Table of Content

25 July 2025, Volume 0 Issue 7

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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

2025, 0(7):  1-4.  doi:10.13474/j.cnki.11-2246.2025.0701

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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.

Comparative analysis of single-epoch RTK fast positioning performance for BDS-3 dual-frequency data

ZHOU Mingduan, SONG Qiao, MENG Qinglong, DU Yao, LIU Minghua, LIN Shiqi, WANG Junjie

2025, 0(7):  5-12.  doi:10.13474/j.cnki.11-2246.2025.0702

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In view to the fact that the uncertainty of performance differences between B1C+B2a and B1I+B3I signals used for single epoch RTK fast positioning in BDS-3 system.In this paper,based on the establishment of the BDS-3 single-epoch RTK fast positioning model,an implementation flowchart of RTK single-epoch double-difference integer ambiguity fast reslution algorithm for BDS-3 dual-frequency data is decuced in detail firstly.The BDS-3 single-epoch RTK fast positioning analysis software (short as SeOTF_RTK) is designed and developed,and the performance differences of single-epoch RTK fast positioning for BDS-3 dual-frequency data are compared and analyzed.The experiment results show that the positioning accuracy of BDS-3 single-epoch RTK fast positioning is basically equivalent for not only B1C+B2a data but also B1I+B3I data,and both can provide users with millimeter to centimeter level RTK surveying accuracy.For both BDS-3 static data and BDS-3 dynamic data,B1C+B2a data is better than B1I+B3I data in the success rate of RTK single-epoch double-difference integer ambiguity resolution both of which is 97.5%above,meanwhile B1I+B3I data is better than B1C+B2a data in the efficiency of RTK single-epoch double-difference integer ambiguity resolution both of which is better than 0.34 s.

Analysis of unmanned aerial vehicle RTK positioning technology based on spherical intersection AFM algorithm

WANG Peiyuan, CHENG Lin, WANG Chen, TIAN Hongying, KONG Hong

2025, 0(7):  13-18.  doi:10.13474/j.cnki.11-2246.2025.0703

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Artificial inspection is easily limited by natural environmental factors and requires a large amount of manpower support.With the development of new technology,unmanned aerial vehicle inspection has gradually attracted attention,and its positioning accuracy directly affects the performance of the drone.In order to reduce the positioning error in the inspection process of multi-rotor unmanned aerial vehicles,and to realize real-time positioning of the inspection UAV,this paper introduces a spherical intersection ambiguous algorithm.By applying the ambiguous algorithm to RTK positioning,the uncertainty and ambiguity in the positioning process can be effectively handled,for improving the positioning accuracy of the UAV.This allows the static positioning accuracy of the unmanned aerial vehicle inspection system to the centimeter-scale,and ensures smooth continuity of the dynamic measurement of the motion trajectory.

PS-InSAR monitoring and analysis of ground subsidence in large-scale water diversion project

XIONG Chunbao, AN Hewen, SU Guangli

2025, 0(7):  19-25,72.  doi:10.13474/j.cnki.11-2246.2025.0704

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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.

A real-time dynamic feature point identification method and its application in visual-inertial odometry

CAO Long, LIU Jingbin, ZHANG Wei, LI Mengxiang

2025, 0(7):  26-31.  doi:10.13474/j.cnki.11-2246.2025.0705

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Visual-inertial odometry is a widely used localization technology. Visual-inertial odometry technology relies on the assumption of a static environment. In dynamic environments, the robustness and localization accuracy of visual-inertial odometry significantly decrease. Some researchers employ semantic segmentation or object detection methods to identify dynamic objects. However, these approaches face challenges such as the inability to detect undefined dynamic objects, misidentification of static objects, and poor real-time performance. To tackle these issues, we propose a real-time identification method of dynamic feature points to enhance the accuracy of visual-inertial odometry in dynamic environments. This method performs clustering analysis on the velocity vectors of feature points in the image and estimates motion states of feature points by using epipolar matching errors. High-dynamic points are identified and removed, while weight factors are assigned to low-dynamic points. Finally, we evaluate the proposed method on the publicly available datasets. Compared with other visual-inertial odometry algorithms, the preposed approach significantly improves the localization accuracy of visual-inertial odometry in dynamic environments.

