Table of Content

25 September 2026, Volume 0 Issue 9

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Innovation and practice of excellent talents cultivation in geomatics for strong nation based on 3D interpenetration and digital-intelligence empowerment synergy

Li Jiancheng, Jiang Weiping, Zou Jingui, Xu Caijun, Zou Bin, Gao Jingxiang, Guo Baoyu, Feng Huihui, Li Shenxin

2026, 0(9):  1-6.  doi:10.13474/j.cnki.11-2246.2026.0901

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[Purposes]Aiming at the strategic objectives of national surveying and mapping for the new era,this research explores an excellent talent cultivation model empowered by 3D integration and digital-intelligence strategies.[Methods]Guided by the philosophy of empowering through spatio-temporal information and digital intelligence,we build digital-intelligent teaching resources based on a strategy that integrates science and education.Through the synergy of competition and teaching,we enhance educators' pedagogical capabilities,and by innovating a model that connects competition with entrepreneurship,we strengthen students' innovative capacities.[Findings]The top-level design has established a new digital-intelligence driven-science-education integration-competition-teaching synergy-competition-innovation interpenetration(DISI)outstanding talent cultivation system in service of the national strategy for strengthening the country through surveying and mapping.It has pioneered a new pathway for the co-construction and sharing of immersive digital-intelligence teaching resources,created a new mechanism for the synergistic enhancement of teacher education capabilities across multiple levels and regions,and built a new ecosystem that strengthens the cultivation of innovation capabilities for outstanding surveying and mapping talents.[Conclusions] The achievements have been implemented in universities nationwide,expanded across sectors,and disseminated internationally,establishing a replicable and transferable paradigm for excellence in talent development．

Status and prospect of navigation and positioning in underground space

Li Linyang, Zhao Dongqing, Zhou Qingyong, Lai Luguang, Yuan Junyan, Hui Jun, Fan Wenzhe, Li Pengfei

2026, 0(9):  7-18.  doi:10.13474/j.cnki.11-2246.2026.0902

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The navigation and positioning in underground spaces have become one of the key areas for building a comprehensive positioning，navigation and timing(PNT)system.Focusing on underground space,the space classification based on navigation scenarios is conducted firstly,and the challenges faced by navigation and positioning are summarized systematically from seven aspects,including signal availability and scene structure.Secondly,the current main technologies and methods are sorted out in the aspects of multi-source sensor fusion,preset positioning base stations,prior matching,muon,and quantum and other technologies,and open-source multi-sensor datasets for underground space are also given.Finally,the future development trends are discussed in the aspects of front-end perception,sensors and platforms,cluster collaboration,and back-end optimization．

Urban resilience assessment using Beijing spatial monitoring data

Huang Qiang, Yu Yongxin, Liu Yaqun

2026, 0(9):  19-26.  doi:10.13474/j.cnki.11-2246.2026.0903

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[Purposes] With the policy emphasis on a holistic approach to national security，resilient city development，and the modernization of megacity governance.It is necessary to leverage Beijing's spatial monitoring products to conduct resilience assessment on a unified surveying-and-mapping baseline and to identify weaknesses and spatial risks.[Methods] This study uses Beijing spatial monitoring products as the main data source.It examines four dimensions： urban carrying capacity，sponge city resilience，risk prevention and operational security maintenance.It applies spatial statistical analysis，global Moran's I，the spatial Gini index and population coverage rate to assesses Beijing's resilience.[Findings] Population and development intensity are excessively concentrated in the core urban area，and stormwater retention capacity is insufficient.Hazardous facilities are clearly spatially separated from the resident population，and public safety and emergency service capacity is relatively weak in suburban areas.[Conclusions] Beijing spatial monitoring provides continuous and consistent surveying and mapping data support for urban resilience assessment.Its products can inform territorial spatial planning and urban safety governance decisions and help enhance the overall resilience of the capital city．

A study on the spatial patterns and spatial separation characteristics of urban residence,workplace,and activity spaces based on mobile phone positioning data

