Table of Content

25 March 2026, Volume 0 Issue 3

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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

2026, 0(3):  1-6.  doi:10.13474/j.cnki.11-2246.2026.0301

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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.

Positioning of image targets of smartphone based on BDSBAS and monocular ranging

MA Xiangtai, WU Ting, ZHENG Zenan, XIN Zhenyu, CHEN Jiahui

2026, 0(3):  7-12,19.  doi:10.13474/j.cnki.11-2246.2026.0302

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To address the insufficient performance analysis of smartphone image target localization,this paper proposes a smartphone image target localization method based on BDSBAS and monocular ranging.BDSBAS-enhanced data is used to improve the positional accuracy of the smartphone as the starting point.YOLOv5 target detection and the principle of similar triangles are used to measure the distance to the target.Yaw angle information from the phone's orientation sensor is fused to determine the target's azimuth angle,thus constructing a forward model for image target coordinate calculation.Experiments show that after BDSBAS enhancement,the horizontal positioning accuracy of a OnePlus 13/vivo X80 phone is within 5 m,and the static and dynamic horizontal positioning accuracy is improved by 16.09% and 36.97% respectively compared to single-point positioning.The monocular ranging accuracy for image targets is within 3 m,and the close-range accuracy can reach the centimeter level.The proposed method can achieve non-contact image target localization relying solely on the smartphone's own sensors.The target planar positioning accuracy of approximately 6 m is comparable to the single-point positioning accuracy of the smartphone.

Positioning technology integrating BeiDou satellite and 5G observation data

XIE Yuchen, HOU Beibei, QIU Lan

2026, 0(3):  13-19.  doi:10.13474/j.cnki.11-2246.2026.0303

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To address the issue of reduced positioning performance of the single BeiDou Navigation Satellite System (BDS) when the number of visible satellites is insufficient,a fusion positioning algorithm combining the BDS and raw 5G observation data is proposed.Firstly,a fusion positioning algorithm model based on the BDS and 5G is established.Secondly,the least squares method results are imported into the Kalman filter and combined with prediction and observation information for analysis to obtain the final positioning result.Finally,the positioning performance of the proposed fusion positioning strategy is experimentally tested and evaluated under three occlusion environments: mild,moderate,and severe.The experimental results show that in the mild occlusion environment,the root mean square error (RMSE) is reduced by more than 50%; in the moderate occlusion environment,the accuracy is improved by nearly 80%; and in the severe occlusion environment,even when the BeiDou satellite signal is limited,the fusion system can still provide continuous high-precision positioning with an RMSE as low as 1.412 m.Increasing the number of 5G base stations can further optimize the positioning effect,especially in severe occlusion environment.

Analysis of temporal application of BDS PPP around antarctica

TANG Jun, WU Duansong

2026, 0(3):  20-25,43.  doi:10.13474/j.cnki.11-2246.2026.0304

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To address the practical application issues of China's BeiDou Satellite Navigation System(BDS) in the antarctic region.This paper utilizes precise point positioning (PPP) techniques to process BDS observational data from five tracking stations surrounding antarctica over a continuous five-year period,analyzing BDS PPP positioning performance and coordinate time series variations in the antarctic region.The experimental results indicate that the BDS satellite constellation status in the antarctic region is favorable,with dual-frequency PPP horizontal positioning accuracy better than 1 cm and elevation positioning accuracy better than 1.5 cm.After denoising,the trends in coordinate changes in the X,Y,and Z directions are noticeable.BDS dual-frequency precise point positioning technology can obtain high-precision continuous coordinate sequences for antarctic tracking stations.Based on the trends in coordinate changes in three directions,it is possible to infer variations in ice flow velocity and mass in the surrounding area,providing a new technological method for future studies on polar ice and snow changes.

Spatio-temporal variation of snow depth and its influencing factors based on microwave remote sensing: a case study of the Brahmaputra River basin

BAI Shuying, ZHU Yanggui, XIE Tao, ZHANG Hui, PENG Da

2026, 0(3):  26-31.  doi:10.13474/j.cnki.11-2246.2026.0305

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Snow and ice meltwater account for more than 30% of the total runoff in the Brahmaputra River basin,making quantitative research on the spatio-temporal distribution of snow cover highly significant for the construction and operation of hydropower projects.This study employed a downscaling approach utilizing snow cover fraction data to reconstruct low-resolution snow depth products.Based on the downscaled data,the spatio-temporal distribution and variation characteristics of snow depth in the Brahmaputra River basin from 2000 to 2019 were analyzed,and the influences of topographic and climatic factors on snow depth were further examined.The results reveal a significant decreasing trend in snow depth across the basin during 2000—2019,with notable spatial heterogeneity.Deep-snow areas were more sensitive to climate change.Among topographic factors,elevation and slope significantly influenced snow distribution,while aspect had a relatively limited impact.Snow depth exhibited a stronger response to precipitation changes than to temperature variations.The findings of this study provide a scientific basis for optimizing the operation of hydropower systems and assessing snow and ice resources under climate change in the Brahmaputra River basin.

