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Deformation monitoring and prediction of wide-area land surface and important infrastructure based on InSAR WANG Xiang, LIU Yanxia, ZONG Qin, SUN Wei, LIU Tao, YANG Xia, FANG Jinling Bulletin of Surveying and Mapping    2025, 0 (7): 104-109.   DOI: 10.13474/j.cnki.11-2246.2025.0717 Abstract （358）      PDF（pc） （5233KB）（103）       Knowledge map    Save Based on InSAR,high-precision,high spatial resolution,and continuous surface deformation information can be obtained.Urban ground subsidence and high-precision deformation information are of great significance for ensuring public safety.This article uses PS-InSAR and wide area surface deformation fast extraction algorithm to obtain spatiotemporal distribution information of surface deformation based on 1600 km 2 COSMO Skyed images in Wuhan from June 2012 to June 2024 and 32 177 km 2 Sentinel-1 images in Wuhan,Ezhou,Huanggang,and Huangshi from January 2018 to June 2024.The deformation accuracy is evaluated based on GNSS and leveling measurement data.The results show that the root mean square error of deformation rate in COSMO data ranged from 2.3～5.8 mm/a,while the root mean square error of deformation rate in Sentinel-1 data ranged from 2.99～6.29 mm/a.The root mean square error of COSMO temporal deformation is 4.96 mm,and the root mean square error of Sentinel-1 temporal deformation is 5.20 mm.At the same time,extract deformation information of important infrastructure areas such as subway lines,subway protected areas,large-span buildings,and foundation pits,and analyze the correlation between deformation and the start and end time of engineering sections,etc.Finally,using the logistic deformation prediction model,the surface subsidence of Wuhan,Ezhou,Huanggang,and Huangshi is predicted for the next two years,with one prediction per quarter for a total of eight periods. Reference | Related Articles | Metrics

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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 （321）      PDF（pc） （3350KB）（61）       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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Landslide early warning model and application based on multi-sensor data fusion WANG Yipeng, XU Dawei, WEI Mingyang, LI Bo, HU Huimin, YANG Mingsheng, XU Yuling Bulletin of Surveying and Mapping    2025, 0 (7): 169-173.   DOI: 10.13474/j.cnki.11-2246.2025.0728 Abstract （317）      PDF（pc） （2654KB）（119）       Knowledge map    Save Landslides,as a sudden and highly destructive geological hazard,pose severe threats to the safety of human production and livelihoods.The limited capability of single sensors to recognize multi-factor coupling effects hinders the comprehensiveness and accuracy of landslide early warning systems.To address this limitation,this paper proposes a multi-sensor fusion early warning model based on the BP neural network.Leveraging the nonlinear feature extraction capabilities of the BP neural network,the data from inclinometers,GNSS displacement sensors,and rainfall sensors are trained and predicted individually.The normalized predictions from these three sensors are then integrated using a weighted scoring method to achieve the final landslide risk assessment,forming an efficient and accurate monitoring system.The proposed early warning system has been successfully applied to a specific slope near the a certain oil pipeline,demonstrating promising results and significant potential for broader applications. Reference | Related Articles | Metrics

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Real-scene 3D data acquisition and fusion technologies inside and outside caves:take Yixing Shanjuan Cave for an example GUO Zhendong, WU Hao, GU Zhengdong, HUANG Liang Bulletin of Surveying and Mapping    2025, 0 (8): 149-152.   DOI: 10.13474/j.cnki.11-2246.2025.0824 Abstract （315）      PDF（pc） （3762KB）（103）       Knowledge map    Save Aiming at the insufficient research on modeling complex indoor and underground spaces in real-scene 3D construction,this paper proposes a 3D modeling method that integrates 3D point clouds and video imagery.Firstly,high-precision laser point cloud data inside the cave is acquired using SLAM technology,while multi-angle video imagery is collected via close-range photogrammetry.Then,the SFM algorithm is employed to generate dense matching point clouds,and the ICP algorithm is applied to achieve precise registration of heterogeneous data,constructing a 3D cave model with both structural features and texture information.Finally,the indoor model is fused with an outdoor terrain-level real-scene 3D model obtained from UAV oblique photogrammetry,forming a unified digital twin platform.The results demonstrate that this method achieves high-precision reconstruction and virtual-real integration of indoor and outdoor scenes,providing a reliable technical reference for modeling complex underground spaces. Reference | Related Articles | Metrics