Ke Rihong, Xiao Liang, Wang Pu

2026, 0(9):  27-33.  doi:10.13474/j.cnki.11-2246.2026.0904

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[Purposes]In response to the increasing separation of residential,workplace,and activity spaces in cities,this study investigates the spatial distribution and separation patterns of home-work-activity (H-W-A)locations of residents in Changsha,providing insights for optimizing urban spatial structure and transportation management.[Methods]Using large-scale mobile phone positioning data,an H-W-A identification framework was developed to extract residents' home,work,and major activity locations.Spatial separation indices were applied to analyze H-W-A patterns and variations across different socio-demographic groups.[Findings]Residential locations exhibit multi-center expansion,while workplaces and activity locations are highly concentrated in the city center.Suburban residents show significantly higher H-W-A separation,which increases in a ring-shaped pattern with distance from the city center.Travel behaviors vary markedly by gender,income,and age.[Conclusions]H-W-A spatial separation in Changsha displays pronounced heterogeneity and socio-demographic differences.Coordinated job-housing planning and optimized public services are recommended to reduce travel burdens and enhance urban efficiency．

Study on the differentiation patterns of urban surface heat islands： a case study of Zhengzhou city

Zhang Jingdong, Zhang Ruolan, Huang Jingjing, He Ruizhen

2026, 0(9):  34-40,48.  doi:10.13474/j.cnki.11-2246.2026.0905

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[Purposes]Local climate zones (LCZ)have become the foundation of urban climate research.Understand the patterns of spatial variation in surface temperature within a local climate zone is of great significance for improving the living environment in these areas.[Methods]The study selected three typical local climate zones in Zhengzhou： multi-story residential areas,high-rise residential areas,and institutional land.Using the multi-scale geographical weighted regression(MGWR）method,it explored the relationship between normalized vegetation index (NDVI),green space,shade,impervious surface area,building height,and sky view factor (SVF)with land surface temperature (LST).[Findings] The results showed that the three types of land-use affected land surface temperature were different,and NDVI had the greatest impact on institutional land use areas,with a bandwidth of 27.88 m.The most influential landscape parameter in the high-rise residential is shading with a bandwidth of 2 728.6 m,but the most influential parameter in the multi-storey residential is impervious ground with a bandwidth of 20.93 m.[Conclusions]The influence of 3D landscape and the combination of landscape on urban land surface temperature was clarified,and it was found that the bandwidth of NDVI and building height was less than 30 m,which further narrowed the scale of urban climate research.It shows that the living environment in LCZ can be changed through the adjustment of landscape elements．

City region-of-interest recommendation based on adaptive anchor graph representation learning

Ma Menghe, Xu Shenghua, Jiang Shunshun, Li Junchen, Wang Zhuolu, Huang Xinhao

2026, 0(9):  41-48.  doi:10.13474/j.cnki.11-2246.2026.0906

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[Purposes]Region-of-interest(ROI)recommendation is a critical application within location-based social networks.Addressing the limitations of existing methods in adequately expressing diverse inter-regional correlations and mining implicit social relationships,this paper proposes an urban ROI recommendation method based on adaptive anchor graph representation learning.[Methods]Specifically,we leverage dual-view constraints—incorporating geospatial structure and functional semantics—to optimize an adaptive anchor graph.A node-anchor message passing mechanism is then designed to capture complex inter-regional correlations,thereby generating robust regional preference features.Furthermore,we mine implicit social associations based on the overlap of user check-in regions and the similarity of activity times to extract implicit social preference features.Finally,these two types of features are fused to predict user preferences for specific regions.[Findings]Extensive experiments on the Sina Weibo and Foursquare datasets demonstrate that the proposed method achieves significant improvements in terms of recall and normalized discounted cumulative gain (NDCG).[Conclusions]This study enables personalized decision support for LBS applications including smart tourism and business management．

Construction of waterfowl ecological network in Suzhou city by coupling MSPA technology and maximum entropy model

Chen Yi, Qu Jun, Song Runze, Chen Chao, Yang Zhaohui

2026, 0(9):  49-54.  doi:10.13474/j.cnki.11-2246.2026.0907

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[Purposes]Accurately assessing the ecological network of waterbirds in Suzhou is of great significance for urban waterbird ecological conservation.[Methods]Based on MSPA technology,the maximum entropy(MaxEnt)model,and the Linkage Mapper tool,a waterbird ecological network was constructed,and its network structure and robustness were assessed.[Findings]The results indicate： ①Ecological sources are predominantly distributed in the densely water-networked areas of the southwest,while ecological corridors exhibit a spatial pattern of dense in the west-central area,sparse or absent in other regions.②The average clustering coefficient of the network is 0.22,the average shortest path length is 3.21,and the global efficiency is 0.40,indicating good overall connectivity but weak clustering,with key nodes playing a significant role in structural control.③Under malicious attacks,network robustness declines significantly； an attack on a single node can cause a 10% network failure,reflecting the vulnerability and importance of key nodes.[Conclusions]The waterbird ecological network constructed in this study achieves local stability while maintaining global connectivity,providing a reference for waterbird ecological conservation research and offering scientific support for spatial decision-making and sustainability in waterbird protection from the perspective of complex network theory．