SFR-YOLO: small target detection algorithm for UAV imagery based on improved YOLOv8

SUN Jiyuan, JI Song, GAO Ding, LI Kai, ZHANG Ruiying

2025, 0(7):  32-39.  doi:10.13474/j.cnki.11-2246.2025.0706

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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.

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

2025, 0(7):  40-45.  doi:10.13474/j.cnki.11-2246.2025.0707

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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.

Remote sensing image analysis of Shaanxi provincial highway network based on PIE-Engine

DU Jianchao, ZHANG Lei, BAI Jinying, LI Tingting

2025, 0(7):  46-51.  doi:10.13474/j.cnki.11-2246.2025.0708

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The construction status of highway network is an important indicator to reflect the development of regions.It has important reference value of analyzing the construction status of highway network in Shaanxi province to study the regional development in Shaanxi province.Based on the 10 m resolution remote sensing image of 2022,this paper has realized the extraction of roads in Shaanxi province in 2022 through the joint application of PIE-Engine AI platform and ArcGIS PRO software,and analyzed the construction status of the road network in Shaanxi province according to the parameters of road mileage,road density and so on.The results show that there is a strong correlation between the road network construction and economic and geographic features in Shaanxi province,and the road network shows an overall trend of focusing on Xi'an city,the capital of the province,and gradually spreading outward.The results of this paper can provide effective data support for regional development research in Shaanxi province.

Aboveground biomass inversion of semi desertified grassland based on Landsat image: a case study of Damao banner

WANG Liqi, CHENG Bo, ZHANG Xiaoping, LI Kedong, SONG Menglong, YAN Tao

2025, 0(7):  52-57.  doi:10.13474/j.cnki.11-2246.2025.0709

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Accurately monitoring the aboveground biomass of semi desertified grasslands is a necessary condition for evaluating the ecological status of grasslands and corresponding sustainable management of grasslands. Damao banner has abundant grassland resources and a simple vegetation community structure, which is a typical representative of semi desertification grassland. In order to improve the quality of information on semi desertification grassland resources, taking Damao banner as the research area, based on Landsat remote sensing images, 23 original features are constructed using ground measured sample data, combined with spectral, vegetation index, meteorological data, and digital terrain data. Random forest (RF), support vector machine (SVM), gradient boosting regression tree (GBRT) and decision tree(CART) regression algorithms are used for grassland aboveground biomass inversion, and feature importance score and recursive feature elimination (RFE) are used for feature optimization. Finally, the 2021 semi desertification grassland AGB inversion mapping in Damao banner was completed. The results show that the RF model had the highest accuracy in the AGB inversion of semi desertified grasslands. After recursive feature elimination, the optimal number of features is selected to 12, among which meteorological and topographic features contributed the most to the AGB inversion of grasslands. The final accuracy determination coefficient (R²) of the inversion model is 0.83, and the root mean square error (RMSE) is 20.31. This study estimates the biomass of semi desertified grasslands, providing a scientific basis for the management and protection of vulnerable grassland ecosystems and an effective methodology for biomass inversion research.

Analysis of vegetation changes and influencing factors on mine-damaged land in a typical county in central Yunnan based on XGBoost-SHAP model

HE Sixuan, YANG Jiehao, ZHANG Guoyou, ZHU Daming, WANG Chong, HU Guanbing

2025, 0(7):  58-65.  doi:10.13474/j.cnki.11-2246.2025.0710

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In central Yunnan province,there are numerous areas of mining damage,and the study of their vegetation restoration is beneficial for the protection and management of the regional ecological environment.In this study,it focus on Anning city,Mile city and Malong district of Qujing city.The research utilizes Landsat remote sensing image data from 2000 to 2022,processed via the GEE platform.The annual average kNDVI index is employed to characterise the vegetation cover.Then combines with the spatial distribution of mining-affected land in the county area in 2023.The XGBoost-SHAP interpretable machine learning model,Theil-Sen and Mann-Kendall trend test,coefficient of variation and Hurst index are employed to systematically analyse the changes in vegetation cover and its influencing factors in the mining-damaged land in the study area.The study reveals that the vegetation of mining-affected land in the study area exhibites a marked degradation trend,characterised by diminished stability and the presence of positive persistence characteristics.The analysis indicates that topographic factors (such as elevation and slope) are emerged as the predominant influences on vegetation evolution,followed by soil chemical property factors (including nitrogen,phosphorus,and potassium) and soil physical property factors (such as porosity and clay content) to a least extent.The driving factor analysis method based on the XGBoost-SHAP model proposed in this study can effectively identify the key influencing factors of regional vegetation change,and provide a reference for ecological restoration research in similar regions.