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Application of BIM reverse modeling based on multi-source data fusion in the preservation of historic and cultural blocks XING Wang, FANG Zheng, XU Yi, ZHANG Canghao, SUN Lianzeng, WANG Zhaoze Bulletin of Surveying and Mapping    2025, 0 (8): 159-163,178.   DOI: 10.13474/j.cnki.11-2246.2025.0826 Abstract （314）      PDF（pc） （3507KB）（116）       Knowledge map    Save Addressing the issues of low model completeness and unstable data accuracy in traditional modeling of existing buildings in historical and cultural districts,a technical system of “comprehensive acquisition-data fusion-intelligent reconstruction” is proposed.By utilizing 3D laser scanning and multi-view photogrammetry technologies,millimeter-level geometric frameworks and high-resolution texture information of buildings are obtained respectively.An improved SICP algorithm is employed to achieve precise fusion of multi-source point clouds.Finally,using BIM reverse modeling to construct a realistic 3D model containing building information.The results indicate that this method achieves millimeter-level geometric accuracy and over 98%completeness of the model,supporting multi-dimensional spatio-temporal information overlay analysis.It provides a full lifecycle solution for preventive conservation,virtual restoration,and revitalization of the district. Reference | Related Articles | Metrics

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SFR-YOLO: small target detection algorithm for UAV imagery based on improved YOLOv8 SUN Jiyuan, JI Song, GAO Ding, LI Kai, ZHANG Ruiying Bulletin of Surveying and Mapping    2025, 0 (7): 32-39.   DOI: 10.13474/j.cnki.11-2246.2025.0706 Abstract （301）      PDF（pc） （11938KB）（108）       Knowledge map    Save Addressing the issues of small targets in drone imagery having a low pixel ratio,leading to easy loss of features,as well as the large parameter count and deployment challenges of traditional object detection models,this paper proposes a lightweight small object detection algorithm named SFR-YOLO based on an improved YOLOv8.Firstly,this paper introduces a lightweight Shared detail-enhanced convolution detection head (SDCDH),which not only reduces the number of parameters in the detection head by sharing convolutions but also enhances the representation of detailed features by introducing detail-enhanced convolution (DEConv) in the shared layers.Secondly,the feature fusion network is improved using a weighted bidirectional feature pyramid network (BIFPN) with added shallow feature fusion branches and the removal of deep convolution,which boosts the detection performance for small objects.Finally,this paper designs a CRFA module that combines spatial attention and receptive field features to enhance the feature extraction capability of the model's backbone network.Experimental results demonstrate that SFR-YOLO achieves a 3.8%improvement in mean average precision (mAP) compared to the YOLOv8n algorithm on the VisDrone2019 dataset,SFR-YOLO not only enhances the detection of small objects but also meets the requirements for model deployment.Additionally,transfer experiments of SFR-YOLO on the CARPK dataset further validating the effectiveness of the proposed method in this paper. Reference | Related Articles | Metrics

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Evaluation of ambiguity fixation performance in GPS and BDS-3 integrated non-combined kinematic precise point positioning ZHAI Yan, XIE Rui, YANG Li Bulletin of Surveying and Mapping    2025, 0 (7): 1-4.   DOI: 10.13474/j.cnki.11-2246.2025.0701 Abstract （298）   HTML （25）    PDF（pc） （2024KB）（136）       Knowledge map    Save This paper compares and analyzes the ambiguity resolution performance of GPS,BDS-3 and the integration of GPS and BDS-3 for non-combined dynamic precise point positioning (PPP). The results indicate that the integration of GPS and BDS-3 increases the number of observable satellites and optimizes the spatial geometric structure, thereby effectively reducing the initial ambiguity fix time, improving the ambiguity fix rate, and enhancing the positioning accuracy. For GPS, the average initial ambiguity fix time and fix rate are 28.7 minutes and 98.6%, respectively, while the positioning accuracies in the horizontal, vertical, and 3D directions after convergence are 0.9, 1.7, and 1.9 cm, respectively. For BDS-3, the average initial ambiguity fix time and fix rate are 47.2 minutes and 96.9%, respectively, while the positioning accuracies in the horizontal, vertical, and 3D directions after convergence are 1.4, 2.5, and 2.7 cm, respectively. When GPS and BDS-3 are integrated, the average initial ambiguity fix time and fix rate reach 13.2 minutes and 99.5%, respectively, and the positioning accuracies in the horizontal, vertical, and 3D directions after convergence are 0.8, 1.6, and 1.8 cm, respectively. Reference | Related Articles | Metrics