Research on Stacking ensemble remote sensing retrieval model for total phosphorus concentration in water with few-shot data

Liang Wenguang, Li Gang, Wang Yeyu, Li Heng, Han Yingcai, Qi Yiheng

2026, 0(9):  55-60,82.  doi:10.13474/j.cnki.11-2246.2026.0908

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[Purposes]Total phosphorus (TP)concentration is a key indicator for measuring eutrophication and ecological health in water bodies.Traditional manual sampling methods are costly and have limited spatial coverage.Although satellite remote sensing inversion technology is an effective alternative,it faces challenges in model fitting and generalization ability when the sample size is insufficient.[Methods]To overcome the accuracy limitations under small sample conditions，this study proposes a Stacking integrated remote sensing inversion model for small sample data.By constructing a multidimensional spectral feature set to mine effective information,combined with recursive feature elimination (RFE)and turbidity mediation model to optimize input feature structure and physical consistency,heterogeneous base learners such as XGBoost,LightGBM,random forest,CatBoost,and partial least squares regression (PLSR)are integrated,and high-order fusion is performed by meta learners.[Findings]In the Sentinel-2 image experiment,the model achieved an inversion accuracy of R² better than 0.92.[Conclusions]This method significantly improves the robustness and generalization ability of the model under small sample conditions,providing a reliable method for remote sensing monitoring of total phosphorus in water bodies．

The impact of glacier changes on basin water resources in the Hexi Corridor and future scenario projection

Yang Wenxuan, Li Xia, Hu Xiaojuan, Qin Xiang, Wang Renjun

2026, 0(9):  61-66,74.  doi:10.13474/j.cnki.11-2246.2026.0909

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[Purposes] Amid accelerating global warming,the sustained retreat of glaciers in the Qilian Mountains poses a direct and growing threat to water security in the Hexi Corridor.This study aims to reveal the impacts of glacier changes on regional water resources.[Methods] This study focuses on the five inland river basins of the Hexi Corridor，we integrated the variable infiltration capacity (VIC)hydrological model with an enhanced glacier mass-balance module,using glacier inventories,meteorological observations,streamflow measurements,and CMIP6 climate projections.Conduct simulation studies on glaciers and meltwater runoff.[Findings] The VIC model enhanced glacier mass-balance module demonstrates good applicability in the study area,with average Nash-Sutcliffe efficiency(NSE)values of 0.80 during the prediction period and 0.78 during validation.Regional glaciers generally exhibit a retreat trend,with significant retreat intensity observed for large glaciers in the western section and relatively gradual shrinkage of smaller glaciers in the eastern section； the contribution of glacial meltwater to runoff is progressively declining.Under the SSP5-8.5 scenario,glacier runoff decreases sharply,leading to rapid deterioration of its hydrological regulation function.[Conclusions] The research findings reveal the spatial variations in glacial-runoff evolution within the Hexi Corridor and its implications for future water resource risks,providing insights that support optimal regional water resource allocation and climate change adaptation．

Temporal and spatial variation of groundwater in North China Plain using data from GRACE satellites and monitoring wells

Wang Ziyi, Tao Tingye, Fang Xingbo, Dai Ju, Qu Xiaochuan

2026, 0(9):  67-74.  doi:10.13474/j.cnki.11-2246.2026.0910

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[Purposes] To address the issue of groundwater depletion caused by overexploitation in North China Plain,this study aims to investigate the impacts of precipitation and human activities on regional groundwater changes.[Methods] Groundwater storage (GWS)changes in North China Plain from 2002 to 2024 were retrieved using GRACE satellites,and joint analysis was conducted in combination with in-situ well observations.[Findings] The results show that GWS in North China Plain has long been in a deficit state but has exhibited a recovery trend since 2021.Both GRACE satellite data and groundwater well observations confirm that GWS increased significantly in 2021 and 2022,and the interannual trends (initially decreasing and then increasing)and seasonal characteristics revealed by the two datasets are generally consistent.Spatially,severely depleted areas are concentrated in the piedmont plain and the vicinities of Handan and Beijing.After heavy rainfall events from 2021 to 2023,most regions began to recover.In addition,the South-to-North Water Diversion project has played a positive role in mitigating regional groundwater depletion.[Conclusions] The study demonstrates that rainfall and human interventions such as inter-basin water transfer have significant impacts on groundwater recovery in North China Plain.The groundwater system in this region shows a certain restorative trend under the combined effects of natural and anthropogenic factors．

