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Geo-Agent: a framework for intelligent geographic information systems with natural language interaction LIANG Hailei, WANG Yong, DU Kaixuan, ZHOU Weixiang Bulletin of Surveying and Mapping    2025, 0 (10): 114-118,126.   DOI: 10.13474/j.cnki.11-2246.2025.1019 Abstract （573）      PDF（pc） （3350KB）（140）       Knowledge map    Save Traditional geographic information systems (GIS)often encounter multiple challenges in the human-computer interaction process, such as cumbersome operation procedures and limited intelligence.With the rapid development of general artificial intelligence technology, new engines centered on generative AI are driving the geographic information industry to accelerate its evolution from digitalization to intelligence.Typical practices include innovative research such as Autonomous GIS, MapGPT, and LLM-Find.Existing studies have confirmed the huge potential of large language models (LLMs)in tasks such as GIS knowledge Q&A and map-making.However, current research still has the following limitations: on the one hand, the models lack the ability to autonomously understand geographic information data and perform complex spatial task analysis; on the other hand, they highly rely on the task parsing and code generation capabilities of the large models themselves.In addition, the API calling mode may lead to the risk of privacy and sensitive geographic data leakage.To address these challenges, this paper innovatively proposes a geographic information intelligent agent, Geo-Agent, based on an open-source architecture.This framework proposes a multi-level instruction parsing strategy based on spatial thinking chains and a data retrieval strategy oriented to graph structures, effectively solving the problems of geographic semantic understanding deviation and spatial logic disconnection.Experimental verification shows that Geo-Agent can understand, manage, and deeply analyze geographic information data, and can complete complex spatial analysis tasks through natural language interaction, providing an innovative path for realizing fully autonomous and intelligent next-generation geographic information systems. Reference | Related Articles | Metrics

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Real-scene 3D data supported UAV low-altitude route planning for complex urban environments YU Zhonghai, YANG Na, WANG Lu, LI Xin, ZHOU Changjiang, DUAN Longmei Bulletin of Surveying and Mapping    2025, 0 (10): 127-132.   DOI: 10.13474/j.cnki.11-2246.2025.1021 Abstract （383）      PDF（pc） （4588KB）（135）       Knowledge map    Save Low-altitude route planning and infrastructure constitute important research directions in the low-altitude economy.To further enhance the application of real-scene 3D modeling in urban digital transformation and provide a sustainable 3D digital foundation for low-altitude economic growth, this paper proposes a low-altitude route planning algorithm based on RRT+Floyd, supported by real-scene 3D data, tailored for complex urban environments.Experiments are conducted in the eastern high-tech zone and northern built-up area of Jinan city, evaluating route planning at four altitudes (30, 80, 120, and 300 m).Five metrics(route length, node count, smoothness, average turning angle, and maximum turning angle)are compared with the traditional RRT algorithm.Results show that the proposed algorithm significantly improves planning efficiency and speed in low-altitude airspace, particularly in class W airspace. Reference | Related Articles | Metrics

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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 （366）      PDF（pc） （9810KB）（232）       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 （327）      PDF（pc） （4233KB）（202）       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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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 （303）      PDF（pc） （3251KB）（98）       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 （299）      PDF（pc） （4117KB）（104）       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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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 （296）      PDF（pc） （2887KB）（107）       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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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 （287）      PDF（pc） （2906KB）（123）       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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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 （285）      PDF（pc） （2443KB）（79）       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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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 （282）      PDF（pc） （2770KB）（122）       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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Analysis of spatio-temporal changes and driving factors of glaciers in the Qilian Mountains of Gansu province JING Hongxia, LIU Yushuo, LI Xia, CAI Xiqin, HU Xiaojuan Bulletin of Surveying and Mapping    2025, 0 (10): 157-162.   DOI: 10.13474/j.cnki.11-2246.2025.1026 Abstract （280）      PDF（pc） （3524KB）（62）       Knowledge map    Save Aiming at the problem that the existing research on the changes and influencing factors of glaciers in the Qilian Mountains of Gansu province is insufficient, this paper uses the high-resolution remote sensing images in 2013, 2019 and 2023 to monitor glacier changes based on the second glacier inventory data, and uses the geographic detector to analyze driving factors.The results indicate:①From 2006 to 2023, the number of glaciers increased by 301, the area decreased by 105.07 km 2.The average annual retreat rate was 0.83 %, and it continued to accelerate.②In the area with small scale, low altitude and steep terrain, the glacier retreats quickly, and the southwestward glacier retreats the fastest, reaching 21.45%.③The increase of temperature changes the thermal balance, which is the key factor for glacier retreat.Slope affects glacier movement and solar radiation promotes ablation, which is the main driving factor.In addition, pollutants such as PM 10 produced by increased human activities in recent years have changed the physical properties of glacier surface and enhanced the ablation process.In order to slow down the glacier retreat, it is recommended to control the discharge of cooling, reduce human pollution emissions, and strengthen the protection of glaciers in complex terrain areas. 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 （276）      PDF（pc） （9409KB）（119）       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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Ground penetrating radar multi-attribute fusion for multi-target detection of tunnel lining ZHAO Liang, TANG Luyi, LIU Shipeng Bulletin of Surveying and Mapping    2025, 0 (10): 30-35.   DOI: 10.13474/j.cnki.11-2246.2025.1006 Abstract （276）      PDF（pc） （4283KB）（82）       Knowledge map    Save This paper addresses the issues of complex ground penetrating radar (GPR)image features and low accuracy in defect detection by proposing a multi-attribute fusion method for tunnel lining defect detection.By extracting instantaneous amplitude, instantaneous phase, and instantaneous frequency attributes of radar signals, combined with wave-particle duality theory to design a Wave module as the backbone network, a multi-modal feature fusion framework is constructed.The method employs a pyramid structure to extract low-level and high-level semantic features in layers, and introduces a lightweight MLP architecture to optimize network dynamics and computational efficiency.Experimental results demonstrate that the model fusing instantaneous attributes with the Wave module achieves a mean average precision (mAP)of 91.7%in cavity, loose, and steel detection tasks, an improvement of 3.8%compared to the baseline YOLOv8 model.Ablation experiments and comparative analysis verify the effectiveness of the multi-attribute fusion strategy and the Wave module in enhancing feature expression capability, providing a new approach for precise non-destructive detection of tunnel lining defects. Reference | Related Articles | Metrics