GDS:drone image-guided cross-view image geographic positioning

XI Zexin, LI Jiayi, XIE Hao, GAN Wenjian, ZHOU Yang

2025, 0(7):  66-72.  doi:10.13474/j.cnki.11-2246.2025.0711

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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.

Semantic mask segmentation enhanced indoor visual global localization

LI Jie, YIN Fei, LIU Jingbin, LI Mengxiang, ZHANG Wei

2025, 0(7):  73-79.  doi:10.13474/j.cnki.11-2246.2025.0712

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In recent years,with the emergence of new technologies in the field of computer vision in image processing and feature extraction,visual indoor positioning has attracted extensive attention.Different from the relative positioning method of visual odometry,visual global positioning methods fuse images with the visual map to provide absolute pose with geographic reference data.There are various movable targets and a large number of repetitive similar textures in indoor structured scenes,which pose a challenge to visual positioning in indoor environment.In this paper,an indoor positioning method combining structure from motion (SfM) and panoptic segmentation is proposed.An environmental semantic mask is designed to remove the interference of movable targets and repeated textures in the image,and improve the accuracy and reliability of global positioning.The experimental results show that the SfM method with semantic mask can reconstruct 3D point clouds with less noise and clearer indoor structure,the positioning accuracy reaches 0.84 m(1σ).

Research on carrier phase small cycle slip detection technology

LI Xiaojiang, ZHAO Lei

2025, 0(7):  80-84.  doi:10.13474/j.cnki.11-2246.2025.0713

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Satellite carrier phase detection is one of the key methods for achieving accurate positioning.However,in practical applications,satellite signals are often obstructed by trees,high-rise buildings,and random errors,leading to reduced positioning accuracy.To address this issue,this study proposes a novel cycle slip detection algorithm that overcomes the limitations of traditional high-order difference methods in detecting minor cycle slips.The algorithm effectively eliminates errors such as receiver clock bias,satellite clock bias,ionospheric delay,and tropospheric delay through inter-receiver and inter-satellite differencing techniques.Additionally,it employs a mathematical model to fit carrier phase observations,verifying carrier continuity and precisely identifying the location of cycle slips.Experimental results demonstrate that the proposed algorithm can comprehensively detect cycle slips caused by obstructions,exhibits strong anti-interference capabilities,and significantly improves positioning accuracy and reliability.

Short-term inertial navigation assisted error compensation method for dynamic gyrocompass attitude angles

YANG Xu, HUANG Xiaojuan, ZHANG Yanshun, LIU Zhaoyang

2025, 0(7):  85-89,109.  doi:10.13474/j.cnki.11-2246.2025.0714

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The heading and attitude measurement of the strapdown gyrocompass will be greatly affected by the dynamic motion of the carrier,resulting in significant errors.Based on the analysis of the transmission mechanism of carrier motion acceleration error,a dynamic compensation method for attitude measurement error of gyrocompass is proposed using the idea of inertial navigation without divergence in a short period of time.When the carrier is in dynamic motion,a short-term inertial navigation algorithm with periodic reset is used to continuously calculate the motion acceleration in the navigation system,compensate for the disturbed accelerometer data in the compass loop,and thereby reduce dynamic measurement errors.The experimental results show that the root mean square error (RMSE) of the heading angle after dynamic compensation is 0.792°,and the pitch angle and roll angle are less than 0.030°,effectively improving the dynamic attitude measurement performance of the gyrocompass system.

Next POI recommendation with an adaptive gating mechanism embedded in graph neural networks