Registration algorithm for heterogeneous point clouds based on building contour features

LIU Yunxuan, ZOU Jingui, ZHAO Yinzhi, HE Yifeng, LIU Wenqin, WANG Na

2026, 0(3):  32-37.  doi:10.13474/j.cnki.11-2246.2026.0306

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In the coarse registration of heterogeneous point clouds in architectural scenes,challenges such as large data volume,feature confusion,and severe interference from outliers hinder registration accuracy.To address these issues,this paper proposes a lightweight coarse registration algorithm based on building contours.First,the algorithm accurately extracts key contour points of buildings by leveraging the eigenvalue ratio of the point cloud normal distribution matrix.Then,it replaces traditional normals with the principal direction of points to compute fast point feature histograms(FPFH),and filters out incorrect correspondences using bidirectional consistency and geometric consistency constraints.Finally,the Geman-McClure function is used to construct a robust objective function to achieve accurate estimation of the transformation matrix.Experimental results demonstrate that the proposed algorithm outperforms existing methods in terms of registration accuracy and overlap ratio,thereby validating its effectiveness and reliability in coarse registration of heterogeneous point clouds in architectural scenes.

Single tree segmentation from LiDAR point cloud by bidirectional growth

LIN Lei, HUI Zhenyang, TU Liping, FAN Junlin, MAO Yaqin, HUI Ting

2026, 0(3):  38-43.  doi:10.13474/j.cnki.11-2246.2026.0307

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To address the issue that traditional individual tree segmentation methods are susceptible to the accuracy of tree apex extraction,this paper proposes a bidirectional growth-based individual tree segmentation method using LiDAR point cloud data.The approach initially calculates the centers of multi-layer slices of trunk point cloud to perform linear fitting,thereby identifying the trunk axis and its intersection point with the ground to determine the tree position.Subsequently,based on the tree position,clustering is progressively conducted from bottom to top to locate the tree apex corresponding to each individual tree.Finally,a top-down progressive growth strategy is employed to segment individual tree point clouds.To validate the effectiveness of the proposed method,experiments were conducted in three distinct forest environments.The experimental results demonstrate F1 scores of 0.971,0.886,and 0.865 for the individual tree segmentation results in the three sample plots,respectively.Compared to conventional methods,our approach achieves the optimal individual tree extraction rate,matching rate,and the lowest omission error.These findings indicate that the proposed bidirectional growth individual tree segmentation method yields more accurate results and exhibits strong robustness.

Building function classification: a multimodal geospatial data fusion approach

YANG Yuting, HU Ting, PAN Ziyong, TONG Xudong, MA Ailong

2026, 0(3):  44-50.  doi:10.13474/j.cnki.11-2246.2026.0308

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Detailed classification of building function provides scientific support for urban planning and digital city modeling.However,existing researches primarily focus on identifying the geometric attributes of buildings based on high-resolution remote sensing images,paying less attention to their functional attributes,and largely ignoring the increasingly popular mixed-use buildings.To address this,this paper introduces multi-source geospatial data,including building vector geometry data,building vector environmental data,and POI data,to compensate for the limitations of optical,nighttime light,and SAR remote sensing images in depicting building uses.A multi-modal data fusion model,MultiMixNet,is proposed to achieve simultaneous identification of both single and mixed-use buildings.Experimental results in urban areas of Shanghai,Hangzhou,and Xi'an show that the MultiMixNet method achieves an average Micro F1 score of 76.63% and an average Macro F1 score of 71.47% across the three study areas.The identification accuracy for mixed-use buildings generally exceeds 60%.The nighttime light data significantly improving the classification accuracy of commercial buildings by reflecting nighttime activity characteristics,while building vector environmental data provides crucial support for distinguishing between residential and mixed-use commercial-residential buildings.To address the issue of mixed-use buildings being far fewer in number than single-use buildings,this paper designs a building buffer zone extraction module based on the object detection framework to enhance the sample proportion of minority categories,effectively alleviating data imbalance and improving the classification accuracy of mixed-use buildings.

