than that in the southwest slope direction.③The annual variation of seasonal snow cover area generally shows a trend of decreasing first and then increasing.The changes of seasonal temperature and precipitation are significantly correlated with the changes of seasonal snow cover.④The influence of temperature change on snow cover in the high-altitude mountainous area of the western section is weak,and the snow accumulation caused by precipitation has a more significant impact on the annual snow cover area and the permanent snow line.The snow cover in the low altitude area of the eastern section is difficult to accumulate all year round,and the influence of solar radiation and rain erosion during the snow melting period on the annual snow cover area and the permanent snow line is more significant. Reference | Related Articles | Metrics

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Vehicle object detection approach in drone imagery based on improved YOLOv8s TENG Min, ZHANG Bo, XU Jiawei, LIN Cong, SHEN Yu, CHU Zhengwei Bulletin of Surveying and Mapping    2026, 0 (2): 68-73,80.   DOI: 10.13474/j.cnki.11-2246.2026.0211 Abstract （333）      PDF（pc） （2770KB）（134）       Knowledge map    Save Accurate and real-time vehicle detection and tracking provide crucial data support for traffic flow estimation and intelligent traffic management.Drone imagery has emerged as a vital data source for vehicle detection tasks.To address the weak ability of existing YOLO models to detect small objects within complex scenarios and the scarcity of open-source datasets for drone vehicle detection,this paper proposes the YOLOv8s-VOD model specifically designed for vehicle detection tasks,and introduces the open-source dataset NJVOD.This method constructs C2f-PTB and BiFPN-GLSA modules to achieve collaborative extraction of global-local featuresand effective fusion of multi-scale semantic and edge information,thereby improving detection accuracywhile reducing network complexity.Experimental results show that YOLOv8s-VOD achieves the highest detection accuracy with minimal parameters,outperforming existing methods by 2.4～12.2 percentage poin on the VEDAI dataset and 4.1～5.3 percentage point on the NJVOD dataset;The C2f-PTB and BiFPN-GLSA modules proposed in this work both effectively improve small-object detection accuracy.Additionally,the newly created NJVOD dataset offers crucial support for related research. Reference | Related Articles | Metrics

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Semantic segmentation of UAV orthophoto images based on improved U-Net3+ model JIANG Lei, LIANG Cong, ZHAO Xu, WANG Peng, YAN Wenkai, YANG Hongding, WU Jizhong Bulletin of Surveying and Mapping    2026, 0 (2): 137-143.   DOI: 10.13474/j.cnki.11-2246.2026.0222 Abstract （321）      PDF（pc） （2443KB）（101）       Knowledge map    Save To address the limitations of insufficient feature abstraction and cross-scale feature redundancy in semantic segmentation of unmanned aerial vehicle (UAV)orthophoto images using the U-Net3+ model,this study proposes an improved U-Net3+ architecture.The improvement incorporates ResNet50,a deep convolutional neural network based on residual network,as the backbone for feature extraction.Simultaneously,the convolutional block attention module (CBAM)is integrated as a lightweight attention mechanism.Experimental results demonstrate that the proposed U-Net3+ model delivers significant improvements in segmentation performance,achieving an 8.3% increase in overall accuracy,2.6% in mean intersection over union,and 1.9% in F1-score compared to the original U-Net3+ model.The proposed model consistently outperforms established benchmarks,including FCN,U-Net,U-Net++,and the DeepLab series,across all evaluation metrics,demonstrating superior feature discrimination and segmentation accuracy in representative scene types.Moreover,the integration of either ResNet50 or CBAM alone results in moderate gains,their combined implementation leads to a notable synergistic effect,yielding the most effective results in segmentation tasks.The improved U-Net3+ model has significantly improved the segmentation accuracy,providing an effective technical solution for semantic segmentation of UAV orthophoto maps. Reference | Related Articles | Metrics