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A lightweight remote sensing image semantic segmentation method based on CBAM enhancement ZHAO Xiaozu, GOU Changlong, YANG Yang Bulletin of Surveying and Mapping    2025, 0 (10): 36-42.   DOI: 10.13474/j.cnki.11-2246.2025.1007 Abstract （275）      PDF（pc） （3403KB）（126）       Knowledge map    Save This study addresses the challenges in high-resolution remote sensing image semantic segmentation, such as large variations in object scales, blurred boundaries, and spectral similarity.A lightweight segmentation model is proposed, which integrates multi-scale features and dual attention mechanisms.The model is based on SegNeXt, incorporating a convolutional block attention module (CBAM)into its multi-scale convolutional attention network to refine feature representations through channel and spatial dual attention mechanisms.During the decoding stage, the Hamburger structure is used to integrate mid-to-high-level semantic information.Experiments on the GF-2 remote sensing image dataset show that the model achieves noticeable improvements over the original SegNeXt across various metrics, with particularly superior performance in handling fuzzy boundaries and linear feature categories.The results demonstrate that this method achieves a balance between accuracy and efficiency while maintaining a lightweight design, offering a feasible solution for real-time semantic interpretation of remote sensing images in resource-constrained environments. 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 （273）   HTML （41）    PDF（pc） （1684KB）（171）       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 （268）      PDF（pc） （17688KB）（105）       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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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 （258）      PDF（pc） （5048KB）（116）       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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Monitoring of sluice subsidence along the Yangtze River based on time-series InSAR technology WANG Yihong, SHI Yifan, WU Yongfeng, LIANG Wenguang Bulletin of Surveying and Mapping    2025, 0 (10): 119-126.   DOI: 10.13474/j.cnki.11-2246.2025.1020 Abstract （257）      PDF（pc） （16163KB）（84）       Knowledge map    Save Sluice play an important role in disaster prevention and mitigation, shipping and transportation, as well as agricultural irrigation.It is significant to strengthen the settlement monitoring of sluice to ensure their safe and stable operation.The settlement monitoring of Dongxingang sluice and Dongjiajiang sluice was carried out using PS-InSAR technology to extract the time-series settlement results of the sluices from 2016 to 2020.The results were then compared with those obtained using the SBAS-InSAR method.Finally, the causes of sluice settlement were discussed.The results indicated that Dongxingang sluice and Dongjiajiang sluice settled at a rate of -5.00 and -3.37 mm/a, respectively; By comparing the monitoring results of PS-InSAR and SBAS-InSAR, it can be seen that the annual deformation rate differences for both sluice gates are within 2 mm, and the coefficient of determination for the time-series settlement monitoring results of both methods is above 0.77, indicating a high degree of consistency and reliability between the two methods.The soil texture type dominated by powdery loam and the special topographic features of Hissing Horse Bend may affected the stability of the sluice.Decreases in groundwater levels and sluice water levels may aggravate the settlement of the sluice.Additionally, high-density urban development impacted soil stress, potentially causing settlement in the sluice area. Reference | Related Articles | Metrics

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Intrinsic error analysis of sea surface significant wave height retrieval from FY-3E GNSS-R based on neural network inversion YU Hui, DU Qifei, XIA Junming, HUANG Feixiong, YIN Cong, BAI Weihua Bulletin of Surveying and Mapping    2025, 0 (10): 100-105,132.   DOI: 10.13474/j.cnki.11-2246.2025.1017 Abstract （252）      PDF（pc） （1715KB）（78）       Knowledge map    Save The global navigation satellite occultation sounder Ⅱ (GNOS Ⅱ), carried by the Fengyun-3 satellite, has achieved operational products such as sea surface wind speed, soil moisture, sea ice coverage and sea ice thickness based on the global navigation satellite system reflectometry (GNSS-R)data acquired in orbit.This study employs neural network (NN)technology to develop an SWH inversion model using Beidou Navigation Satellite System reflectometry (BDS-R)and Global Positioning System reflectometry (GPS-R)data provided by GNOS Ⅱ on the FY-3E satellite.A triplet comparison analysis method is adopted to compare and analyze the inherent errors in SWH inversion based on BDS-R and GPS-R data.The research results indicate that the inversion accuracy of BDS-R for significant wave height, assessed solely with data from the European Centre for Medium-Range Weather Forecasts (ECMWF)is 0.43 meters, compared to 0.46 meters for GPS-R.When validated independently using buoy data from the National Data Buoy Center (NDBC), the inversion accuracies for BDS-R and GPS-R are 0.45 and 0.50 meters, respectively.Using the triplet comparison analysis method, the inherent errors in SWH inversion for BDS-R and GPS-R are estimated to be 0.40 and 0.43 meters.This method effectively reduces the impact of inherent errors in the comparison data on the assessment results.Overall, the inversion accuracy of BDS-R for SWH is approximately 7%better than that of GPS-R.The findings of this study provide a reference for the operational application of SWH retrievals from FY-3 GNOS Ⅱ. 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 （250）      PDF（pc） （10148KB）（133）       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