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Landslide hazard identification based on the object detection algorithm YOLOv9:taking Yongxin county as an example TU Liping, CHEN Meiqiu, LENG Peng Bulletin of Surveying and Mapping    2025, 0 (6): 37-42,102.   DOI: 10.13474/j.cnki.11-2246.2025.0607 Abstract （293）      PDF（pc） （5997KB）（118）       Knowledge map    Save Landslide disaster is one of the most serious geological disasters,which causes huge property losses and casualties every year.Traditional image-based manual investigation is heavy in workload and low in efficiency.This study takes Yongxin county as the research area,firstly,uses the YOLOv9 object detection algorithm to build a landslide recognition model based on 207 landslide samples constructed by high-resolution aerial images,and then evaluates the accuracy of the model.Finally,the landslide of the whole county is identified and the landslide results identified are analyzed.The results show that the accuracy of the model is 0.98,the recall rate is 0.97,and the mAP is 0.95.There are 312 common landslides in the county,and 46 are misjudged by the model through comparison and field investigation,and the accuracy of model recognition is 85.26%.It can be seen that YOLOv9,an object detection algorithm,can effectively identify landslides in the southern region,providing an effective solution for large-scale identification of small-scale landslides in the south. Reference | Related Articles | Metrics

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Precision analysis and positioning evaluation of satellite-based precise point positioning service in the Antarctic region LIU Yang, CHAI Hongzhou, WANG Min, ZHOU Yingdong, SUN Shuang, ZHANG Qiankun Bulletin of Surveying and Mapping    2025, 0 (8): 1-6.   DOI: 10.13474/j.cnki.11-2246.2025.0801 Abstract （289）   HTML （16）    PDF（pc） （3117KB）（176）       Knowledge map    Save Satellite-based precise point positioning service offers high-precision positioning in the environment where terrestrial networks are hard to cover,yet there are few applications in polar regions.Based on the measured HAS data of China's 40th Antarctic Expedition and the real-time precision orbit and clock deviation products of MADOCA and CNES broadcast on the network.This paper assesses the availability,accuracy and PPP performance of different products' orbit and clock correction of GPS and Galileo in the Antarctic region.The results demonstrate that GPS and Galileo correction products provided by HAS,MADOCA and CNES are highly accurate and can satisfy the requirement of centimeter-level positioning accuracy,reflecting the applicability of these real-time products in the Antarctic region.Both HAS and MADOCA can provide stable and reliable PPP services,with a horizontal positioning accuracy of less than 0.2 m and a vertical positioning accuracy of less than 0.4 m,conforming to the service standards.This provides a theoretical and practical basis for the application of satellite-based precision single-point positioning technology in polar regions and other high latitudes. Reference | Related Articles | Metrics