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BeiDou satellite clock bias modeling and prediction method based on limited inter-satellite link measurement data YE Chaofan, BAI Yan, ZHANG Xiaozhen, ZHANG Feng Bulletin of Surveying and Mapping    2026, 0 (3): 1-6.   DOI: 10.13474/j.cnki.11-2246.2026.0301 Abstract （311）   HTML （42）    PDF（pc） （1684KB）（187）       Knowledge map    Save BeiDou Navigation Satellite System (BDS) achieves autonomous orbit determination and time synchronization capabilities through inter-satellite link (ISL) technology,enabling the determination and predictive modeling of satellite clock biases using ISL measurements during autonomous operation.Addressing discontinuous and non-uniform data characteristics in BDS ISL measurements,this study proposes a hybrid modeling approach integrating a quadratic polynomial (QP) model with the Lomb-Scargle (LS) periodic correction algorithm to effectively decompose clock biases into trend,periodic,and stochastic components.Validations using BeiDou-3 ISL measurements demonstrate that the QP+LS fusion model significantly enhances prediction accuracy:3-hour predictions achieve an RMS better than 0.128 ns,and 24-hour predictions an RMS better than 0.442 ns,representing average improvements of 29.68% and 17.60% respectively over the conventional QP+FFT algorithm.These results provide critical technical references for clock bias prediction in BDS autonomous operations and clock modeling under constrained measurement data conditions. Reference | Related Articles | Metrics

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Monitoring and influencing factors analysis of land subsidence along the Beijing-Xiong'an intercity railway using InSAR technology LIN Yang, YU Bing, TIAN Xin, ZHANG Guanjun, LIU Cheng, GAN Jun, LI Guangyu Bulletin of Surveying and Mapping    2026, 0 (2): 46-53.   DOI: 10.13474/j.cnki.11-2246.2026.0208 Abstract （310）      PDF（pc） （17688KB）（124）       Knowledge map    Save To address the limited quantitative research and insufficient temporal coverage of surface subsidence and its driving factors along the Beijing-Xiong'an intercity railway.The SBAS-InSAR technique,Moran's I index,and multi-scale geographically weighted regression (MGWR)model were employed to monitor and analyze surface deformation from November 2022 to November 2024.Within a 4 km buffer zone on both sides of the railway,the maximum average annual vertical deformation rate reached -127 mm/a.Subsidence was mainly concentrated west of Xiong'an Station to Bazhou North Station,showing significant spatial clustering.In order of contributions size,driving factors are groundwater level fluctuation、distance to faults、distance to rivers、surface roughness、distance to roads.Cumulative deformation was negatively correlated with groundwater level fluctuation,indicating that groundwater extraction aggravates subsidence.In the section west of Xiong'an to Bazhou North,subsidence increased with distance to faults,suggesting that the Niudong Fault may cause the observed differential settlement.These findings provide a scientific reference for railway maintenance and groundwater resource management. Reference | Related Articles | Metrics

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Identification of rural homestead utilization status by integrating multi-source high-resolution remote sensing imagery XIE Jianing, LIU Zhenbo, YANG Yuting Bulletin of Surveying and Mapping    2026, 0 (2): 54-59,67.   DOI: 10.13474/j.cnki.11-2246.2026.0209 Abstract （309）      PDF（pc） （10148KB）（159）       Knowledge map    Save To furnish data-driven decision support for rural settlement spatial restructuring,revitalization of underutilized homesteads,and precision land governance through accurate identification and classification of rural homestead utilization states.A rural homestead utilization identification framework driven by multi-source high-resolution remote sensing data is proposed,integrating deep learning and machine learning techniques.The findings indicate that:①The overall accuracy of homestead recognition based on GF imagery and Google Earth imagery exceeds 84%;②The XGBoost model demonstrates superior performance in identifying inhabited homesteads,achieving a precision of 94.6%,while the random forest (RF)model exhibits the best performance in recognizing idle homesteads,with a precision of 77.8%;③According to comprehensive evaluations using ROC and PR curves,the RF algorithm outperforms the others,with the green looking ratio derived from Google Earth imagery contributing 12.7%to feature importance.This study substantiates that fusing multi-source remote sensing and machine learning technologies constitutes an effective approach for homestead utilization mapping,thereby providing a robust technical foundation for advancing land resource intensification and sustainable rural land management. Reference | Related Articles | Metrics

