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Theoretical foundation of gravity field and improvement of classical concepts for geodetic height datum unified in the terrestrial reference system Chuanyin ZHANG, Tao JIANG, Baogui KE Acta Geodaetica et Cartographica Sinica    2025, 54 (9): 1561-1571.   DOI: 10.11947/j.AGCS.2025.20250102 Abstract （1673）   HTML （120）    PDF（pc） （1832KB）（448）       Knowledge map    Save The current theory of geodetic height datum were mainly established during the era of traditional terrestrial geodesy, and have difficulty adapting to the rapid development of the Earth's gravity field and satellite geodesy. This paper strictly follows the principles of geometric and physical geodesy and the uniqueness and precise measurability requirements of geodetic elements and concepts, and deduces the theoretical and logical relationship among the height datum, the terrestrial reference system and the gravity field concisely and clearly by conducting scientific research on the theoretical foundations and implementation principles necessary for unifying the elements of physical geodesy into the terrestrial reference system, and then re-examines some classical concepts of the height datum. The paper presents the following main results and their specific geodetic evidences. ①It is demonstrated that whether it is the orthometric height, normal height or geopotential number system, the height starting datum surface is the geoid if the deformation of the geoid is ignored, and it is pointed out that the analytical orthometric height is more suitable for the purpose of the height datum than other types of orthometric heights. ②The theoretical foundation of the gravity field for the geodetic height datum unified in the terrestrial reference system is improved, and the geodetic datum conditions and technical implementation principles for the GNSS replacing leveling technology are derived. ③The theoretical method of Earth's center of mass and shape polar positioning based on space geometric and physical geodesy is derived. Which neither relies on geophysical assumptions or geodynamic protocols, nor on the principle of earth rotation and its dynamics, but rather realizes scientifically the positioning and orientation of the terrestrial reference system only based on the theory of geodesy. ④It is demonstrated that the surfaces of orthometric equi-height are parallel to the geoid and the normal gravity field can be fully determined with only three parameters. Thus the trouble of coordination and consistency between the geoid defined by the Gaussian convention and the gravity geoid has been effectively solved. Table and Figures | Reference | Related Articles | Metrics

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Satellite gravity technology oriented towards data-scenario-model driven approach: developments, challenges and outlook Jiancheng LI, Yunlong WU, Yibing YAO, Zhicai LUO Acta Geodaetica et Cartographica Sinica    2025, 54 (9): 1537-1560.   DOI: 10.11947/j.AGCS.2025.20250274 Abstract （1612）   HTML （148）    PDF（pc） （6221KB）（1063）       Knowledge map    Save Satellite gravimetry, as a major breakthrough in modern geodesy, has demonstrated strong capabilities in capturing mass variations in the Earth's surface and subsurface layers. It has been widely applied in critical fields such as geodetic surveying, hydrological cycle monitoring, glacier mass balance, sea level change, and tectonic deformation. This study systematically reviews the evolution of gravity satellite missions from CHAMP and GRACE to GRACE-FO and Chinese gravity satellite programs, with a particular focus on next-generation satellite gravimetry missions and emerging trends in quantum-based gravity satellite concepts. Based on this, the study comprehensively summarizes the data processing pipeline from Level-0 to Level-3, key inversion methodologies, and science product development. Application cases are presented across hydrology, cryosphere, oceanography, seismology, and geoid refinement. Furthermore, major challenges in China's current gravimetry application system are identified, including data quality limitations, multi-source signal separation, lack of interpretability in AI-based models, and barriers to interdisciplinary integration. Finally, the study calls for synergistic innovation driven by “data-scenario-model” integration to support multi-satellite networks and high-precision modeling in service of national strategic needs and global sustainable development. Table and Figures | Reference | Related Articles | Metrics