MFG-SCDNet:multi-scale awareness and change-guided fusion network for semantic water body change detection

Wang Yihong, Wei Yong, Wang Dongmei, Zhang Peng

2026, 0(9):  75-82.  doi:10.13474/j.cnki.11-2246.2026.0911

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[Purposes]Aiming at the problem that traditional change detection methods struggle to distinguish subtle water body changes from semantic categories,this study proposes a deep learning model capable of accurately identifying change regions and outputting their semantic categories.[Methods] A multi-scale feature-guided semantic change detection network (MFG-SCDNet)is designed,which adopts a dual-stream encoder-decoder architecture and integrates a grouped multi-scale aggregation (GMCA)module,an adaptive feature selection (AFSF)module,and a change-aware attention module (CAAM)to achieve multi-scale feature extraction and semantic classification of change regions.[Findings]Experiments on the SECOND and Landsat-SCD datasets show that MFG-SCDNet outperforms mainstream methods across all evaluation metrics.On the SECOND dataset,it achieves an mIoU of 73.63%,a SeK of 23.04%,and an OA of 87.73%.On the Landsat-SCD dataset,it achieves an mIoU of 88.98%,a SeK of 59.34%,and an OA of 96.14%.Ablation experiments verify the effectiveness of each module.[Conclusions] MFG-SCDNet can effectively improve the accuracy and consistency of water semantic change detection,providing a reliable technical means for water resource remote sensing monitoring.

Remote sensing estimation of tropical forest carbon storage in Xishuangbanna using GEE

Wang Qin, Shi Qian, Pei Chengbin, Chang Zheng, Pei Baohong, An Sanping

2026, 0(9):  83-88.  doi:10.13474/j.cnki.11-2246.2026.0912

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[Purposes]To address the issues of scarce ground samples and insufficient accuracy in carbon stock estimation in tropical cloudy forest regions,this study conducted a remote sensing inversion of carbon stock at the regional scale.[Methods]Based on Sentinel-2 multispectral imagery and WCMC carbon density data,a multi-dimensional feature set integrating spectral reflectance,vegetation indices,tasseled cap transformation components,and topographic factors was constructed.A random forest regression model was then employed to estimate carbon stock.[Findings]On the validation set,the model achieved a correlation coefficient of 0.72 and an RMSE of 16.10 tC/hm².Feature analysis indicated that the shortwave infrared bands (B11,B12)and the tasseled cap wetness component (TC_W)were the dominant predictors,with a cumulative importance exceeding 0.28,while topographic factors played a supplementary role.[Conclusions]The estimated total forest carbon stock in Xishuangbanna is approximately 158.89 TgC,exhibiting a spatial pattern of higher carbon density in peripheral areas and lower density in the central region.Carbon density increases with elevation,while low-elevation valley areas show significant reductions due to human activities．

Analysis of the spatio-temporal evolution characteristics and driving factors of sand-dust weather in the sandy areas on both sides of the Longyangxia Reservoir

Zhang Lechun, Zhang Dengshan, Dong Fengling, Tuo Wanbing

2026, 0(9):  89-94,137.  doi:10.13474/j.cnki.11-2246.2026.0913

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[Purposes] This study aimes to elucidate the dynamic mechanisms of wind-sand activities around the Longyangxia Reservoir.[Methods]Focusing on the Talatan and Mugetan sandy areas on both sides of the reservoir,this research systematically analyzed the spatiotemporal characteristics and driving factors of dust weather based on meteorological and remote sensing data from the winter and spring seasons of 2019—2023.The sand drift potential (DP)was introduced as a comprehensive wind dynamic indicator,combined with factors such as vegetation coverage and precipitation.[Findings] ①The number of dust days in the study areas showed an overall increasing trend during winter and spring,with significant spatial heterogeneity.Dust activity intensity in Talatan (monthly average of 1.83 d)was much higher than that in Mugetan (monthly average of 0.17 d).②Dust days were highly positively correlated with DP,with Pearson correlation coefficients of r=0.794 in Talatan and r=0.857 in Mugetan.③Both vegetation coverage and precipitation showed low to moderately low negative correlations with dust days,with correlation coefficients of -0.343 and -0.186 in Talatan and Mugetan respectively.[Conclusions] Dust weather in this region is primarily driven by wind forces.Compared to wind speed or frequency of sand-driving winds,DP provides a more comprehensive representation of the wind-sand dynamic process,particularly offering superior explanatory power in areas with generally weaker wind forces,such as the Mugetan area．