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Deformation monitoring of ring rockfill dam in pumped storage power station based on spaceborne InSAR WAN Peng, ZHAI Ruoming, DING Bangning, LI Jianzhou, ZOU Shuangchao Bulletin of Surveying and Mapping    2025, 0 (12): 71-76.   DOI: 10.13474/j.cnki.11-2246.2025.1212 Abstract （305）      PDF（pc） （9409KB）（130）       Knowledge map    Save This study aims to investigate the deformation characteristics of the annular rockfill dam in pumped-storage power plants using time-series spaceborne InSAR deformation monitoring technology.The permanent scatterer InSAR (PSInSAR)processing technique was employed,combined with high-precision digital elevation model (DEM)data,to monitor the surface deformation of the annular rockfill dam in the upper reservoir of the Zhanghewan pumped-storage power plant.The monitoring accuracy of InSAR was validated using ground-based synchronous monitoring data from high-precision measurement robots,and the deformation characteristics of the annular rockfill dam were analyzed.The results show that the dam body and slopes of the upper reservoir in the Zhanghewan power plant exhibited an overall uplift trend during the observation period,which is preliminarily attributed to the temperature increase from winter to summer.The correlation coefficient between the deformation rates of monitoring points obtained by InSAR technology and the ground-based synchronous observations reached 0.838,with a root-mean-square error (RMSE)of 7.24mm/a.The cumulative displacement of monitoring points in the annular rockfill dam is significantly correlated with temperature.The influence of temperature on the displacement of monitoring points exhibits a slow nonlinear characteristic,and the responses of different monitoring points are divergent.The displacement of monitoring points shows a strong correlation with water level changes,indicating that water levels have a significant impact on the upstream-downstream displacement of specific points.This study provides important references for the research and application of InSAR deformation monitoring for large-area structures such as annular rockfill dams. Reference | Related Articles | Metrics

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Analysis of low-altitude visual flight obstacles based on real-scene 3D modeling: a case study of Chongqing ZENG Yixiao, WU Menghua, WANG Leyuan, PENG Hui, LI Han Bulletin of Surveying and Mapping    2025, 0 (12): 93-97.   DOI: 10.13474/j.cnki.11-2246.2025.1216 Abstract （297）      PDF（pc） （5048KB）（142）       Knowledge map    Save As a mega mountainous city in Southwest China,Chongqing features densely clustered high-rises and concentrated populations in its urban areas.Under the perspective of intelligent transformation in new surveying and mapping,this study addresses safety challenges posed by complex spatial environments to low-altitude economic development.This paper focus on key technologies for obstacle extraction and analysis during the compilation of 2D and 3D low-altitude visual navigation charts.For geographical environments characterized by complex terrain and densely distributed high-rises,a technical framework for obstacle extraction in visual navigation charts is developed based on 3D real-scene modeling.The achievements are applied and demonstrated in Chongqing's citywide visual aeronautical charts compilation project,supporting follow-up processes including visual flight navigation and safety early-warning systems,thereby enhancing low-altitude flight safety operations. Reference | Related Articles | Metrics

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Rapid detection of geometric parameters of subway tunnel catenary based on point cloud data BAO Yan, GAO Liye, LU Jianjun Bulletin of Surveying and Mapping    2025, 0 (12): 65-70.   DOI: 10.13474/j.cnki.11-2246.2025.1211 Abstract （294）      PDF（pc） （1496KB）（124）       Knowledge map    Save The electric traction system is important equipment of subway power supply facilities,mainly composed of pantographs,catenaries,traction substations and other devices.Catenary failures may cause major safety accidents.In order to quickly and accurately detect the geometric parameters of catenaries,this paper proposes a method to analyze the dynamic height and stagger of the catenary in subway tunnels based on point cloud data.This method extracts the central axis of the tunnel via the spatial projection method,cuts the point cloud according to the relative positional relationship of cross-section point clouds,and thus quickly extracts the position coordinates of the tops of the two rails and the bottom of the catenary.Through the geometrical positional relationship of these three,it can calculate the dynamic height and stagger of the catenary at any mileage section of the tunnel.Taking a subway tunnel as an example for verification,the dynamic height and stagger of the catenary extracted from point cloud data are compared with the measured values obtained by existing detection methods.Results show that the error is within the design specifications,which verifies the accuracy of the method.Based on 3D laser technology,this method can quickly,comprehensively and accurately obtain the geometric parameters of catenaries in tunnels,greatly improving detection efficiency and boasting broad application prospects. Reference | Related Articles | Metrics