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GDS:drone image-guided cross-view image geographic positioning XI Zexin, LI Jiayi, XIE Hao, GAN Wenjian, ZHOU Yang Bulletin of Surveying and Mapping    2025, 0 (7): 66-72.   DOI: 10.13474/j.cnki.11-2246.2025.0711 Abstract （287）      PDF（pc） （4419KB）（52）       Knowledge map    Save Cross-view image geographic positioning refers to the method of matching the ground-view image with unknown geographic coordinates with the reference satellite image with high precision spatial coordinate information,so as to determine the geographical coordinates of the ground-view image.Due to the large difference in viewing angle between the unpositioned ground viewing angle image and the reference Satellite image,it is difficult to retrieve and match.In this paper,a UAV image-guided cross-viewing angle geographic positioning method ground-drone-satellite(GDS) is proposed,which uses the tilting photographic image of low-altitude UAV as a transition.Firstly,the unpositioned ground view image is matched with the UAV image,and then the retrieved UAV image is matched with the satellite image with accurate geographic coordinates,so as to determine the geographical position of the ground view image.In this paper,the ConvNeXt model based on convolutional neural network and Vision Transformer is used to extract image features,and InfoNCE loss is used as the training target for comparative learning,which improves the accuracy of image query.Meanwhile,random sampling strategy is adopted to disrupt and randomly remove a small part of training samples.The generalization ability of the model is improved.Experimental results on University-1652,a universal cross-view data set,show that the proposed method is superior to the method for retrieving satellite images directly from ground-view images in terms of Recall and average accuracy AP.In this paper,the accuracy of querying UAV view images from the ground perspective is 11.63%Recall@1,and the accuracy of querying satellite view images from the UAV view is 91.49%Recall@1.The two-stage retrieval method is comprehensively used to query satellite view images from the ground view images,and the accuracy reaches 10.64%Recall@1.Compared with 5.23%Recall@1 in the direct retrieval of satellite images from the ground perspective,this is a great improvement,which verifies the effectiveness and advancement of the proposed method. Reference | Related Articles | Metrics

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Evaluation of landslide susceptibility by fusing SBAS-InSAR deformation and machine learning model LI Wendong, YE Yu, LI Xia, WEI Wei, XIN Cunlin Bulletin of Surveying and Mapping    2025, 0 (7): 126-131,146.   DOI: 10.13474/j.cnki.11-2246.2025.0720 Abstract （287）      PDF（pc） （3493KB）（83）       Knowledge map    Save This paper comprehensively employs InSAR and machine learning techniques to conduct landslide susceptibility assessment in the key landslide-prone area in the northern part of Xiahe county,Gansu province.The deformation information obtained by SBAS-InSAR is incorporated as a dynamic evaluation factor into the 11 static factors.Three models,namely RF(random forest),LR (logistic regression),and XGBoost (extreme gradient boosting),are used for susceptibility assessment,and their evaluation performances are compared and analyzed.The results show that among the three assessment models,the XGBoost model has the best performance.The results indicate that the XGBoost model with the addition of surface deformation variables has a higher evaluation accuracy than the XGBoost model using only static factors.Its comprehensive performance indicators,such as AUC value,Recall,Precision,and F1,reach 0.93,0.896,0.894,and 0.898 respectively.Therefore,incorporating surface deformation variables obtained by SBAS-InSAR technology as landslide susceptibility evaluation factors can improve the accuracy of model prediction and enhance the effectiveness of the assessment. Reference | Related Articles | Metrics

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Forest aboveground biomass mapping in the Greater Mekong Subregion using multi-source remote sensing data fusion YUAN Lili, YANG Xinwei, LI Menghua, CHEN Yuquan, TANG Bohui Bulletin of Surveying and Mapping    2025, 0 (8): 43-47.   DOI: 10.13474/j.cnki.11-2246.2025.0807 Abstract （285）      PDF（pc） （6381KB）（120）       Knowledge map    Save Accurate estimation of forest aboveground biomass density is crucial for advancing sustainable forest management.This study focuses on the Greater Mekong Subregion (GMS)and utilizes spaceborne global ecosystem dynamics investigation(GEDI),Sentinel-1,Sentinel-2,and auxiliary datasets to extract 52 feature variables.By applying the LightGBM machine learning model,a 1 km resolution forest aboveground biomass density map of the GMS is generated.The results indicate that the LightGBM model achieved R 2=0.65,RMSE=38.11 Mg/hm 2,and EA=72.03%.Across the study area,biomass density ranged from 15.16 to 423.87 Mg/hm 2.The derived biomass product demonstrated strong correlation with the GEDI L4B product ( R 2=0.52,RMSE=61.91 Mg/hm 2).In conclusion,open-access earth observation (EO)data exhibits significant potential for estimating forest aboveground biomass. Reference | Related Articles | Metrics