Spatio-temporal variations and spatial heterogeneity of ecological environmental quality in the Altay region

Yuan Guihao, Cui Dong, Jiang Zhicheng, Hou Yao, Wang Weiwei, Wang Yinuo, Zheng Yixuan, Gong Xin, Yu Yupei

2026, 0(9):  95-100.  doi:10.13474/j.cnki.11-2246.2026.0914

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[Purposes]To address the limited understanding of ecological vulnerability,spatio-temporal variations in ecological environmental quality,and their driving mechanisms in the Altay region,this study systematically investigates the spatio-temporal dynamics of ecological environmental quality and its influencing factors.[Methods]First,multi-period MODIS data were acquired from the Google Earth Engine(GEE)platform,and the remote sensing ecological index (RSEI)was constructed using principal component analysis to comprehensively characterize regional ecological environmental quality.Then,Sen's slope estimator,the Mann-Kendall test,and the coefficient of variation (CV)were applied to identify spatiotemporal trends and stability patterns.Finally,an XGBoost model combined with SHAP was employed to quantify spatial heterogeneity and determine the dominant driving factors.[Findings]The results indicate that：① The mean RSEI exhibited an overall fluctuating downward trend over the past 20 years,with ecological environmental quality mainly classified as “poor” and “relatively poor，” and a spatial pattern characterized by better conditions in the north than in the south；②Approximately 67% of the region experienced a declining trend,while only 1.2% showed significant improvement,and 87.3% of the area displayed low or relatively low variability,indicating generally high stability； ③Evapotranspiration and slope were the dominant factors governing the spatial heterogeneity of ecological environmental quality.[Conclusions]These findings provide scientific support for ecological conservation and zonal management strategies in the Altay region．

Photovoltaic equipment segmentation using YOLOv8 and RepViT from a UAV perspective

Zhong Wenping, Zhang Xinchang, Huang Yu

2026, 0(9):  101-107.  doi:10.13474/j.cnki.11-2246.2026.0915

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[Purposes] To address the challenges of small-scale and densely distributed rooftop photovoltaic (PV)modules in high-altitude UAV imagery,this study proposes an efficient,automated method for precise PV identification and area estimation to support regional-scale monitoring of distributed PV assets.[Methods] An improved instance segmentation model is developed based on YOLOv8-seg by replacing the original backbone with a lightweight RepViT architecture,utilizing structural re-parameterization to enhance global feature modeling and representation efficiency.Furthermore,a multi-stage feature fusion strategy is employed in the neck network to strengthen the perception of small-scale modules,and a mask-based physical area estimation model is constructed by integrating ground sampling distance(GSD).[Findings] Experiments conducted on UAV datasets from Zengcheng district,Guangzhou,show that the improved model achieves a Box mAP ₅₀ of 95.4% and a Mask mAP ₅₀ of 94.5%,representing precision increases of 3.4% and 2.9% respectively compared to the baseline.In large-scale practical tests,the model successfully achieved accurate multi-scale photovoltaic area calculation spanning three orders of magnitude, with errors meeting the requirements of engineering applications.[Conclusions] The proposed method effectively reduces parameter count while significantly improving segmentation accuracy and inference speed,providing a high-precision,automated technical solution for the census and fine-grained management of distributed rooftop solar resources．

An identification method for Eolagurus luteus burrows in large-scale UAV images

Huang Mingqin, An Jiale, Zheng Jianghua, Tuerxun Nigela, Liu Li, Wang Nan, Lin Jun, Liu Xuan, Xu Yapeng, Li Xuan

2026, 0(9):  108-114.  doi:10.13474/j.cnki.11-2246.2026.0916

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[Purposes]To address the inefficiency of manual detection caused by the small size and wide distribution of Eolagurus luteus burrows,this study aims to develop an automated identification method based on large-scale UAV imagery.[Methods]An end-to-end automated detection framework was proposed,with its core being an improved YOLO-Burrow model.Based on YOLO11n，the model is optimized through a lightweight strategy,the introduction of the Monte Carlo attention (MCA)mechanism,and the adoption of the Shape-IoU loss function.[Findings]Experiments on a self-constructed dataset showed that the model achieved an average precision (AP) of 87%,a reorganized accuracy (RA)of 97%,with a compact model size of only 0.89 MB,significantly outperforming the baseline model.[Conclusions]The proposed framework enables accurate,automatic detection and density assessment of Eolagurus luteus burrows in large-scale UAV images,providing reliable technical support for grassland rodent pest monitoring and control．