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Construction and application of water-land integrated DEM based on multi-source data fusion of lake wetlands: a case study of Sanchahe National Wetland Park WEI Linghui, LI Bao, CHEN Liangsong, GAO Yang, SUN Wei Bulletin of Surveying and Mapping    2026, 0 (2): 18-23,45.   DOI: 10.13474/j.cnki.11-2246.2026.0204 Abstract （290）      PDF（pc） （3096KB）（113）       Knowledge map    Save The terrain environment of lake wetlands is complex,and a single measurement method is difficult to meet the requirements of high-precision terrain data acquisition and high-precision three-dimensional expression.This paper proposes a multi-source collaborative measurement method,which uses unmanned boats for single-beam depth sounding,unmanned aerial vehicles (UAVs)for LiDAR height measurement,and combines GNSS and depth rods for supplementary measurement in small areas to achieve efficient,high-precision,and high-density data collection of water and land terrain.Based on the vector boundary of the water area,the constrained Delaunay triangulation algorithm is adopted to seamlessly integrate and smoothly transition the water and underwater terrain data,constructing a high-precision water-land integrated digital elevation model (DEM),and then establishing a surface water storage calculation model.The research results show that the collaborative application of multiple measurement technologies can significantly improve the data collection efficiency and accuracy of the water-land integrated DEM.This method can provide reliable technical support for water resources investigation,management,and sustainable utilization. Reference | Related Articles | Metrics

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Characterization of surface subsidence in Hefei city based on the new domestic LT-1 SAR satellite WANG Yingchun, LI Jinchao, WANG Chengzhi, LI Shuiping, TAO Tingye Bulletin of Surveying and Mapping    2026, 0 (2): 149-155,173.   DOI: 10.13474/j.cnki.11-2246.2026.0224 Abstract （285）      PDF（pc） （5334KB）（93）       Knowledge map    Save The LuTan-1 (LT-1)satellite is China's first L-band differential interferometry SAR satellite,notable for its high spatial resolution,short revisit cycle,and full polarization capabilities.The current research on the application of SAR images acquired by this novel sensor is insufficient.The city of Hefei is chosen as the focal point for this study,where in three high-resolution radar images from LT-1 spanning July 2023 to May 2024 are utilized.To derive the surface subsidence deformation image of Hefei city,we employ the GACOS atmospheric correction model and delaunay minimum cost flow phase unwrapping method.The findings indicate that the ground deformation in Hefei city is generally smooth,with cumulative settlements in most areas ranging from -200～20 mm.However,certain local areas exhibit noticeable deformation trends.Five regions with significant deformation have been selected for a more detailed analysis,and their results have been compared with concurrent Sentinel observations,thereby validating the reliability of the LT-1 monitoring data.This study demonstrates that LT-1 imagery is capable of detecting finer deformation signals and providing higher-precision subsidence information,which can serve as vital support for urban safety and maintenance monitoring. Reference | Related Articles | Metrics

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Application of time-series synthetic aperture radar interferometry in embankment deformation monitoring LIANG Zhaoxiong, WANG Xizhi, XU Dan Bulletin of Surveying and Mapping    2025, 0 (12): 88-92.   DOI: 10.13474/j.cnki.11-2246.2025.1215 Abstract （282）      PDF（pc） （3427KB）（108）       Knowledge map    Save Deformation monitoring of embankments is of great significance for flood prevention safety and engineering maintenance.This study employed DS-InSAR technology to monitor the deformation of the Sanshui section of the Beijiang embankment in Guangdong province,based on 74 scenes of Sentinel-1A data from 2019 to 2023.By integrating persistent scatterers (PS)and distributed scatterers (DS)signals,the spatio-temporal distribution characteristics of embankment deformation were obtained.The results indicate that: ①The annual deformation rate in the study area ranged from -9 to 6mm/a,showing significant spatial heterogeneity,with three subsidence centers formed from north to south.The maximum subsidence rate reached -9mm/a at the turning point of the embankment in the Lubao Town section (region B),while the subsidence area in the Leping Town section (region C)extended to the 200m protection zone outside the embankment.②Cumulative deformation analysis revealed a notable acceleration in subsidence during 2022—2023,with the subsidence in 2023 alone accounting for 61.5%of the total over five years.The cumulative subsidence near the Lubao Sluice reached -39mm.③Time-series analysis of characteristic points showed significant subsidence in PS1 and PS2 in September 2023,while PS3 exhibited a stable subsidence trend.PS4 underwent alternating uplift and subsidence processes.This study validates the applicability of time-series InSAR technology in monitoring earthen embankments and provides scientific support for the safety maintenance of critical embankment projects in the Pearl River Delta. Reference | Related Articles | Metrics