Study on the spatio-temporal distribution of Cladophora in the Shaliu River estuary of Qinghai Lake and its response to the lake area and meteorological elements

YIN Wanling, ZHANG Zhijun, ZHANG Yin, YUAN Shuai, HU Qingwu, XU Wenting, LI Zhijun

2026, 0(3):  51-56.  doi:10.13474/j.cnki.11-2246.2026.0309

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As China's largest inland saltwater lake,the distribution of Cladophora in Qinghai Lake has shown dynamic changes in response to climate change.This study selected the estuarine area of Shaliu River in Qinghai Lake to carry out detailed research.Based on the Google Earth Engine (GEE) platform,monthly cloud-free Sentinel-2 images during the non-icing period from 2022 to 2024,as well as TerraClimate and ERA5-Land meteorological data,were acquired.For the RGB-NDWI fused images,the WEU-Net model and the CSI method were used to identify Cladophora.Additionally,the spatiotemporal distribution characteristics of Cladophora and its response relationships with lake water area and meteorological factors were analyzed.The results show that there is a significant positive correlation between the area of Cladophora and the lake area,with a Pearson correlation coefficient of 0.76.Both the area of Cladophora and the lake area increase from April to September,and then decrease after reaching the peak in September.Cladophora is mainly distributed at the edge of the lake area and in the bays,and its outer boundary is consistent with the lake boundary.There are significant positive correlations between the area of Cladophora and precipitation,air temperature,and lake mixed layer temperature,and a significant negative correlation with wind speed.The order of the correlation intensity is lake mixed layer temperature,wind speed,air temperature,precipitation.This study reveals the synergistic driving mechanism of hydrometeorological factors on the distribution of Cladophora.

CSG-Net: a method for building footprint extraction from remote sensing images by integrating domain adaptation and the visual foundation model SAM

WANG Ye, ZHANG Xinchang, JIANG Ming, RUAN Yongjian

2026, 0(3):  57-61.  doi:10.13474/j.cnki.11-2246.2026.0310

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To address the substantial decline in generalization performance of deep learning models for building footprint extraction across different platforms and spatial resolutions caused by inter-domain distribution inconsistency,this study introduces a cross-scale geometric-refined network (CSG-Net) aimed at enhancing both adaptability and extraction accuracy in unlabeled target domains.The proposed approach establishes a cascaded probabilistic-geometric pseudo-label refinement framework.Firstly,pseudo-label uncertainty is quantified by computing the Jensen-Shannon divergence (JSD) between the dual prediction branches of the model,and a probabilistic weighting scheme is applied to suppress noise in unreliable regions.Subsequently,geometric priors derived from the segmentation results of the segment anything model (SAM) are incorporated to perform explicit geometry-constrained refinement of pseudo-label boundaries based on overlap ratio analysis,thereby generating high-quality training targets.Extensive experiments on challenging cross-scale building extraction tasks demonstrate that CSG-Net achieves an intersection over union (IoU) of 73.05%,markedly surpassing the Baseline (52.49%) and outperforming other state-of-the-art domain adaptation approaches.These findings confirm the effectiveness of the proposed framework in improving cross-domain robustness and extraction accuracy.

Decision tree-adjusted long time-series land cover classification for Hangzhou bay reclamation areas

YE Jinyang, XU Yuying, LI Yixin, XU Rouyi, XU Cundong

2026, 0(3):  62-67,74.  doi:10.13474/j.cnki.11-2246.2026.0311

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Due to the reliance on medium and low-resolution historical remote sensing images,the classification results of long-term land cover sequences often have low accuracy or are not fully applicable to certain areas.Therefore,this study proposes an improved land cover classification method based on training decision trees to reveal the drastic changes in land cover in the Hangzhou bay under the interweaving of intense human activities and natural succession.The study firstly uses the synthetic image classification based on the preprocessed bands and multi-index inversion results,modifies the region of interest of the image to be classified on the basis of the classification results of the pre-trained decision tree model,and then obtains the classification results through supervised classification methods and verifies them.The 30 meter resolution land cover classification results of the Hangzhou bay from 2000 to 2024 show that the overall average accuracy of this method reaches 91.72%,and the Kappa coefficient is 0.91.Further analysis indicates that the land structure evolution in the study area can be divided into three stages: the stage dominated by grain production (2000—2007),the dynamic development stage (2007—2015),and the ecological restoration stage (2015—2024).

Research on canopy height inversion based on Sentinel-1/2 spatio-temporal fusion model

ZHANG Wenwen, ZHOU Wei, WANG Jie

2026, 0(3):  68-74.  doi:10.13474/j.cnki.11-2246.2026.0312

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To address the limitations in forest canopy height estimation,including low efficiency and high costs of field monitoring,insufficient resolution of single-source remote sensing data,and lack of underlying surface parameters.This study proposes a multi-source spatio-temporal fusion deep learning model named SST-CLT,based on a ConvLSTM-Transformer hybrid neural network.The model integrates Sentinel-1 time-series SAR data,Sentinel-2 multi-spectral data,and airborne LiDAR-derived canopy height reference data to enhance inversion accuracy through collaborative spatio-temporal-spectral feature modeling.The results demonstrate excellent performance: the training set achieved R²=0.93,RMSE=3.10 m,and MAE=2.39 m,while the validation set yielded R²=0.84,RMSE=4.54 m,and MAE=3.00 m.The overall sample set reached R²=0.89,RMSE=3.81 m,MAE=2.65 m,with generated canopy height maps accurately characterizing spatial heterogeneity.The SST-CLT model exhibits both high precision and strong generalization capability,providing reliable technical support for forest resource dynamic monitoring and ecosystem studies.