CHI Jinzhe, LIU Jiping, XU Shenghua, WANG Yong, WANG Zhuolu

2025, 0(7):  90-96.  doi:10.13474/j.cnki.11-2246.2025.0715

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The recommendation of the next POI has attracted much attention in location-based social networks,aiming to accurately recommend POIs based on users' historical check-ins and temporal information.However,traditional methods fail to consider temporal sequences and have low learning efficiency for graph nodes.To address this,in this paper,we propose a method for next POI recommendation by incorporating adaptive gating mechanisms into geographic and sequential graph modules.The proposed network consists of three main parts: the adaptive geographic graph module,which combines the adaptive gating mechanism with graph convolutional neural networks to adjust node fusion update weights by using gating signals; the adaptive sequential graph module,which learns user access preferences through a random walk network and enhances the weights of relevant preferences on the basis of target task attributes using adaptive gating mechanisms; and the semantic joint module,which maximizes the consistency distribution between the geographic and sequential graph modules and optimizes the loss of the joint framework via soft-label cross-entropy loss functions.To validate the effectiveness of the model,experiments are conducted on foreign datasets (Foursquare_NYC and Foursquare_TKY)and a domestic dataset(Microblog).The experimental results demonstrate that the proposed model achieves recommendation accuracies of over 85% across all datasets,with performance improvements ranging from 2.97% to 86.90% over those of state-of-the-art baseline models.

Automated construction of road intersection in high-definition map based on hidden Markov model

ZHANG Hengqi, LIN Qiannan, LI Qiaomin, YAN Yaya

2025, 0(7):  97-103.  doi:10.13474/j.cnki.11-2246.2025.0716

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With the widespread production and application of high-definition maps,the construction of map model topology has always been a key step in the automated map production.To address the challenge of automatically establishing topological relationships for high-definition map in complex scenarios such as road intersections,this paper proposes a method for automatic construction of topological relationships in high-definition maps based on a hidden Markov model road network matching strategy.By combining the matched standard road navigation map to provide topological information,lane attachments are made for road intersections in various complex scenarios.Based on the map data of several cities in Ningxia and Zhejiang,analysis and verification show that the recall rate and accuracy of various intersection models meet the requirements for automated production.Experiments prove that the algorithm in this paper can efficiently and accurately calculate the topological relationships of road intersection models,while effectively reduce the cost and cycle time of mass production of high-precision maps.

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

2025, 0(7):  104-109.  doi:10.13474/j.cnki.11-2246.2025.0717

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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 km² COSMO Skyed images in Wuhan from June 2012 to June 2024 and 32 177 km² 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.

Spatio-temporal variation of fraction vegetation cover and their driving factors in Zhejiang province from 2000 to 2023

YU Fenghua, LIU Zhenghua, HUANG Li, ZHOU Shikai, SHEN Di, YAO Juxiang, WANG Yanan

2025, 0(7):  110-117.  doi:10.13474/j.cnki.11-2246.2025.0718

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In the context of global climate change,studying vegetation dynamics and its influencing factors is of great significance for monitoring regional ecological environment quality and improves ecosystem structure.This paper uses Theil-Sen trend analysis,Mann-Kendall test and coefficient of variation method to study the spatio-temporal variation characteristics and stability of the fractional vegetation coverage (FVC) in Zhejiang province,and analyzes the driving force of vegetation change through geographic detectors.The results show that: ①On the temporal scale,the FVC fluctuation in Zhejiang province showed an obvious upward trend from 2000 to 2023,with an increase of about 3.1%/10a (P<0.05),and the multi-year mean was 56.3%. ②On the spatial scale,the spatial distribution of FVC in Zhejiang province had significant heterogeneity,and the high-value areas are mainly distributed in the northwest,south and eastern mountainous areas of the study area. ③On the trend of change,the FVC in the study area showed an increasing trend,of which the proportion of areas with significant increase is 63.84%,and the FVC is mainly low in volatility during the study period. ④Elevation and topography are the main driving factors affecting the differences in the spatial distribution of FVC in the study area,and the q values are all above 0.6. ⑤The interactive effects between the factors are higher than the influence of a single factor,and the interactions between the factors show nonlinear enhancement and double-factor enhancement relationships.The changes in FVC are most prominently affected by the interaction between land use and population density.

Phenological changes and monthly lag effects of grasslands on the Tibetan Plateau based on MODIS data

QU Peng, LI Zhen, WANG Junyan, GAO Yufeng, KANG Hongjie

2025, 0(7):  118-125.  doi:10.13474/j.cnki.11-2246.2025.0719

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Vegetation phenological changes in the Tibetan Plateau play an important role of global climate change. However, the study on the effects of climate change on phenology in this region is still insufficient. Therefore, in this study, remote sensing data MODIS (MOD13A2) NDVI was used as the data source to invert vegetation phenological changes in Tibetan Plateau grassland from 2000 to 2023. The results show that: ①The variation of precipitation and temperature over the Tibetan Plateau has obvious zonal regularity, with precipitation lower in the west and higher in the east; The overall temperature shows a trend of low in the west and high in the east. ②The 111～192 days are the main time range of SOS occurrence, accounting for 61.21% of the total area. ③In February, the positive correlation between air temperature and SOS is the highest, 55.96%. The main conclusions are as follows: ①The SOS of the Tibetan Plateau shows an overall advance trend during 2000—2023, while the EOS shows a delayed trend. ②SOS is negatively correlated with precipitation in February in the early growing season, while EOS is positively correlated with precipitation in August in the late growing season.