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Monitoring method of Earth's center of mass, figure pole and various rotational dynamics parameters Chuanyin ZHANG, Wei WANG, Tao JIANG Acta Geodaetica et Cartographica Sinica    2025, 54 (7): 1157-1169.   DOI: 10.11947/j.AGCS.2025.20250141 Abstract （1590）   HTML （175）    PDF（pc） （3104KB）（371）       Knowledge map    Save The Earth's center of mass and figure pole are geodetic datum for describing and measuring the rotation of the Earth. The inability to accurately measure mass redistribution within the Earth's interior and geomaterial motion results in more uncertainties in geophysical excitations estimated from geophysical fluid data, thereby limiting in-depth investigations into the rotational dynamics of the Earth. In this paper, various geodetic measurements and Earth rotation motion are unified in an only Earth-fixed reference system, and the effects of Earth's figure polar shift and rotation variation on various geodetic elements are investigated. And then a time-synchronized monitoring methodology for Earth's figure pole and various rotational dynamic parameters is present by multi-geodetic collaboration, which can provide more favorable scientific and technological conditions for the in-depth study of the excitation dynamics mechanism of the Earth's rotation and the interaction of the Earth's spheres. The paper presents the following main results. ① The theoretical method for positioning of Earth's center of mass and figure pole is derived by space geometric and physical geodetic collaboration, which can not only accurately measure the variation time series of Earth's center of mass and figure pole, but also position and orient the current terrestrial reference system to the mean center of mass and mean figure pole in a specified time period. ② This paper presents the monitoring algorithms of the Earth's center of mass, figure pole and various rotational dynamics parameters by collaborating with the Earth's satellite observation, VLBI kinematics measurement, and the site's radial placement and gravity variation observations, which can improve the constraints of the study on the mechanism of the Earth's rotational dynamics. Table and Figures | Reference | Related Articles | Metrics

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Spatio-temporal fusion algorithm based on adaptive reference feature incorporation and multi-scale feature aggregation Shuai FANG, Jiaen LIU, Jing ZHANG Acta Geodaetica et Cartographica Sinica    2025, 54 (8): 1476-1488.   DOI: 10.11947/j.AGCS.2025.20240457 Abstract （1510）   HTML （19）    PDF（pc） （12525KB）（113）       Knowledge map    Save The purpose of spatio-temporal fusion algorithm is to generate dense time series images with high spatial resolution, which is very important for monitoring fine dynamic-changes of the surface. Most of the spatio-temporal fusion algorithms help to predict the target fine image by reference fine image in the adjacent time, which makes the purpose of spatio-temporal fusion algorithm is to generate dense time series images with high spatial resolution, which is very important for monitoring fine dynamic-changes of the surface. However, the existing spatio-temporal fusion algorithms are easily misled by the reference image in the area with land cover change, and the reconstruction of heterogeneous areas composed of small targets is more difficult. To this end, this paper proposes a spatio-temporal fusion algorithm based on adaptive reference feature incorporation and multi-scale feature aggregation. In the encoding stage, an adaptive reference feature incorporation module is designed. According to the change information provided by the time series coarse image pair and the gating structure, the adaptive introduction of the reference fine image is realized, which not only improves the prediction accuracy by using the reference information, but also suppresses the misleading of the reference information to the change area. In the decoder stage, a multi-scale feature aggregation strategy is designed to aggregate information of different scales for each layer of the decoder, and the channel attention mechanism is combined to filter information with important features to improve the reconstruction accuracy of heterogeneous areas. Finally, the focal frequency loss term is introduced into the loss function. From the perspective of frequency distribution, it enhances the authenticity of the generated image and focuses on the reconstruction of difficult frequency bands to make up for the deficiency of spatial spectrum loss. The experimental results on LGC, CIA and Wuhan datasets show that the proposed algorithm has better fusion results than the other six algorithms. Table and Figures | Reference | Related Articles | Metrics

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The correction method of relativistic effects for GNSS and LEO satellites Tao GENG, Qiang LI, Lingyue CHENG, Jingnan LIU Acta Geodaetica et Cartographica Sinica    2025, 54 (12): 2129-2141.   DOI: 10.11947/j.AGCS.2025.20250226 Abstract （1459）   HTML （22）    PDF（pc） （4044KB）（217）       Knowledge map    Save Relativistic effects in satellite navigation stem from the differential motion states between satellites and terrestrial users, manifesting as gravitational frequency shifts and time dilation. These effects are more pronounced for low earth orbit (LEO) satellites due to stronger perturbations from Earth's non-spherical gravity, raising questions about the applicability of existing correction methods. This study reviews the rigorous relativistic correction formula and two approximations used by IGS Analysis Centers and global navigation satellite systems: the traditional formula, which assumes a small orbital eccentricity and only considers Earth's central gravity, and a modified formula that additionally accounts for gravitational perturbations from Earth's higher-order terms. We first evaluate these formulas for GPS, GLONASS, Galileo, and BDS-3 satellites. Subsequently, we analyze the relationship between their correction accuracy and the orbital inclination, semi-major axis, and eccentricity of LEO satellites using simulated and measured data. Results indicate that for MEO satellites (excluding E14/E18), the modified formula reduces the periodic error amplitude from 0.11 ns to 0.05 ns. For BDS-3 IGSO satellites, however, the traditional formula yields a better accuracy of 0.05 ns compared to 0.06 ns from the modified one. For LEO satellites, the accuracy of both formulas decreases significantly and is strongly influenced by orbital parameters. Specifically, correction accuracy decreases with greater orbital inclination, lower orbital altitude, and larger eccentricity, with periodic errors for near-polar LEO satellites reaching up to 1 ns. Table and Figures | Reference | Related Articles | Metrics