High-precision trajectory and pose estimation for integrated railway measurement system platforms

LI Min, ZHOU Letao

2026, 0(3):  75-79.  doi:10.13474/j.cnki.11-2246.2026.0313

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To meet the operational demands of intelligent and precise maintenance in railway infrastructure,this paper proposes a multi-source heterogeneous data fusion framework based on GNSS+IMU+odometer integrated positioning and orientation technology.A novel odometer joint tightly coupled (OJTC) algorithm is introduced to address the challenges of pose estimation accuracy under limited satellite visibility (fewer than four satellites) and to accelerate ambiguity resolution after long-term GNSS signal loss.Experimental results demonstrate that the OJTC algorithm significantly outperforms conventional methods in terms of position,velocity,and attitude estimation accuracy.Moreover,the average ambiguity fixing time after signal outage is reduced to 9 s,achieving improvements of 57.1%,47.1%,and 35.7%compared to RTK,loose couple(LC),and tight couple(TC) methods,respectively.The proposed framework enables high-precision trajectory computation for railway measurement platforms,providing a reliable spatial foundation for intelligent diagnosis and precise maintenance of track geometry.

Research on construction configuration deviation detection of large-span spatial structures based on non-rigid registration

LI Dong, LI Baoluo, YANG Xianzhi, LIU Yufei, LIU Yuzhi, ZHANG Qian

2026, 0(3):  80-85.  doi:10.13474/j.cnki.11-2246.2026.0314

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The detection of construction configuration deviations in large-span spatial structures is critical for construction quality control.However,conventional point cloud methods based on terrestrial laser scanning often suffer from limited robustness and applicability when dealing with complex structural systems and variable construction environments.This paper proposes a non-rigid registration method that eliminates structural responses and uses the steel roof structure of a certain international airport's terminal as an engineering background to compare the performance of the method and detect the construction configuration deviation of the structure.The results show that the proposed method can accurately identify and segment all structural instances.Compared with the classic non-rigid method,the RMSE of semi-rigid forward registration is reduced by 2.8 mm,and the RMSE of rigid reverse registration is reduced from 5.0 to 0.4 mm.The convergence speed is faster,the robustness is stronger,and the construction configuration deviation of the joints does not exceed 0.1 m.This study solves the application problem of non-rigid registration in the detection of construction configuration deviation,providing an effective technical means for the construction quality inspection of large-span spatial structures and promoting the intelligent development of construction quality management.

Floating bridge deformation monitoring and prediction based on multi-frequency GNSS data

LIU Jun, OU Tonggeng, GUO Xiaofei

2026, 0(3):  86-93,99.  doi:10.13474/j.cnki.11-2246.2026.0315

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To address the limitations of traditional methods in monitoring multi-frequency GNSS deformation of floating bridges regarding fluid-structure interaction and nonlinear problem-solving,the insufficient physical credibility of purely data-driven deep learning approaches,the impacts of marine multipath effects and ionospheric delays on multi-frequency GNSS observations,and the inadequate fusion of multi-sensor data,this study proposes a physics-informed neural network (PINN) framework integrating multi-frequency GNSS observations with structural dynamics equation constraints.Partial differential equations describing fluid-structure interactions,structural vibrations,and conservation laws are embedded into the neural network's loss function,constructing an end-to-end model featuring spatiotemporal adaptive multi-scale weighting and marine error correction.Validation using long-term monitoring data from the Bergsøysund Floating Bridge demonstrates:Compared to Kalman filtering,the root mean square error (RMSE)during normal monitoring is reduced by 35%~47%;Compared to purely deep learning methods,training data requirements decrease by 1~2 orders of magnitude,and generalization capability improves by over 60%;After multi-frequency fusion,the L5 band exhibits a 40% enhancement in multi-path error resistance,achieving an overall positioning accuracy of 0.8 cm.This framework provides a novel solution for intelligent deformation monitoring and prediction of structures in complex environments.

Traffic perception method for operational bridges based on DAS fiber optic monitoring

QU Yinghao, HOU Yabin, TIAN Hailang, WANG Shidi, OUYANG Mingming, ZHANG Jinghao

2026, 0(3):  94-99.  doi:10.13474/j.cnki.11-2246.2026.0316

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To achieve real-time dynamic acquisition of bridge traffic conditions across the entire area,the Jialing River Shimen bridge in Chongqing is taken as the research object in this paper,and a research on urban bridge traffic perception method based on distributed acoustic sensing (DAS) fiber optic monitoring is carried out.A DAS fiber optic monitoring system is deployed,and the installation and layout process of distributed fiber optic cables in the bridge body is proposed.At the same time,we have assisted in arranging fiber Bragg grating (FBG) sensors to verify the DAS fiber optic monitoring results.The study demonstrates that: DAS systems can achieve high-precision dynamic perception of traffic loads across bridges,accurately capture vehicle position,trajectory,and speed information,and distinguish between heavy vehicles and sedans through vibration amplitude.DAS fiber optic monitoring senses the complete traffic situation of the bridge within a week,and overall shows the characteristics of large amplitude and high frequency of wave peaks during morning and evening peak hours and working days,which it is consistent with the actual situation.The regularity of DAS monitoring data is consistent with the FBG monitoring results,and the acceleration amplitude of the two monitoring results is basically consistent,which it verifies the effectiveness of DAS monitoring system in bridge traffic monitoring.