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PS-InSAR monitoring and analysis of ground subsidence in large-scale water diversion project XIONG Chunbao, AN Hewen, SU Guangli Bulletin of Surveying and Mapping    2025, 0 (7): 19-25,72.   DOI: 10.13474/j.cnki.11-2246.2025.0704 Abstract （274）   HTML （16）    PDF（pc） （8173KB）（69）       Knowledge map    Save The South-to-North Water Diversion project is a national strategic project aimed at alleviating the problem of unequal distribution of water resources between the south and the north. Ground deformation monitoring along the project can identify potential safety hazards and is greatly significant to the safe operation of this large-scale water diversion project. 45 Sentinel-1A imagery data from July 2020 to June 2023 are acquired for the Tianjin branch of the South-to-North Water Diversion project.Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is used to monitor the ground subsidence in the study area. The average annual rate and the cumulative amount of subsidence in the area are obtained. The reliability of PS-InSAR in measuring ground deformation is verified by comparing with the data of Global Navigation Satellite System (GNSS).The results show that the average ground subsidence rate in the study area ranges from -72.26 to 17.30 mm/a during the study time. There are two obvious subsidence zones along the Tianjin branch, which one is located at the junction of Xiongxian county and Gu'an county, and another the eastern part of Bazhou city. The main reasons of ground subsidence in the area include over-exploitation of groundwater and the increase of ground loads due to urban infrastructure construction and accelerated industrialization.The lag time of ground deformation in the study area compared to groundwater level changes is about 1 to 3 years. Reference | Related Articles | Metrics

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Ground deformation monitoring and influence factors analysis of the Gaizi valley near the Karakoram highway based on SBAS-InSAR technology MO Dandan, HUO Jiuyuan Bulletin of Surveying and Mapping    2025, 0 (8): 32-42.   DOI: 10.13474/j.cnki.11-2246.2025.0806 Abstract （270）   HTML （13）    PDF（pc） （14386KB）（105）       Knowledge map    Save The Karakoram highway (KKH) in China and Pakistan has complex geological conditions, peculiar and variable climate, and landslide hazards are frequent along the route, so the investigation and monitoring study of landslide hazards in this region is of great importance for disaster prevention and mitigation. In this study, the small baseline subsets InSAR (SBAS-InSAR) technique is used in combination with optical remote sensing images to monitor the surface deformation and analyze the time-series deformation characteristics of the Gaizi valley section of the China-Pakistan highway.Based on 64 scenes of Sentinel-1 image data covering the study area, the SBAS-InSAR technique is used to obtain deformation distribution maps and time-series deformation features of the study area over the time span. The deformation rate values of the radar line of sight (LOS) in the study area from March 2017 to August 2022 ranged from -33.5 to 11.6 mm/a, with a maximum cumulative deformation of 179.4 mm. On this foundation, the accuracy of the deformation detection results is verified by combining the optical remote sensing images of four typical landslide areas in the region and previous research results, demonstrating that SBAS-InSAR technology is an effective tool for deformation monitoring of landslide hazards. The time series deformation curves are analyzed by using monthly average precipitation, monthly average temperature, monthly maximum temperature, monthly minimum temperature, surface soil moisture data, glacier distribution data, earthquake catalogue, IGBP land cover data and so on. Furthermore, the influence of different factors on the surface deformation of landslides in the study area is explored to provide a scientific basis for early identification and prevention of disasters. Reference | Related Articles | Metrics