A sparse-map-based lightweight and robust LiDAR-inertial localization method

Wei Mingyang, Deng Minwei, Zhang Shunzheng, Yang Junfu, Zhou Baoding, Song Jiangbo

2026, 0(9):  115-122,163.  doi:10.13474/j.cnki.11-2246.2026.0917

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[Purposes]To address the high computational resource consumption of traditional map-based localization methods in large-scale complex environments,which makes efficient deployment on embedded platforms challenging,this paper proposes a lightweight autonomous localization method integrating LiDAR and inertial navigation.[Methods]The method is built upon sparse map construction,optimizing the scan matching process by extracting local planar features to reduce computational overhead.A time-interval-based map matching strategy combined with a temporal threshold correction mechanism is adopted to avoid per-frame global matching,effectively balancing the computational load.Multi-source sensor data and map matching information are further fused,and a strongly convergent nonlinear geometric observer is introduced to optimize pose estimation.[Findings]Experimental results demonstrate that the proposed method outperforms existing state-of-the-art algorithms in overall performance,with its engineering practicality validated through field inspection deployments at Shengli petrochemical general plant.[Conclusions]The method achieves high-accuracy and robust autonomous localization on resource-constrained embedded platforms,offering a viable technical solution for reliable navigation of unmanned systems in large-scale complex environments．

Value formation and evaluation indicator system of high-definition map under vehicle-road-cloud integration

Ying Shen, Pu Beiji, Tao Lu, He Feng, Sun Guorui

2026, 0(9):  123-130.  doi:10.13474/j.cnki.11-2246.2026.0918

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[Purposes] Under the vehicle-road-cloud integrated architecture,the value formation of high-definition map (HDMap)data is characterized by staged evolution,multi-stakeholder participation,and dynamic accumulation.It is therefore necessary to establish a value evaluation indicator system suited to its scenario-specific characteristics.[Methods] Based on Marx's labor theory of value and from the perspectives of value transfer and value valorization,this study examines the value formation logic of HDMap data in single production,multiple production,and feedback optimization,as well as the corresponding market structures,in relation to the three stages of data circulation： pre-production,production,and application.[Findings] A stage-specific value evaluation indicator system for HDMap data assets is constructed,including 11 evaluation dimensions： data quality,compliance governance,information richness,cost input,perception coordination,decision support,control execution,public governance value valorization,data empowerment,platform coordination,and business model.[Conclusions] The proposed indicator system can systematically characterize the value formation process of HDMap data assets and provide a foundation for their value evaluation and subsequent monetary valuation．

Research on the production technology of multi-source foreign language geographical names

Fan Jing, Chen Xingyu

2026, 0(9):  131-137.  doi:10.13474/j.cnki.11-2246.2026.0919

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[Purposes]Countries around the world attach great importance to the modernization of geographical name information management.At present，China also needs to expand the reserve of bilingual geographical name information and establish a global coverage bilingual geographical name database.[Methods]Based on multi-source foreign language geographical name data，this paper designs a geographical name production technical process based on multi-source foreign language geographical name materials.It conducts research and discussion on key links in the technical process，including data collection and analysis，data preprocessing，transliteration and free translation of foreign language geographical names，integration of multi-source geographical names，and revision of derived geographical names.[Findings]A standardized and engineered production process for multi-source foreign language geographical names has been initially established.[Conclusions]To further improve the efficiency of geographical name production and the quality of geographical name results，this paper focuses on analyzing the current technical shortcomings in key links such as geographical name translation and multi-source data integration，providing improvement ideas for the large-scale production of multi-source foreign language geographical names．

An image-based building change detection method based on Transformer-HRNet collaboration

Nie Ke, Xiao Zuxiang, Han Sai, Hong Yu, Liu Yun, Zhang Xuerui, Zhao Jun

2026, 0(9):  138-143,176.  doi:10.13474/j.cnki.11-2246.2026.0920

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[Purposes]Traditional building change detection methods for remote sensing images have limitations in preserving high-resolution details and global semantic modeling of complex scenes.Aiming at the demands of regular,automatic and regular,automatic and efficient change detection for urban buildings,this paper carries out research on method improvement and practical application.[Methods] A two-stage collaborative detection framework integrating Transformer-based change detection and HRNet-based building extraction is constructed.First,Transformer is adopted to model the global semantic relationships between bi-temporal images and locate change regions.Then,HRNet is utilized to retain high-resolution spatial details,so as to realize refined building extraction and change type identification within candidate change areas.[Findings]Taking Shenzhen city as the research area,application verification is conducted from pilot tests in typical zones to full-city promotion.The effectiveness of model-generated change clues is further analyzed from the perspective of practical business work.Experimental results demonstrate that the proposed method achieves favorable feasibility and performance in high-density urban environments.[Conclusions]The proposed method can provide technical support for refined natural resources management and smart city construction,and possesses great practical engineering application value．

