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Dynamic monitoring for open-pit mine reclamation based on UAV oblique photogrammetry ZHONG Weihua, LIU Jingkuang Bulletin of Surveying and Mapping    2025, 0 (3): 21-26.   DOI: 10.13474/j.cnki.11-2246.2025.0304 Abstract （714）      PDF（pc） （8280KB）（242）       Knowledge map    Save Traditional open-pit mine reclamation monitoring relies on satellite remote sensing and on-site investigation, but suffers from low precision and efficiency. To enhance dynamic monitoring and improve oversight, this paper explores UAV oblique photogrammetry. Using an open-pit mine in Guangzhou as a test case, the technology generated a real-life 3D Mesh model across four phases, synchronously outputting DEM and producing DLG through stereo acquisition. Monitoring indices such as earth backfill elevation, volume, and building demolition were analyzed using DEM and DLG data. Results indicate that UAV data collection over 1.56km 2 took about one hour, three minutes, and fifty-five seconds, with a GSD of 2.51cm/pixels, marking an improvement in efficiency and accuracy over traditional methods. Additionally, the DLG data helped count a demolition area of 16442.36m 2, and DEM data allowed for the calculation of a total earth backfill volume of 0.017km 3, demonstrating the technology's digital and visual analysis capabilities. This study offers technical support for regional authorities in dynamically monitoring open-pit mine reclamation and provides references for monitoring other mines. Reference | Related Articles | Metrics

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Application of time series InSAR technology in surface subsidence monitoring and spatio-temporal evolution analysis in mining area ZHANG Yuxin, YUAN Xiping, GAN Shu, PENG Xiang, WANG Song Bulletin of Surveying and Mapping    2025, 0 (3): 15-20.   DOI: 10.13474/j.cnki.11-2246.2025.0303 Abstract （410）      PDF（pc） （3246KB）（260）       Knowledge map    Save In view of the hazards caused by surface subsidence to the safety, environment, socio-economic development and sustainability of resource utilization in the mining area, 63 Sentinel-1A data from December 31, 2021 to March 2, 2024 were first obtained, and SBAS-InSAR (time series interferometry) technology was adopted to monitor the surface deformation of Baicao mining area. The results of surface settlement rate and cumulative settlement in the mine area are obtained, and then the reliability analysis of the monitoring results is carried out by using the measured data. Finally, the settlement of the mine area is predicted based on the LSTM model, and the spatiotemporal variation characteristics and evolution rules of the settlement of the mine area are analyzed in detail.The final conclusions are as follows: ① Spatially, the surface subsidence of Baichuang Mining area is mainly concentrated in the west of the mining area, with the maximum subsidence of -316.86mm and the maximum annual average subsidence rate of -148.4mm/a, and the total subsidence area of 0.6236km 2, of which the heavy and extremely heavy subsidence area of 0.2804km 2 needs to be monitored.②In time series, the area with severe subsidence starts to settle from the monitoring starting point, and the subsidence rate tends to be uniform. If no protection is taken, the area will continue to settle in the future, and the settlement may be intensified.③The fitting degree of measured data and monitoring data is high, and the coefficient of determination R 2 is up to 0.994. The prediction effect of LSTM prediction model on monitoring data is good, and the linear fitting coefficient of determination R 2 of predicted value and monitoring value can reach more than 0.946, indicating that the prediction of surface settlement by LSTM model can meet the requirement of accuracy. The experimental results can provide technical support for disaster prevention and control in mining areas, and provide strong support for more accurate surface deformation evaluation in mining areas. Reference | Related Articles | Metrics