Evaluation of landslide susceptibility by fusing SBAS-InSAR deformation and machine learning model

LI Wendong, YE Yu, LI Xia, WEI Wei, XIN Cunlin

2025, 0(7):  126-131,146.  doi:10.13474/j.cnki.11-2246.2025.0720

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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.

Improved YOLOv11-based construction progress detection technology for photovoltaic sites

WEI Wenhao, ZHONG Cheng, GUO Jianchen, ZHANG Zhenglin, LIAN Jing

2025, 0(7):  132-137,163.  doi:10.13474/j.cnki.11-2246.2025.0721

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As an important branch of renewable energy,the photovoltaic industry has ushered in explosive growth under the background of “double carbon”.The traditional means of progress management during the construction period of photovoltaic projects are no longer sufficient to meet the needs of on-site managers,and there is an urgent need for an accurate and efficient construction progress automation management method.Current target detection algorithms are limited by the difficulty of feature extraction in complex environments and low detection accuracy,and can not be applied to string identification in photovoltaic fields.Based on this,this paper proposes a PV field construction progress detection technology: firstly,use a drone to take remote sensing images of the PV field,and automatically stitch and crop the image data; secondly,use the improved YOLOv11 model to identify the images,identify the objects including PV panels and racks,and compare the coordinates of the identified objects with the CAD layout plan; finally,generate the accurate construction progress information for site management.Relying on the data collected from the actual project for analysis and verification,the results show that,the progress recognition error of this technology for PV panels and brackets is5.2% and 9.5% respectively,which is smaller than the real results,and considering the improvement of the generalisation ability of the model after iteration,it can meet the requirements for practical use.The improved YOLOv11 model used in this technology enhances the detection performance in complex environments by introducing BiFPN and SEAM modules,and achieves 91.8%mAP@0.5,93.4%accuracy and 89.8%recall,which are 1.6%,2.2%,1.2%and 4.2%,3.6%,3.1%respectively compared with YOLOv11 and YOLOv10 models.

Application of UAV airborne LiDAR in the calculation of reclamation volume in coastal reclamation areas

WANG Ruzheng, Lü Lilei, REN Xiaodong

2025, 0(7):  138-141.  doi:10.13474/j.cnki.11-2246.2025.0722

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In order to solve the difficulties of manual surveying of coastal reclamation areas,the accuracy and density of points can not meet the needs of high-precision reclamation calculation,this paper uses the UAV airborne LiDAR point cloud to calculate the reclamation amount and presents a complete technical flow which is suitable for UAV airborne LiDAR point cloud classification,high precision digital elevation model modeling and calculation of the amount of blow fill in coastal reclamation area.The results show that the UAV LiDAR technology can effectively solve the problem of high-precision calculation of the amount of blow fill in coastal reclamation projects,and has significant application advantages and broad development prospects.

Analysis of the influence of densified gravity survey on the provincial quasi-geoid model

WU Zhiwen, GENG Xiaoyan, TANG Guoli, WANG Yixin, ZHOU Wei, ZHANG Sijun

2025, 0(7):  142-146.  doi:10.13474/j.cnki.11-2246.2025.0723

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Gravity data is basic data to determine the quasi-geoid model,which directly affects the medium and short wave components of the quasi-geoid.In this paper,the influence of gravity data on the quasi-geoid is analyzed by the variation of height anomaly.Firstly,the reference gravity field model,gravity data and topographic data are used to determine the quasi-geoid model based on Molodensky principle.Secondly,the quasi-geoid is calculated based on the same strategy by adding the gravity data in the sparse area.Finally,the difference of the quasi-geoid model in the changing area of gravity data is analyzed.The result shows that the variation range of the new quasi-geoid model is -3.7～4.0 cm after the addition of gravity data,and the proportion of the region with the absolute value of change greater than 1 cm is 7.8%of the whole region.In addition,the area with significant height anomaly changes are mainly located in complex mountainous area,which further highlights the importance gravity data in area with complex topographical features.