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InSAR deformation monitoring and early identification of landslide disasters in Lanping county LI Ruofan, LI Yongfa, ZUO Xiaoqing, HUANG Cheng, XING Mingze, LI Yongning, ZHANG Jianming, SHI Chao, GU Xiaona Bulletin of Surveying and Mapping    2025, 0 (7): 40-45.   DOI: 10.13474/j.cnki.11-2246.2025.0707 Abstract （267）   HTML （15）    PDF（pc） （5814KB）（143）       Knowledge map    Save In response to the frequent occurrence of geological disasters in Lanping county,traditional methods are difficult to achieve early identification of landslides in a wide area.This article uses SBAS InSAR technology to conduct early identification and analysis research on landslides in Lanping county.Firstly,in response to the serious problem of geometric distortion in SAR images in the high mountain canyon area of Lanping county,the R index method is used to extract the geometric distortion area in the area to ensure the accuracy and reliability of InSAR deformation results.Secondly,SBAS InSAR technology is used to obtain surface deformation information of Lanping county from January 2021 to December 2023,and optical images are combined to identify landslide disasters in Lanping county.Finally,select typical landslide disaster points for spatiotemporal evolution feature analysis.The research results show that the method proposed in this article can effectively improve the accuracy of landslide hazard identification in high mountain and canyon areas.A total of 42 deformation areas have been detected in Lanping county,mainly distributed on both sides of the Lancang River basin.The research results can provide scientific basis for geological hazard prevention and control work in Lanping county. Reference | Related Articles | Metrics

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Colorized 3D reconstruction technology integrating multi-source and multi-view data ZHANG Lijun, GAO Yunhan, ZOU Xiaofan, SHI Hang, XIE Yangmin Bulletin of Surveying and Mapping    2025, 0 (6): 62-67.   DOI: 10.13474/j.cnki.11-2246.2025.0611 Abstract （265）      PDF（pc） （5938KB）（93）       Knowledge map    Save With the growing demands of digitalization, data acquired from a single sensor has become inadequate for complex modeling tasks, and single-view approaches are inherently prone to data sparsity and occlusion issues. To address this problem, this paper proposes two sets of 3D reconstruction systems, which are equipped with multiple sensors including LiDAR, monocular cameras, and IMU, and can be respectively applied to multi-view data collection in both indoor and outdoor environments. Furthermore, this paper also presents a colorized 3D reconstruction technology based on the fusion of multi-source and multi-view data. The prerequisite is to obtain the coordinate system transformation relationships among various sensors through joint calibration. Then, the visual-inertial coupling system is utilized for motion estimation to acquire accurate postures and trajectories. Based on this, the single-view laser point clouds are optimized for distortion removal and assigned with true colors. Finally, multi-view point cloud stitching is carried out based on the mixed information of color and geometry to obtain the true-color 3D model of the scanned object. Tests and verifications have been conducted in both indoor and outdoor scenarios. The experimental results show that the 3D reconstruction accuracy of this method is higher than that of advanced algorithms, with the modeling error reaching the centimeter level. Additionally, the algorithm has higher robustness. Reference | Related Articles | Metrics

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Influence of stochastic model processing strategies for seafloor geodetic control point positioning LÜ Zhipeng Bulletin of Surveying and Mapping    2025, 0 (6): 1-5.   DOI: 10.13474/j.cnki.11-2246.2025.0601 Abstract （262）      PDF（pc） （2126KB）（180）       Knowledge map    Save GNSS-A joint positioning technology is the main way to determine the coordinates of seafloor geodetic control points. The position error of transducer is considered to be a non-ignorable error source in the process of GNSS-A joint positioning. To solve this problem, the following three parameter estimation methods can be used:①least-squares (LS) estimation, which ignores the influence of transducer position error; ②improved least-squares (ILS) estimation, which incorporates the transducer position error into the random part of the acoustic ranging error; ③total least-squares (TLS) estimation, which introduces the transducer position error into the underwater acoustic positioning model. Through Monte Carlo simulation, the above three parameter estimation methods are analyzed from the aspects of estimation bias, effectiveness and computational efficiency. The results show that the LS estimation principle is simple and the computational efficiency is the highest. The TLS estimation has the smallest estimation deviation and is the most effective, but it has the lowest computational efficiency and poor convergence reliability. As a compromise scheme, the ILS estimation reduces the estimation bias and improves the validity compared with the LS estimation, improves the computational efficiency and enhances the convergence reliability compared with the TLS estimation. Reference | Related Articles | Metrics