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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 （269）      PDF（pc） （5233KB）（85）       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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Design and implementation of an intelligent yard management platform based on UAV: a case study of Rizhao port CHENG Jie, ZHONG Yong, YANG Yugang, JIAO Baotong, ZHANG Fusheng Bulletin of Surveying and Mapping    2025, 0 (3): 178-182.   DOI: 10.13474/j.cnki.11-2246.2025.0331 Abstract （258）      PDF（pc） （3985KB）（99）       Knowledge map    Save With the continuous growth of global trade, ports, as critical nodes in the logistics chain, have their operational efficiency and management levels directly impacting the effectiveness of the overall logistics system. Rizhao port, Shandong port, an important bulk cargo hub in China, faces challenges such as complex yard management and inefficiency. Addressing the practical needs of Rizhao port, this paper designs and implements an intelligent yard management platform based on unmanned aerial vehicles. It aims to enhance the level of intelligence in yard management by integrating information technology such as artificial intelligence, improves yard utilization through intelligent stacking operations by UAV, and reduces operational costs. This paper elaborates on the design concept, technical architecture, key functions, and innovations of the platform, and conducts an empirical analysis using Rizhao port as a case study. The results show an increase in stacking efficiency by over 90%, significantly enhancing the operational efficiency of yard stacking management. Furthermore, the analysis and assessment of yard storage capacity indicate an improvement of over 10% in the port's storage capacity. 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 （256）      PDF（pc） （2654KB）（97）       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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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 （251）   HTML （25）    PDF（pc） （2024KB）（122）       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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The extraction method of tea plantation information based on multi-dimensional features from UAV remote sensing images YANG Jiafang, YIN Linjiang, ZHANG Hongliang, ZHAO Weiquan, LI Wei Bulletin of Surveying and Mapping    2025, 0 (3): 138-143.   DOI: 10.13474/j.cnki.11-2246.2025.0324 Abstract （243）      PDF（pc） （2619KB）（100）       Knowledge map    Save As a crucial economic crop, the rapid measurement of tea planting areas holds significant value for estimating tea yield, optimizing tea garden management strategies. The study employs UAV to obtain multispectral remote sensing images of tea plants. It selects vegetation indices, texture features, and their combined features as multivariate analysis indicators. By using the RF model and correlation analysis, the study evaluates the importance of these indicators and performs correlation tests. Subsequently, three supervised classification algorithms, namely MLC, SVM, and RF are utilized to precisely identify the distribution of tea plants plantations in the study area. The results reveal that: ①When using the SVM and RF algorithms, the incorporation of texture features or multi-feature combinations notably enhances classification accuracy compared with using a single vegetation index feature. ②The optimal classification feature combination for the study area is identified as the visible vegetation indices combined with texture features, achieving a total classification accuracy of 95.5% and a Kappa coefficient of 0.917 with the SVM algorithm. ③The use of full-feature datasets and feature dimensionality reduction datasets does not significantly enhance classification accuracy; however, the latter demonstrates the highest stability in classification results and achieves an accuracy that is second only to the optimal classification feature datasets. In summary, by utilizing the SVM algorithm,the fusion of visible vegetation indices and texture features can effectively differentiate tea plants from other land features,achieve high-precision extraction of tea planting information,and provide valuable reference and practical guidance for accurately extracting crop planting information. Reference | Related Articles | Metrics

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Water extraction from Sentinel-1 images based on improved DeepLabV3+ network ZHAO Xingwang, ZHAO Yan, LIU Chao, LIU Chunyang Bulletin of Surveying and Mapping    2025, 0 (3): 66-70.   DOI: 10.13474/j.cnki.11-2246.2025.0311 Abstract （239）      PDF（pc） （2008KB）（210）       Knowledge map    Save In order to improve the accuracy of water extraction from radar images, this paper uses Sentinel-1 series images from 2023 as the data source, optimizes the backbone network on the basis of the DeepLabV3+ network model, integrates the SE channel attention mechanism, and proposes an improved deep learning network model SEDeepLabV3+. The ablation experiment is carried out for the improved model, and the model is verified by the water body extraction in Changping district of Beijing on July 31. The experimental results show that when the improved SEDeepLabV3+ method is used to extract water body, the mean intersection over union and pixel accuracy can reach 88.55% and 93.49%. Compared with DeepLabV3+, HRNet and U-Net, the average intersection ratio is increased by 2.26%,2.31% and 5.08%,and the average pixel accuracy is increased by 0.76%,0.80% and 3.07%,respectively. The improved SEDeepLabV3+ not only has a lighter network structure,but also can effectively improve the accuracy and efficiency of water extraction. Reference | Related Articles | Metrics

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Analysis of spatio-temporal variations and drivers of habitat quality in the coal mine area of Yangquan city ZHANG Nan, CHEN Shenghua, SUN Caixia Bulletin of Surveying and Mapping    2025, 0 (3): 27-32.   DOI: 10.13474/j.cnki.11-2246.2025.0305 Abstract （239）      PDF（pc） （4075KB）（112）       Knowledge map    Save The impact of coal mining on the habitat quality of coal mining area in Yangquan city is solved.Based on Landsat images from 2003 to 2023, this paper classifies the land use types in Yangquan coal mining area, uses the InVEST model to analyze the temporal and spatial evolution characteristics of habitat quality in coal mining area, and uses geographic detectors to explore the driving factors affecting the spatial distribution of habitat quality.The results show that:① In the past 20 years, the land use types are mainly composed of forest land and grassland, the cultivated land area is generally stable, and the construction land has shown an increasing trend year by year.② The habitat quality grade was mainly composed of medium and high grades, but the changes mainly changed from high grade to low grade, and the area area in a state of deterioration accounted for nearly 70%, and the overall habitat quality status deteriorated.③ Land use is the main driver of differences in the spatial distribution of habitat quality,and the interaction of the other influences with land use had the strongest explanatory power.The results of the study provide a theoretical basis for ecological environment management and restoration of government departments. Reference | Related Articles | Metrics