Semi-supervised segmentation and rapid vectorization of risk components in transmission corridors through point cloud-image fusion

Wu Jiangyi, Qin Jiafan, Zhang Sihan, Yang Zhaonan, Yuan Xinxing

2026, 0(9):  144-150.  doi:10.13474/j.cnki.11-2246.2026.0921

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[Purposes] To address the challenges of large spans in transmission corridors,strong vegetation and terrain obstructions,and the high cost of acquiring finely annotated samples,this paper proposes a point cloud-image fusion semi-supervised segmentation and rapid vectorization method tailored for corridor risk components.[Methods] A dual-branch segmentation model integrating point cloud and imagery data was constructed.The imagery branch extracts multi-scale texture semantics,while the point cloud/elevation branch characterizes geometric and spatial relationships.Cross-modal attention achieves feature alignment and complementarity.Utilizing the spatial intersection of canopy height models and corridor buffers to automatically generate binary risk grids as weak annotations,we introduced teacher-student consistency learning and a confidence-based pseudo-labeling mechanism.Combined with Focal and Lovsz losses,this mitigates class imbalance and enhances segmentation accuracy for slender objects.During inference,connected component analysis and area threshold filtering are applied to segmentation results,overlaid with corridor boundary constraints to generate structured vector alert features.[Findings] Experiments on actual transmission line data in Guangdong Province demonstrate that this method outperforms unimodal approaches relying solely on point cloud or optical image features across three metrics： mIoU, F1 score,and overall accuracy.Specifically,it achieves mIoU of 0.185,F1 score of 0.307,and overall accuracy of 0.842,while generating topologically complete corridor risk patches.[Conclusions] The proposed point cloud-image fusion semi-supervised segmentation and rapid vectorization method enables effective extraction of high-vegetation/potential over-line objects within transmission corridors and their vectorization for operation and maintenance purposes,with minimal additional manual annotation costs.This approach demonstrates engineering feasibility for practical applications．

Multi-scale feature fusion DeepLabv3+ in remote sensing monitoring for comprehensive management of illegal rural construction on cultivated land

Zhang Tao, Liu Xue, Chen Song, Fu Jiadi

2026, 0(9):  151-157.  doi:10.13474/j.cnki.11-2246.2026.0922

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[Purposes] Monitoring illegal rural construction on cultivated land remains a critical challenge due to land-cover heterogeneity and building scale variability.This study proposes an enhanced DeepLabv3+ framework with multi-scale feature fusion for accurate building extraction and suspected illegal patch identification.[Methods] Efficient channel attention (ECA)modules are embedded into the ResNet-50 encoder to recalibrate channel responses with negligible overhead.A feature pyramid network (FPN)performs top-down semantic propagation and lateral aggregation,enabling effective fusion of high-level semantics with spatial details.The atrous rates in ASPP are reparameterized to align the receptive field with small rural buildings.[Findings] On a subset of the WHU-Mix dataset,the method achieves 80.13% IoU and 88.97% F1-score,outperforming state-of-the-art baselines.Cross-scene validation using Henan-1 satellite imagery confirms the framework's robustness in cultivated land non-agriculturalization monitoring.[Conclusions] The proposed approach provides a reliable solution for building extraction,supporting natural resource supervision and enforcement at the grassroots level．

Extraction of water hyacinth information in Taihu Lake using Sentinel-2 images and decision tree classification