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Deep learning methods for remote sensing intelligent change detection: evolution and development Jixian ZHANG, Haiyan GU, Huan NI, Haitao LI, Yi YANG, Shaopeng DING, Songman SUI Acta Geodaetica et Cartographica Sinica    2025, 54 (8): 1347-1370.   DOI: 10.11947/j.AGCS.2025.20240417 Abstract （1442）   HTML （87）    PDF（pc） （9549KB）（795）       Knowledge map    Save The rapid development of multimodal remote sensing and deep learning technologies has expanded the data and method dimensions of remote sensing change detection, laying the foundation for more automated, refined, and intelligent change detection. This article focuses on change detection based on deep learning, addressing two fundamental scientific issues: change feature expression and network learning strategies, and detailing the evolution of change detection research. In terms of change feature expression, there are four research trends: from local to global and spatiotemporal integration, from single modality to multimodality, from lightweight models to large models, and from binary to multi-category semantic feature expression. In terms of network learning, there is a development trend from fully supervised to weak/semi-supervised to unsupervised change detection. Based on this, the article discusses the current challenges faced by deep learning-based change detection and, in conjunction with the development trends of artificial intelligence technology, points out three development directions: text-image fusion, generative, and human-computer collaborative modes. This aims to provide direction and ideas for theoretical methods and application research, and to enhance the intelligence and application level of remote sensing change detection. Table and Figures | Reference | Related Articles | Metrics

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GNSS-assisted InSAR tropospheric delay correction model incorporating vertical stratification and turbulent components Hailu CHEN, Yunzhong SHEN Acta Geodaetica et Cartographica Sinica    2025, 54 (10): 1786-1797.   DOI: 10.11947/j.AGCS.2025.20250124 Abstract （1383）   HTML （44）    PDF（pc） （7033KB）（186）       Knowledge map    Save GNSS reference station observed tropospheric delays are commonly employed to correct tropospheric delays in InSAR, which involves spatially interpolating the GNSS-observed delays to unmeasured locations. Traditional methods focus solely on the spatial correlation characteristics of turbulent components, achieve interferogram correction through functional or stochastic modeling while neglecting the stratified component. This study proposes a joint correction model that accounts for both stratified and turbulent delay components. Specifically, an elevation-dependent functional model and a stochastic model are adopted to absorb stratified and turbulent delay components, respectively. The deterministic parameters of stratified component and random turbulence at the GNSS-measured points are simultaneously resolved via least squares collocation. Finally, predict them to unmeasured points. Validation using 71 Sentinel-1 datasets over Southern California demonstrates that the proposed method reduces the average standard deviation (STD) of 70 short temporal baseline interferograms from 4.7 rad to 1.4 rad, outperforming both GACOS (average STD reduced to 2.7 rad), linear model (average STD reduced to 4.1 rad), GInSAR (average STD reduced to 2.9 rad) and LSC-GInSAR (average STD reduced to 1.8 rad) corrections. The derived deformation velocity reveals regional long-wavelength deformation pattern that agrees well with GNSS measurements (correlation coefficient is 0.67). These results confirm that the proposed approach can effectively correct medium-to-long-wavelength tropospheric delays in interferogram and is suitable for measuring large-scale deformation signals. Table and Figures | Reference | Related Articles | Metrics

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A high-precision deformation monitoring method with GNSS multi-baseline solutions Bofeng LI, Long CHEN, Leitong YUAN Acta Geodaetica et Cartographica Sinica    2025, 54 (12): 2116-2128.   DOI: 10.11947/j.AGCS.2025.20250245 Abstract （1357）   HTML （45）    PDF（pc） （4322KB）（292）       Knowledge map    Save The GNSS deformation monitoring field faces dual challenges from increasingly complex monitoring scenarios and the shift toward low-cost equipment, where performance degradation occurs under signal obstruction, interference, or long baselines. To overcome these limitations, this study leverages spatiotemporal correlations in regional station networks to develop a high-precision method with GNSS multi-baseline solutions. We derive the analytical solution for this model, theoretically analyze the improvement in float solution precision with increasing numbers of monitoring stations, and validate its efficacy through simulated obstructed-environment tests and real-world application on Hangzhou Bay bridge. Results confirm that the approach significantly improves performance in complex conditions, delivering faster convergence, extended monitoring range, and streamlined parameters—highlighting its scalability and practical potential for engineering applications. Table and Figures | Reference | Related Articles | Metrics