Application of LT-1 SAR data to deformation monitoring and DEM inversion in the mining area

ZHANG Chuang, FANG Ping, YAO Xinyu, DONG Ruxu, BU Wenchao, LI Haodong, WANG Zhixiong, LIU Ying, YUE Hui

2026, 0(3):  100-105,123.  doi:10.13474/j.cnki.11-2246.2026.0317

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Efficient monitoring of surface deformation in mining areas is vital for disaster prevention and control and ecological protection.This paper takes the open-pit mine and underground mine of Dongsheng coalfield as the research object.Based on the L-band SAR satellite data of LT-1,combined with D-InSAR and InSAR technologies,the monitoring of surface subsidence in the mining area and the inversion of DEM are achieved,and the accuracy is verified through the measured elevation and unmanned aerial vehicle data.The results show that: ①The LT-1 data can effectively capture large gradient deformations (>1 m),the subsidence range of open-pit mines is wide (up to 39 cm),the settlement depth of underground mines is large (up to 135 cm),and the deformation points migrate dynamically along with the mining and excavation directions.②Under the condition of no control points,93% of the elevation difference between LT-1 DEM and unmanned aerial vehicle DEM is concentrated within ±5 m,meeting the mapping standard of 1∶50 000.③The dual-star formation mode significantly enhances the monitoring efficiency,and the 4 d revisit cycle can support dynamic deformation tracking in the mining area.This study confirms the advantages of LT-1 data in deformation monitoring and high-precision terrain reconstruction in complex mining areas,providing reliable technical means for mine safety and ecological restoration.

Visual measurement disturbance compensation by integrating multi-dimensional environmental features

CHEN Boyu, LIU Xinlin, ZHU Song, YUAN Pengpeng, MENG Fanyi, HUA Yuansheng, ZHU Jiasong

2026, 0(3):  106-111.  doi:10.13474/j.cnki.11-2246.2026.0318

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The accuracy and stability of high-precision outdoor visual measurement systems are often significantly degraded by the complex coupling and time-lag effects of environmental factors such as temperature,humidity,and illumination.To address this challenge,this paper proposes a data-driven disturbance compensation framework that integrates multi-dimensional environmental features.The method first identifies key environmental disturbance sources through systematic analysis and innovatively introduces a time variable to capture the diurnal periodicity of measurement errors,thereby enabling the construction of a high-precision compensation model.Experimental results demonstrate that temperature,humidity,and illumination are primary influencing factors,and their effects exhibit significant time lags.Introducing time variable is critical for enhancing model performance,improving the compensation accuracy of non-linear models by approximately 30%,which significantly outperforms linear models and achieves sub-pixel level accuracy.This study validates the effectiveness of the proposed framework,providing theoretical basis and practical pathway for solving the environmental adaptability problem in long-term outdoor visual measurement systems.

Automatic reconstruction method of 3D white models of buildings based on monocular remote sensing imagery

QIU Zhiwei, QIN Jing, XIANG Qianjin, WANG Chenxi

2026, 0(3):  112-117,129.  doi:10.13474/j.cnki.11-2246.2026.0319

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With the development of real-world 3D,digital twin cities and smart cities,automatic building extraction and 3D modeling are of great significance in urban planning and visualization.In order to solve the problem that the existing methods rely on DSM or manual operation,a method of automatic reconstruction of three-dimensional white model of buildings based on monocular remote sensing images is proposed.In this method,which only relies on a single high-resolution satellite image,the high-precision segmentation of the building footprint and the roof is achieved through the improved U-Net model that fuses the CBAM attention mechanism and the SMU activation function,and then the building elevation is estimated by using the geometric relationship between the roof offset and the satellite image.Finally,the CGA modeling language is used to generate a 3D white model model of the building.Experimental results show that the overall accuracy of the improved model is 96.46%and 98.4%,and the mIoU is 90.93%and 92.15%,respectively,on the UBC and WHU datasets.In the elevation estimation experiment of 45 buildings,the average absolute error was 1.061 8 m,and 93.33%of the buildings had an error of less than 2 m.It verifies the feasibility and engineering application value of the method.