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UAV-based object recognition dataset for coastal sewage outfalls YIN Junjie, GUAN Daiwanjing, LI Hao, ZHANG Xiaoyang, MA Yujie, XING Hanfa Bulletin of Surveying and Mapping    2025, 0 (8): 112-117.   DOI: 10.13474/j.cnki.11-2246.2025.0818 Abstract （254）   HTML （8）    PDF（pc） （3975KB）（42）       Knowledge map    Save The identification of coastal sewage outfalls is a crucial aspect of marine supervision,providing essential safeguards for the ecological and resource security of marine areas.Addressing the current challenges of insufficient specialized datasets and the lack of precision in target recognition algorithms for coastal sewage outfall detection using unmanned aerial vehicle (UAV)imagery,this study constructs a high-quality dataset of coastal sewage outfalls and proposes an enhanced detection method based on the improved YOLOv8n model.Initially,focusing on the coastal region of Yangjiang city,Guangdong province,the study employs UAVs to capture images at various altitudes,establishing a comprehensive dataset that encompasses diverse characteristics of sewage outfalls.Subsequently,the YOLOv8n model is augmented with the SimAM parameter-free attention mechanism to refine feature extraction and fusion,alongside the integration of NWD and CIoU loss functions to address issues of boundary ambiguity and target overlap.Experimental results demonstrate that the enhanced model surpasses the original in terms of precision,recall rate,and mAP,achieving an mAP of 98.27%.This research offers an intelligent solution for monitoring coastal sewage outfalls,contributing technological support for marine supervision and pollution control. Reference | Related Articles | Metrics

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Intelligent collaborative DOM production technology based on remote sensing image production platform WANG Yingmou, LI Lei Bulletin of Surveying and Mapping    2025, 0 (8): 142-148.   DOI: 10.13474/j.cnki.11-2246.2025.0823 Abstract （250）      PDF（pc） （9843KB）（63）       Knowledge map    Save This paper investigates the intelligent collaborative DOM production technology based on a remote sensing image production platform.This technology effectively addresses the issues of low efficiency,insufficient accuracy,and cumbersome processes associated with traditional DOM production by constructing a control point database and a multi-software intelligent collaborative platform.The establishment of the control point database enables the integration and efficient utilization of historical control point data,significantly reducing fieldwork and production costs.The multi-software intelligent collaborative platform combines the functional advantages of software such as INPHO,Tian Gong 2D integrated software,and DPGrid,achieving full-process optimization from aerial triangulation to DOM production.This effectively improves the geometric accuracy and visual effects of DOM while significantly increasing production efficiency.The experimental results show that this technology can effectively improve the efficiency and quality of DOM production,reduce fieldwork and costs,which has considerable value for promotion and application. Reference | Related Articles | Metrics

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The deformation monitoring and prediction of ultra-high voltage transmission channels using combined SBAS-InSAR and DS-InSAR WANG Shenli, LIU Yi, HAN Hao, DU Yong Bulletin of Surveying and Mapping    2025, 0 (6): 130-135,141.   DOI: 10.13474/j.cnki.11-2246.2025.0622 Abstract （243）      PDF（pc） （25364KB）（114）       Knowledge map    Save This paper combines SBAS-InSAR and DS-InSAR technologies to monitor and predict the deformation of the extra-high voltage transmission corridor in Wufeng county, aiming to improve the safety of the transmission line and the disaster warning capability. Firstly,combining these two techniques, a multi-scale deformation monitoring model is established, which provides finer data support for risk assessment of transmission lines. Then, this paper introduces a long short-term memory (LSTM) neural network model for time series prediction of ground subsidence trends. By training and testing the Sentinel-1A satellite data from October 2023 to October 2024, the LSTM model shows high prediction accuracy, with the maximum absolute error of 3.28 mm, the minimum absolute error of 0.13 mm, and the root-mean-square error (RMSE) of 1.32 mm, which verifies the validity and reliability of the model in ground deformation prediction. The study shows that the LSTM model is able to capture the long-term trend of subsidence changes and provide strong support for the maintenance of transmission corridors and disaster warning. Reference | Related Articles | Metrics