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On the development of surveying and mapping in the era of artificial intelligence Deren LI, Mi WANG, Wenbin SHEN, Qingyun DU, Shuo WANG Acta Geodaetica et Cartographica Sinica    2025, 54 (12): 2107-2115.   DOI: 10.11947/j.AGCS.2025.20250438 Abstract （1341）   HTML （154）    PDF（pc） （4381KB）（814）       Knowledge map    Save Against the backdrop of rapid advances in artificial intelligence and the surging demand for spatio-temporal information applications, spatio-temporal intelligence, a new discipline integrating surveying, navigation, remote sensing, and artificial intelligence (AI) has emerged. Leveraging intelligent sensors for communication, navigation, and remote sensing, cloud computing, and AI technologies, it enables intelligent perception, cognition, and decision support for natural and human activities, with a focus on promoting sustainable development. Surveying and mapping plays a core supporting role, requiring the construction of a four-dimensional spatio-temporal reference frame to ensure the accuracy of spatio-temporal information, the development of “fast, accurate, and agile” intelligent processing technologies to address massive data challenges, and the enhancement of spatio-temporal information comprehension efficiency through multi-dimensional dynamic visualization. The “National One Map” project serves as a typical paradigm for the practical implementation of spatio-temporal intelligence theory. Relying on the Oriental Smart Eye constellation and Tianditu (national geospatial information public service platform), it promotes the unified application of spatio-temporal information in government affairs, public services, and commercial sectors, achieving dynamic updates and intelligent analysis. In the future, spatio-temporal intelligence will deepen its theoretical system, improve the theories of spatio-temporal reference frame construction and data processing, integrate cutting-edge AI technologies, facilitate global spatio-temporal information sharing, provide more precise decision support for urban governance, environmental protection, and other fields, and contribute to the construction of a community with a shared future for mankind. Table and Figures | Reference | Related Articles | Metrics

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A U-shaped graph convolution network method for semantic segmentation of vehicle LiDAR point clouds towards urban road scenes Jie WAN, Zhong XIE, Yongyang XU, Liufeng TAO Acta Geodaetica et Cartographica Sinica    2025, 54 (7): 1280-1293.   DOI: 10.11947/j.AGCS.2025.20230481 Abstract （1319）   HTML （21）    PDF（pc） （11426KB）（117）       Knowledge map    Save Semantic segmentation of vehicle LiDAR point clouds aims to extract the 3D information of roads and various roadside objects, which is crucial for the objectification and 3D modeling of urban road scenes. Aiming at the challenges faced by current deep learning networks in handling vehicle LiDAR point clouds, including architectural constraints and difficulties in effectively extracting and utilizing multi-scale information, leading to inaccuracies in segmenting small objects, incomplete objects and occluded objects, this paper proposes a point cloud semantic segmentation method based on the U-shaped graph convolutional network (U-GCN). The proposed method firstly designed a dynamic graph convolutional operators that utilized learnable graph convolutional point kernels to adaptively extract local geometric features from the point cloud. Additionally, the cascaded dynamic graph convolutional operators were employed to construct a local feature aggregation module and expand the receptive field, enabling the capture of structural and contextual information on the objects. Subsequently, combined with the U-shaped encoder-decoder network architecture, deep and shallow point features are fused through skip connections to obtain multi-scale detailed information of objects, so as to enhance the feature representation of objects. Finally, a deep supervision loss function was introduced to guide the network to utilize output prediction information from different layers for the multiscale supervision training, further improving the network robustness and overall performance. Experiments on the Toronto-3D and WHU-MLS datasets show that the proposed method outperformed current mainstream networks in both visual analysis and quantitative evaluation. It can effectively improve the low segmentation accuracy caused by object scale variations, occlusion, and data incompleteness. Table and Figures | Reference | Related Articles | Metrics