The models spectrum of nautical chart service in the intelligent era

WANG Zhao, BAI Tingying, REN Xiaodong, REN Fu

2026, 0(3):  118-123.  doi:10.13474/j.cnki.11-2246.2026.0320

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In the intelligent era,nautical chart services are facing novel challenges arising from the dual trends of highly integrated functions and highly differentiated applications.Constructing a model spectrum of nautical chart services is of significant research and practical value for precisely fulfilling the diverse requirements of multi-level users.This paper systematically reviews the developmental trends of nautical chart services.Subsequently,it establishes a models spectrum for nautical chart services in the intelligent era.An in-depth analysis is conducted on the characteristics of five current mainstream chart service products,namely electronic nautical charts/nautical charts,web-based chart services,scientific data cartography,thematic marine charts,and public cartography & marine information graphics.Moreover,the paper explores the impacts of artificial intelligence technology on these service modes.The established models spectrum demonstrates a good adaptation to the user needs spectrum,facilitating effective alignment and matching between the supply and demand sides.The models spectrum offers a reasonable theoretical and application framework for the development of nautical chart services in the intelligent era.

Multi-modal fusion-based 3D reconstruction of power corridor using 3DGS

ZHOU Chao, GAO Shuhan, TU Diantao, LIU Yangdong, LIU Hui, SHEN Hao, JIA Ran, LIU Chuanbin, ZHANG Yang, LIU Rong, SHEN Shuhan

2026, 0(3):  124-129.  doi:10.13474/j.cnki.11-2246.2026.0321

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Due to the limitations of LiDAR,such as a small field of view and insufficient density of sampling points,the 3D point cloud of power corridors reconstructed on the ground based on LiDAR is prone to information missing.To address this issue,this paper proposes a multi-modal fusion reconstruction method based on Gaussian splatting.Firstly,a SLAM system that fuses vision and LiDAR is adopted to achieve accurate estimation of sensor pose.Secondly,3D Gaussian primitives are initialized,and RGB images and depth maps are rendered through 3D Gaussian splatting (3DGS) technology.Finally,the Gaussian primitives are dynamically updated and the final results are generated by optimizing color and depth errors.Experimental results show that this method can effectively compensate for LiDAR blind areas,complement the reconstruction results using image information,and is superior to traditional methods in both model integrity and accuracy.This paper verifies the effectiveness of 3DGS in multi-modal 3D reconstruction,providing a new and effective approach for 3D reconstruction of power corridors.

Fusion application of multi-source data in complex river section surveying technology

XIANG Tao, WANG Chuanguang, XU Zhangping, WU Bowen, ZHENG Zumei

2026, 0(3):  130-136.  doi:10.13474/j.cnki.11-2246.2026.0322

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To explore the fusion application of multi-source heterogeneous data in shallow water river section mapping technology with abundant aquatic plants and green trees,and to address the difficulty of extracting river sections with high precision and efficiency,a method is proposed to use high-precision DEM of river channels to extract cross-sectional scatter points for cross-sectional processing.This method uses optical satellite remote sensing images to pre-layout the cross-sectional line of the river channel.A UAV is used for low altitude aerial photography and point cloud scanning measurement of the river channel.For smooth and flat water areas without grass,a single beam depth sounder is used on an unmanned ship for water depth measurement.For shallow water areas with more grass in the river,GNSS RTK is used for underwater terrain point acquisition.Using processed data fusion to generate a water land integrated DEM with a resolution of 0.2 m,transverse scatter points with a spacing of 200 m are extracted.Any point is selected from the transverse scatter points with three-dimensional coordinates as the endpoint to calculate the station number and offset.Based on the endpoint coordinates and the perpendicular line drawn from the endpoint to the centerline of the river channel,the observation direction of the cross-section is considered.The offset distance between the endpoint and the transverse scatter points is calculated to draw the cross-section map.The total station is used to conduct high-precision inspection of the surveying results,and the error of 168 cross-sectional points is calculated to be 0.065 m.The quality level of the measurement results is evaluated,and the quality element score is 94.9 points,indicating that the quality level is rated as excellent.

Optimized path planning strategies for UAV ultraviolet inspection using enhanced A^* and genetic algorithms

JI Shuolei, CHEN Hailin, LI Yucheng, HUANG Hengying

2026, 0(3):  137-141.  doi:10.13474/j.cnki.11-2246.2026.0323

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To enhance the efficiency and reliability of UAV-based ultraviolet (UV) inspection in smart grids and address the shortcomings of traditional path planning methods in complex terrains and discharge source regions,this paper proposes a path optimization strategy combining an improved A^* algorithm with a genetic algorithm.A 3D multi-objective path planning model is established based on UV discharge characteristics,incorporating a composite cost function that accounts for flight distance,altitude variation,path smoothness,obstacle avoidance,and discharge source evasion.An improved A^* algorithm is designed by introducing penalty terms for terrain elevation and discharge source distribution into the heuristic function to guide the generation of an initial path suitable for power grid scenarios.This path is then used as a heuristic individual in a genetic algorithm for global optimization,improving both search efficiency and path quality.Simulation experiments in typical mountainous transmission scenarios demonstrate that the algorithm outperforms traditional methods in terms of path length,obstacle avoidance,and discharge source coverage.The proposed approach significantly improves the rationality and completeness of UAV inspection paths and provides effective technical support for UV-based intelligent inspection in smart grids.