Subway tunnel disease detection method based on linear array camera

WANG Zhi, ZHAO Yabo, SUN Haili

2025, 0(7):  147-151.  doi:10.13474/j.cnki.11-2246.2025.0724

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As the operation time of subway tunnels increases,common diseases such as lining peeling,cracks,and water leakage are prone to occur due to complex factors such as geological conditions,natural disasters,construction quality,and surrounding environment.At present,the inspection method mainly relies on manual visual inspection,which is limited by various factors such as tunnel section,skylight time,and lighting conditions,making it difficult to improve tunnel inspection with high precision and efficiency.A subway tunnel disease detection system based on a vehicle-mounted multi-eye camera is designed.A special inspection vehicle is used as a carrier platform,and hardware facilities such as linear array cameras,light sources,and power supplies are integrated.The inspection vehicle is used to move along the track at a uniform speed and perform 2D scanning in the vertical direction to collect high-resolution color image data of the tunnel surface.Based on the spliced images,the information of tunnel cracks,leakage,peeling,and other diseases can be judged and identified.Compared with traditional methods,the detection system based on vehicle-mounted linear array cameras can achieve full coverage and rapid collection of tunnel space information,and is convenient for digital archiving and disease development trend analysis.

Observed deformation performance of 40 m ultra-deep foundation pit project in the Guangdong-Hong Kong-Macao Greater Bay Area

ZHONG Heng, YUAN Qiang, LIAN Changjiang, HUANG Xiaocheng, JIA Shiqiang, WEN Xuanyue

2025, 0(7):  152-158.  doi:10.13474/j.cnki.11-2246.2025.0725

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With the development of the national strategy for the Greater Bay Area,deep foundation pits have been widely applied.It is necessary to study the deformation law of deep foundation pits in the deep soft soil layers of the Greater Bay Area to provide reference for design and construction.Based on the 40 m deep foundation pit project in the China Greater Bay Area,the deformation characteristics of the diaphragm wall during the excavation process are investigated comprehensively.During the excavation stage,the deformation rate of the wall caused by the excavation of the marine soft soil layer is 2～6 mm/d,increasing with the excavation depth; the maximum deformation occurs below the excavation surface.At the end of excavation,the maximum lateral displacement of the wall is mainly between 0.1% and 0.5% H_e. The deformation data of the wall indicates that excavation of the marine soft soil layer is the main cause of wall deformation,which needs to be taken seriously in the design and construction stages to ensure the safety of the deep foundation pit itself.

Construction of a three-dimensional spatio-temporal database for natural resource investigation and monitoring: taking Jiangsu province as an example

CHEN Chao, TAO Yang, ZHANG Daqian

2025, 0(7):  159-163.  doi:10.13474/j.cnki.11-2246.2025.0726

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The 3D spatio-temporal database for natural resource survey and monitoring is an important component of natural resource management, serving as a foundation, a set of data, and a platform. It is the data support for the national spatial basic information platform. This article takes Jiangsu province as an example to sort out and summarize the natural resource survey and monitoring work deployed in Jiangsu after the “two unifications”, and proposes a 3D spatio-temporal database integration construction path for natural resource survey and monitoring coordinated at the provincial, municipal, and county levels to ensure the vertical connection of survey and monitoring results at the provincial, municipal, and county levels. This construction concept has been successfully validated in the construction of a 3D spatio-temporal database and management system for natural resource survey and monitoring in Jiangsu province, which can provide certain reference and inspiration for the work in other regions.

Natural resource ecological risk assessment and characterization of spatial and temporal leaps in the pearl river delta urban agglomeration in the context of high-quality development