Wu Yongfeng, Wang Wenhui, Shi Yifan, Wang Yihong, Long Fei, Mo Ran

2026, 0(9):  158-163.  doi:10.13474/j.cnki.11-2246.2026.0923

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[Purposes] The large-scale outbreak of water hyacinth can exert significant adverse impacts on flood control,water supply security,and aquatic ecological health of rivers and lakes.To address the problems that water hyacinth is prone to cause misjudgments in cyanobacteria monitoring of Taihu Lake and interfere with cyanobacteria salvage and treatment during cyanobacteria bloom periods,a targeted water hyacinth monitoring method is proposed.[Methods] Based on the spectral response differences of water hyacinth,cyanobacteria,and other aquatic vegetation in specific bands of Sentinel-2 images,this study constructs corresponding feature indices,and extracts water hyacinth using threshold segmentation and decision tree classification methods.[Findings] According to the experimental results,the accurate extraction of water hyacinth in Taihu Lake can be achieved under the conditions of VREI >0.05,FLVI>0.57,and WVI>0.065.[Conclusions] Field verification indicates that this method can be applied to water hyacinth extraction and can distinguish water hyacinth from cyanobacteria,nymphoides peltatum,and other aquatic vegetation in Taihu Lake.The application of this method to Ge Lake further verifies its applicability．

Scale estimation and matching extension for aerial-space image registration

Yu Pengfei, Wang Baocheng

2026, 0(9):  164-170.  doi:10.13474/j.cnki.11-2246.2026.0924

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[Purposes] To address the challenge of direct registration between optical UAV and satellite images caused by significant differences in scale,radiation,and viewing angle,this paper proposes an aerial-space image registration method integrating scale estimation and match expansion.[Methods]The method embeds scale estimation and a homonymous match expansion mechanism into the SIFT feature registration framework,and introduces an angle-constrained iterative cost function to achieve rapid coarse registration of heterogeneous aerial-space imagery.[Findings] Experimental results demonstrate that the proposed method achieves accurate registration under multiple scale differences and significant grayscale variations,with a registration accuracy better than 3 pixels and an average time per correct match of less than 1 second.Without relying on prior geographic information or dataset training.[Conclusions]This method is well-suited for tasks such as coarse registration of heterogeneous aerial-space images and fast visual localization．

Precise registration of steel structure based on layered ICP point cloud and detection method of key screw hole size

Chen Yucheng, Lian Xugang, Zhan Xinbin, Zhang Yu, Li Yibiao, Chen Chunyang, Cai Yinfei, Hu Haifeng

2026, 0(9):  171-176.  doi:10.13474/j.cnki.11-2246.2026.0925

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[Purposes] Aiming at the problems of complex construction site environment and low efficiency of manual inspection in the pre-installation process of steel structure buildings,this paper proposes a hierarchical ICP point cloud registration algorithm based on the engineering background of Taiyuan Airport Terminal.[Methods] This method stratifies the point cloud along the main direction and performs ICP registration layer by layer,and adaptively selects the optimal matching result,which solves the problem of low point cloud overlap rate and point cloud registration failure caused by environmental constraints in the data acquisition process.In order to quickly detect the error value of component bolt hole products,based on the point cloud main plane projection and two-dimensional morphological analysis of hole characteristics,an automatic bolt hole recognition method based on image processing mechanism is proposed.[Findings]The results show that the matching degree of the hierarchical ICP algorithm is more than 15% higher than that of the traditional algorithm,and the accuracy of bolt hole recognition is more than 94%.[Conclusions]This method has been verified in the pre-installation stage of steel structure,which can provide data support for the site and significantly improve the construction efficiency and assembly accuracy．

Wetland mapping of the Yellow River Delta based on time series Sentinel-1 and improved mRMR algorithm

Zhang Yanwei, Li Zhenjin

2026, 0(9):  177-184.  doi:10.13474/j.cnki.11-2246.2026.0926

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[Purposes]Coastal wetlands play a crucial role in maintaining coastal stability,and accurate monitoring of these ecosystems is of great importance.Synthetic aperture radar (SAR)imagery,with its all-weather and time-series acquisition capability,provides strong support for wetland monitoring in both temporal and spatial dimensions.However,feature redundancy limit the performance of classification models.[Methods]To address this issue,this study uses 30 Sentinel-1 SAR images acquired over the Yellow River Delta wetlands in 2020 as the data source.An improved minimum redundancy maximum relevance with cross-validation feature selection strategy is proposed to identify the optimal feature subset for machine-learning classifiers,and Yellow River Delta wetlands maps from 2020 to 2024 are generated.[Findings]The results show that the wetland classification accuracy of the proposed method under extreme gradient boosting reached 89.66%,outperforming feature selection methods such as random forest,recursive feature elimination,and principal component analysis.The Spartina alterniflora in the Yellow River Delta was extensively removed in 2023 and completely eradicated by 2024.Under the new pattern,tidal flats have been continuously transformed into native salt marsh vegetation such as Phragmites australis and Suaeda salsa.[Conclusions]The proposed method provides a new technical reference for wetland monitoring using SAR imagery．