Satellite radar observation reveals the spatial distribution and motion characteristics of landslides in Wudongde Reservoir area

WANG Lidong

2026, 0(3):  142-149.  doi:10.13474/j.cnki.11-2246.2026.0324

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Water storage in hydropower stations will directly affect the movement status of landslides in the reservoir area.Identifying and monitoring landslides in the reservoir area and revealing the relationship between landslide deformation and influencing factors such as water storage and rainfall is crucial to reducing the threat of landslide disasters to reservoir area residents and hydropower stations.In order to study the impact of water storage in Wudongde hydropower station on landslides in the reservoir area in 2020,this paper uses the Sentinel-1 dataset to detect active landslides in Wudongde Reservoir area based on SBAS-InSAR technology.A total of 25 landslides in the reservoir area are identified,including 10 water wading landslides and 15 non-wading landslides.Most landslides are distributed in areas with an altitude of 2000～5000 m and a slope angle of 1°～40°.In addition,a typical landslide is selected from each of two different types of landslides for deformation time series analysis: the effect of water storage on water wading landslides is analyzed by K-means clustering; and the effect of rainfall on non-water wading landslides is analyzed by wavelet transformation.The results show that water storage is the dominant factor driving accelerated deformation of water wading landslides,and rainfall affects seasonal deformation of non-water wading landslides.The research results of this article can provide reference for landslide disaster warning and prevention in Wudongde Reservoir area.

Disparities in the impact of 3D visibility on urban housing prices

WANG Haohui, MA Ding, DENG Hongye, LIU Yihang, WANG Zhenkun, ZHU Wei, WANG Weixi

2026, 0(3):  150-155.  doi:10.13474/j.cnki.11-2246.2026.0325

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Understanding the relationship between housing prices and the surrounding environment is essential for optimizing spatial resource allocation and advancing refined urban governance.Despite its importance,the influence of visibility attributes on housing prices remains insufficiently explored.This study integrates four 3D visibility metrics into a machine learning-enhanced hedonic pricing model (HPM) and employs SHAP(shapley additive explanations) analysis to elucidate the heterogeneous effects of these metrics on housing prices in Shenzhen.The enhanced HPM substantially improves prediction accuracy,with the random forest model outperforming ordinary least squares and XGBoost approaches,achieving the highest explanatory power(R²=0.881).Visibility metrics account for 38.4% of the explained variance in housing prices.The analysis reveals that a higher visible volume of sky exerts a suppressive effect on housing prices,whereas longer average sightlines,larger visible facade areas,and greater visible volume of buildings are positively associated with price increases.These findings offer novel insights into the visual dimension of urban form and its economic implications,contributing to human-centered and sustainable urban development strategies.

AIGC-empowered generation of rural architectural characteristics under geocultural spatial constraints: a case study of Zengcheng district in Guangzhou

DENG Maoying, CAO Kaibin, LIU Bohua, LOU Yuting, ZHAO Yuan

2026, 0(3):  156-161.  doi:10.13474/j.cnki.11-2246.2026.0326

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With the deepening advancement of rural revitalization strategies,AIGC (artificial intelligence generated content),with its rapid response capabilities and diverse content generation,precisely meets the dynamic and differentiated needs of rural construction.It provides core support for preserving rural collective memory and cultural genes,shaping livable environments,and enhancing rural attractiveness.This study,taking Zengcheng district in Guangzhou as an empirical case,proposes a closed-loop technical framework of “feature cognition-feature learning-feature generation.”It systematically implements geo-cultural spatial gene decoding, construction of a multimodal dataset for vernacular architecture,and multi-scale scenario validation of AIGC generative models.The research explores synergistic mechanisms for AIGC-driven targeted generation of distinctive rural landscapes.Key outcomes include:①Achieved controllable landscape generation under cultural-geographical constraints; ②Established a three-tier advancement paradigm (data-algorithm-application); ③ Provided technical methodological support for rural cultural preservation and spatial governance in the AI era.