WANG Nan, CHEN Min, LIU Jiamin, WU Hui

2025, 0(7):  164-168.  doi:10.13474/j.cnki.11-2246.2025.0727

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Coordinating the relationship between urban development and natural resources ecology is a major issue considered in the context of high-quality development.In this paper,by constructing the driver-pressure-state-impact-response (DPSIR) natural resources ecological risk evaluation index system,combining the Gray-TOPSIS multi-objective decision-making evaluation methodology and exploratory spatio-temporal data analysis meth-odology,we analyze the natural resources ecological risk of the pearl river delta urban agglomeration.The spatio-temporal pattern and its spatio-temporal leap evolution characteris-tics are analyzed.The study shows that: ①More than three quarters of the cities in the PRD economic circle will experience a gradual decrease in natural resource ecological risk from 2012 to 2022,but more than half of the cities will experience medium risk and above,with an overall high level of risk. ②The eastern and southern parts of the PRD urban agglomeration exhibit more dynamic spatial structural characteristics,and the northwestern part of the urban agglomeration will be more influenced by neighboring cities,with a greater spatial and temporal influence on the level of risk of natural resources.Neighboring cities,with stronger spatial and temporal dependence,and more than 50%of the cities' natural resource ecological risks show strong spatial integration in the evolution of spatial patterns.③The spatial cohesion of Moran's I is only 11.11%,indicating that the ec-ological security level of each city in the PRD urban agglomeration has a large pattern of change,and its own relative position is prone to change.

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

2025, 0(7):  169-173.  doi:10.13474/j.cnki.11-2246.2025.0728

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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.

Response of the temporal and spatial pattern of carbon storage to land use change in typical black soil region

LIANG Xin, LU Tingjun, LIU Xing, MEI Xiaodan

2025, 0(7):  174-179.  doi:10.13474/j.cnki.11-2246.2025.0729

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The typical black soil area of Heilongjiang province has the dual attribute of “commercial grain base and fragile carbon pool system”,and regional land use change affects carbon storage transformation and food security.At present,there are limited studies on the spatio-temporal evolution of carbon storage and land use response in black soil region from the perspective of long time series and multi-spatio-temporal overall.Based on the spatial analysis functions of ArcGIS software,such as spatial autocorrelation,cluster analysis and barycentric analysis,and InVEST model,this study quantitatively and visualises the impact of land use change on the spatio-temporal pattern of carbon storage in typical black soil regions from 1990 to 2020.The results showed that:①In the past 30 years,cultivated land was the dominant land type in the study area,and the overall growth trend was 64%,the area of forest land and grassland fluctuated,the water area continued to shrink year by year,and the unused land and construction land showed an increasing trend,with an increase or decrease of about 6%.②The carbon storage in the typical black soil area showed a “growing-decent-decline”fluctuation,with a total decrease of 7.28×10⁷ t in the past 30 years,in which the carbon storage of cultivated land increased by 12.65×10⁷ t,while the carbon storage of forest land,grassland and water area decreased by 21.85×10⁷ t.③From the perspective of the direction of the change of the center of gravity,the carbon storage showed a “Z-shaped”trend of “northwest-northeast-west”,showing a certain spatial convergence phenomenon,which first increased and then weakened.The high-high concentration was obvious in the north,and the low-low concentration was mainly in the west and south.④Land use transfer is an important factor in the change of carbon storage,and cultivated land is the main carbon reservoir in the study area,accounting for 66%of the total carbon.The conversion of grassland to cultivated land and unused land to cultivated land is the key reason for the increase of carbon storage,and construction land contributes the least.Changes in water-unused land lead to greater loss of carbon stocks.The research can provide scientific basis for food and ecological security decision-making in typical black soil areas.

Geographical information technology enabling the construction and analysis of long-term surface temperature dataset in Fujian province

LI Wene, LAI Xiaolong, SUN Hua, HUANG Lianying

2025, 0(7):  180-184.  doi:10.13474/j.cnki.11-2246.2025.0730

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Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales,linking the heat fluxes and interactions between the ground and atmosphere.Based on GIS/RS,combined with the MODIS 8-day LST products (MOD11A2) from the split-window algorithms,we construct and obtain the monthly and annual LST dataset of Fujian province from 2000 to 2016.Then,Using geospatial information technologies,we analyze the monthly and yearly time series LST data and further investigate the LST distribution and its evolution features.The average LST of Fujian province reaches the highest in July,while the lowest in January.The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2016.The spatial distribution shows that the LST in North and West is lower than South and East in Fujian province.With the rapid development and urbanization of the coastal area in Fujian province,the LST in coastal urban region is significantly higher than that in mountainous rural region.The LST distributions might are affected by the climate,topography and land cover types.The spatio-temporal distribution characteristics of LST using geospatial information technologies could be regarded as crucial evidences in agricultural layout and provides important reference for the formulation of regional ecological environment protection and sustainable development policies in Fujian province.