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3D deformation prediction of mine surface based on combined SMA-CNN-GRU-Attention modeling PENG Yibo, YANG Weifang, YAN Xiangrong, GAO Motong, HOU Yuhao, ZHANG Delong Bulletin of Surveying and Mapping    2025, 0 (3): 8-14,20.   DOI: 10.13474/j.cnki.11-2246.2025.0302 Abstract （233）      PDF（pc） （6421KB）（160）       Knowledge map    Save Research on monitoring and prediction of surface deformation in mining areas is of great significance for safe production and disaster prevention and warning in mining areas. Existing studies tend to monitor and predict the vertical subsidence of the ground surface, and there are fewer studies on the prediction of 3D directional deformation. To address the above problems, this paper is based on the small baselines set synthetic aperture radar interferometry (SBAS-InSAR) technology to monitor the surface deformation of the west second mining area of Jinchuan mining area with multi-track data. A combined SMA-CNN-GRU-Attention network model with slime mould Algorithm (SMA) is proposed to predict the surface deformation in this area. The results show that adding SMA for optimal parameter solving reduces the MAE and RMSE of the vertical prediction results by 30% and 46% compared to the CNN-GRU network model; the MAE and RMSE of the north-south prediction results are 37% and 39% lower, respectively; and the accuracy of the east-west prediction results is lower, with the MAE and RMSE lower than those of the CNN-GRU network model by 6% and 10%, respectively. The SMA algorithm can accelerate the efficiency of the optimal parameter selection of the model, and it can also improve the prediction performance of the CNN-GRU-Attention model to a larger extent.The SMA-CNN-GRU-Attention multi-feature input prediction model has the superiority compared with other prediction models, and it provides an effective method for the research of 3D deformation prediction of the ground surface. 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 （232）      PDF（pc） （5997KB）（110）       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 （232）   HTML （15）    PDF（pc） （3117KB）（164）       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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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 （227）      PDF（pc） （11938KB）（97）       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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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 （225）      PDF（pc） （3350KB）（55）       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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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 （224）      PDF（pc） （2126KB）（178）       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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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 （224）      PDF（pc） （4419KB）（34）       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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Improved U-Net convolutional network application for land cover classification in remote sensing images GOU Changlong, PANG Min, YANG Yang Bulletin of Surveying and Mapping    2025, 0 (3): 150-155.   DOI: 10.13474/j.cnki.11-2246.2025.0326 Abstract （221）      PDF（pc） （2122KB）（176）       Knowledge map    Save Land cover classification plays a crucial role in environmental monitoring, resource management,and urban planning. However,the classification process faces various challenges due to factors such as spectral similarity,noise interference,and the intermixing of natural and man-made objects. To improve classification accuracy and enhance the robustness of the model,this paper proposes a deep learning network based on the U-Net convolutional architecture,combined with the Transformer self-attention mechanism.Experiments conducted on the remote sensing dataset of Lanzhou city demonstrate that the proposed model outperforms PSPNet,DeeplabV3,Segformer,and Swin-T in terms of average classification accuracy (mAcc),mean intersection over union (mIoU),and mean F1 score (m F1). In addition to improving classification accuracy,the model achieves high inference speed,showcasing its potential for applications in complex land cover scenarios and offering new insights for remote sensing image classification. Reference | Related Articles | Metrics

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A construction waste pile detection and identification method based on improved U-Net algorithm ZOU Weilin, ZHOU Wen, ZHANG Yongli, GAO Siyan, WANG Puliang Bulletin of Surveying and Mapping    2025, 0 (3): 161-167.   DOI: 10.13474/j.cnki.11-2246.2025.0328 Abstract （220）      PDF（pc） （5599KB）（142）       Knowledge map    Save At present, China's actual production and construction of large volumes of construction waste and complex composition, if not properly handled, some of the components will react with the surrounding environment to form a health hazard, resulting in immeasurable consequences. In addition, under the policy of “waste-free city”, construction waste has become an important issue in solving environmental problems. This paper proposes an improved model of U-Net algorithm based on the construction waste dataset collected independently. Relying on the original U-Net network, the model introduces ResNet residual network, wavelet transform and attention mechanism module into the backbone network, which not only effectively solves the problems of gradient disappearance and blurring of edge features of the original model, but also improves the performance indexes such as mIOU, mPA, F1 score, etc., compared with some other models, and the overall performance of the model is smoother, so that the model can successfully and efficiently accomplish the construction waste stacking and disposal. The overall performance of the model is smooth, which can successfully and efficiently accomplish the task of recognizing and detecting the construction waste dumping.Finally, the streets under the jurisdiction of Weidong district, Pingdingshan city, Henan province, are used as the experimental area for the application of the results to verify the results. The experimental results show that the identification and detection model can effectively identify and detect the coverage of construction waste, and the accuracy of identification and detection meets the requirements of practical application, which can provide important decision support for the realization of construction waste management and disposal. 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 （220）      PDF（pc） （6381KB）（105）       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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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 （216）      PDF（pc） （5938KB）（83）       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