Research on online service assembly and access control for geographic entities

ZHANG Xiaoping, WANG Zhe, LIAN Junping

2026, 0(3):  162-167.  doi:10.13474/j.cnki.11-2246.2026.0327

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Aiming at customized and intelligent 3D real-scene services,this study follows the concept of “one database with multiple capabilities,on-demand assembly” and systematically reconstructs and optimizes the existing technical framework to address current shortcomings in geographic entity data applications,dynamic assembly,and access control.Using Xi'an city's geospatial data as the data source,the research innovatively integrates commercial cryptographic algorithms and proposes a technical method that supports real-time online service assembly and fine-grained access control.Experimental results demonstrate that this method significantly improves the efficiency of constructing geographic entity online services,effectively reduces data preprocessing time,and exhibits notable advantages in key performance metrics such as dynamic entity assembly,multi-level access control,and intelligent on-demand delivery.This study effectively enhances the online service efficiency of geographical entity data,providing a technical reference for the management and service applications of real-scene 3D data.

Collaborative dynamic updating method for terrain level basic geographic entities and elements

REN Guangyao, SUN Yunfei, HOU Enbing

2026, 0(3):  168-173.  doi:10.13474/j.cnki.11-2246.2026.0328

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The addition,loss,and other changes of physical graphic elements in terrain level basic geographic data make it difficult to associate them with graphic elements when updating in the database,which can not guarantee the dynamic updating of geographic information,and the spatial location accuracy of complex scenes is insufficient.To this end,a collaborative dynamic update method for terrain level basic geographic entities and elements is proposed.Connect the basic geographic information feature data and terrain level basic geographic entity data through the correspondence between features and graphic elements.Build a collaborative update library,design hanging fields for basic geographic information feature data,conduct entity entity association modeling,and adopt a collaborative dynamic update method for entity entities based on DOM orthophoto comparison analysis.Using collaborative database generation for updating operations,exporting basic geographic features and entities.Through steps such as spatial benchmark conversion,resolution unification,and entity change detection,achieve collaborative dynamic updates for the creation,expansion,and loss of entity entities.The experimental results show that the collaborative update library constructed by this method has detailed classification,efficient collaborative updates,can effectively maintain the relationship between entities and graphic elements,accurately capture changes in geographical features,and the spatial position deviation of the same named land features is within 0.3 m.Therefore,this method can effectively ensure the accuracy and consistency of updates.

Land use classification and spatio-temporal pattern of carbon emissions based on multi-source remote sensing collaboration: a case study of Qinghai Lake basin

SONG Yingbin, XU Qing, YE Fei, LUO Wei

2026, 0(3):  174-180.  doi:10.13474/j.cnki.11-2246.2026.0329

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In the context of the “Dual Carbon” strategy,precise identification of regional land use changes and their spatiotemporal patterns of carbon emissions is crucial.Taking the Qinghai Lake basin as a case study,this paper establishes a technical framework integrating “multi-source data coordination,land use classification,and spatio-temporal analysis of carbon emissions”.By combining Sentinel-1/2 satellite data,automated sampling,and multi-feature optimization techniques,an object-oriented random forest classification model is developed to achieve accurate land use classification in the Qinghai Lake basin.Building on this foundation,the study combines coefficient analysis with indirect energy consumption estimation to calculate and analyze the spatiotemporal evolution of land use-related carbon emissions from 2017 to 2022.The findings demonstrate:①The proposed framework significantly reduces manual annotation workload and improves sampling consistency,effectively compressing 37 feature variables to 27.Through the implementation of the object-oriented random forest model,both overall classification accuracy and Kappa coefficient exceeds 96%.②The carbon balance structure in Qinghai Lake basin shows construction land as the primary carbon source (accounting for over 93%of total emissions),with water bodies,forests,and grasslands serving as major carbon sinks.③From 2017 to 2022,net carbon emissions in the basin surges from 6.262×10⁴ t to 13.445×10⁴ t,representing a 114.71%increase,driven by urban expansion and declining carbon sequestration capacity of forests and wetlands.④The spatial distribution of carbon emissions exhibits a “corridor-based concentration of sources—waterside concentration of sinks” pattern.The automated classification and carbon accounting framework develop in this study provides decision-making support for regional ecological management and carbon neutrality pathways.

Application of space-air-groud integrated surveying technology of railway in complex and difficult mountainous area

KANG Miaohang

2026, 0(3):  181-185.  doi:10.13474/j.cnki.11-2246.2026.0330

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This paper addresses the challenges of large-scale,high-precision,and efficient railway surveying in complex mountainous areas characterized by high altitude,significant elevation differences,extreme cold and oxygen deficiency,and conducts research on the comprehensive integration and innovative application of surveying technologies.The technical key points of the air,space and ground surveying methods and their applicability at each stages of railway surveying are systematically summarized.Furthermore,a space-air-ground integrated comprehensive surveying system suitable for complex mountainous areas is established.This system has been successfully applied in railway projects such as the Sichuan-Xizang and Yunnan-Xizang lines,generating a variety of surveying outcomes including multi-scale topographic maps,surface deformation rate maps,real-scene 3D models,large-scale stereoscopic image models,digital elevation models,route centerlines,cross and longitudinal sections,and control networks,etc.It significantly improves the quality and efficiency of surveying,providing important technical references and practical experience for railway surveying in similar challenging environments.