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Exploration and practice of reforming education for“spatial analysis” course in the big data era SHI Yan, HAN Jingsha, DENG Min, LIU Qiliang Bulletin of Surveying and Mapping    2025, 0 (12): 163-167.   DOI: 10.13474/j.cnki.11-2246.2025.1228 Abstract （278）      PDF（pc） （1406KB）（214）       Knowledge map    Save The rapid iteration of new technologies in the big data era necessitates educational reform in the major of Geographic Information Science.This paper systematically analyzed and summarized the current situation and main issues of “spatial analysis” course teaching in universities of our country.By combining with the development tendency of geographic information industry in the big data era,we proposed a series of innovative reforming measures for “spatial analysis” course teaching in terms of course knowledge system,classroom teaching method,and practical teaching mode.These measures produced significant achievements,and can provide case guidance for optimizing the course education system of the Geographic Information Science major. Reference | Related Articles | Metrics

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Research and application of an improved ecological assessment index model integrating multi-source spatio-temporal data PAN Lei, ZHOU Song, LI Yanan, RAO Jiawang Bulletin of Surveying and Mapping    2025, 0 (12): 144-149,177.   DOI: 10.13474/j.cnki.11-2246.2025.1225 Abstract （278）      PDF（pc） （2749KB）（292）       Knowledge map    Save To address the limitations of existing ecological indices that often neglect water bodies and are inadequate for comprehensive regional assessment.This study develops an improved remote sensing ecological index (MRSEI)by integrated multi-source spatio-temporal data.The model incorporates five indicators—NDVI,NDBSI,TVDI,EWI,and TWI—to evaluate ecological conditions from four dimensions: greenness,dryness,wetness,and water.An empirical analysis is conducted using 2023 Landsat-8 imagery in Peixian county,Jiangsu province.The results indicate that the ecological environment in the study area had generally improved,with significant enhancements observed in three western towns and eastern lakeside regions.Compared to the traditional RSEI,the MRSEI demonstrates higher accuracy in evaluating water bodies (with a approximately 12% increase in correlation coefficient),owing to its incorporation of water-related indicators and optimized weighting.This study provides an effective tool for accurate ecological quality assessment and sustainable management at the county level,and more suitable for precise monitoring of large-scale land-water composite ecosystems. Reference | Related Articles | Metrics

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Research and application of building deformation monitoring based on BDS-3 ZHANG Xijun, FU Shaohua, WU Haiyang, XU Hanchao, ZHAO Shuyang, ZHAO Chuanhao Bulletin of Surveying and Mapping    2025, 0 (12): 103-108.   DOI: 10.13474/j.cnki.11-2246.2025.1218 Abstract （275）      PDF（pc） （4225KB）（112）       Knowledge map    Save In order to study the high-precision,continuous,and real-time monitoring of building deformation by BDS-3,this paper conducts research based on the BDS-3 system and the CORS network.It utilizes the NTRIP protocol to transmit monitoring data in real time,employs the precise point positioning (PPP)technology to calculate coordinates in real time,and uses GAMIT/GLOBK for static differential calculation,thus realizing millimeter-level deformation monitoring.On this basis,a real-time deformation monitoring system has been developed.Experiments show that the mean value of the normalized root mean square (NRMS)for baseline calculation over 60 days is 0.1917,and the repeatability precision is better than 2mm.The weighted root mean square (WRMS)of the coordinate time series is approximately 1mm,and the static calculation reaches the millimeter level.The horizontal precision of the dynamic calculation is 5mm and the vertical precision is 13mm.It has been verified that BDS-3 system can provide solutions for the monitoring of urban buildings and has engineering application value. Reference | Related Articles | Metrics