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Loop closure detection method for LiDAR SLAM supported by stable static point cloud clusters Jiaxin GAO, Xin SUI, Changqiang WANG, Aigong XU, Zhengxu SHI Acta Geodaetica et Cartographica Sinica    2025, 54 (12): 2194-2205.   DOI: 10.11947/j.AGCS.2025.20250252 Abstract （1267）   HTML （7）    PDF（pc） （3125KB）（133）       Knowledge map    Save In dynamic, degenerate, and large-scale cluttered environments, loop closure detection methods based solely on point cloud processing exhibit poor robustness. Moreover, existing methods generally suffer from weak translation sensitivity and low computational efficiency. To address these challenges, this paper proposes a bag-of-words with stable static point cloud clusters-based loop closure detection method. Firstly, the degradation of the preprocessed point cloud is evaluated from the environmental structure perspective, and a robust point cloud cluster classification scheme is designed to obtain the stable static point cloud clusters to weaken the interference of dynamic targets. Subsequently, to reduce the redundancy in loop closure information, the fuzzy comprehensive evaluation algorithm is used to adaptively filter the key frames. Finally, based on the stable static point cloud cluster and keyframe selection results, a bag-of-words with point cloud cluster local descriptors-based loop closure detection algorithm is proposed. The relative spatial relationship and attribute relationship between stable point cloud clusters are used to improve the translation and rotation sensitivity of bag information, so as to ensure the actual performance of loop closure detection in degenerate and cluttered scenes. Experimental results demonstrate that the proposed method can robustly detect the correct loop closure relationship in the measured scene, and the non-loop closure frame error detection rate is only 5.56%, with a single-keyframe processing time of 0.052 8 s. Compared with three similar methods BoW3D, ISC, and SGLC, the average improvement in the loop closure frame correct detection rate reaches 75.73%, the average reduction in the non-loop closure frame error detection rate is 81.93%, the processing has strong real-time performance, and it exhibits stronger robustness and applicability. Table and Figures | Reference | Related Articles | Metrics

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GPS/Galileo/BDS overlapping frequencies multipath error analysis and modeling Yangyi CHEN, Kai ZHENG, Xiaohong ZHANG, Mingkui WU, Pengxu WANG, Wenju FU, Kezhong LIU Acta Geodaetica et Cartographica Sinica    2025, 54 (8): 1427-1438.   DOI: 10.11947/j.AGCS.2025.20240316 Abstract （1257）   HTML （17）    PDF（pc） （5084KB）（140）       Knowledge map    Save Multipath error is one of the primary unmodeled errors affecting GNSS precise positioning. Currently, the multipath hemispherical map (MHM) model generated by single system is limited by satellite orbit period and data volume, leading to restricted data coverage and low modeling efficiency. Therefore, this paper constructs an integrated multi-system MHM model by combining overlapping frequency data from GPS, Galileo, and BDS. The effectiveness of the short-term fusion model for multipath correction is evaluated with short-baseline data. The results show that the multipath error of the overlapping frequency for the three systems exhibits consistent spatial distribution characteristics, and the data coverage (the proportion of grids with more than 30 residuals within the total grids) of the three-system fusion model constructed with 4-day data is higher than that of the model built with 10-day data of a single system. The multipath models with overlapping frequency between systems has good interoperability. After using an overlapping grid evaluation method, the inter-system multipath correction rate is approximately 30%~45%. The correction rates of the three-system fusion model for GPS, Galileo, and BDS are approximately 60%、46%, and 48%, respectively. Additionally, the multi-system fusion model, constructed in a short time, can enhance positioning accuracy by approximately 10%~20% compared to single-system models, which is comparable to those of single-system multipath models built with long-term data. However, the difference in the multipath correction effect between the GPS single-system model and the multi-system fusion model is not significant. Table and Figures | Reference | Related Articles | Metrics

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Wide area coastal subsidence monitoring and driver analysis with multi tracks of TS-InSAR—a case study of Shandong province Peng LI, Jianbo BAI, Zhenhong LI, Houjie WANG Acta Geodaetica et Cartographica Sinica    2025, 54 (7): 1178-1191.   DOI: 10.11947/j.AGCS.2025.20250061 Abstract （1255）   HTML （43）    PDF（pc） （13186KB）（269）       Knowledge map    Save Coastal subsidence will exacerbate relative sea level rise and increase the risk of flood-related coastal infrastructure inundation and soil salinization. As a major economic province in the east coast of China, the coastline of Shandong accounts for about 1/6 of the country. However, the spatiotemporal evolution characteristics and key drivers of land subsidence in Shandong are still unclear. In this paper, we conducted multi-track radar interferometry (InSAR) time series analysis with the Sentinel-1 imagery from 2019 to 2022. Firstly, we proposed a multi-track InSAR uncontrolled splice method applicable to the interface region between land and sea to correct the systematic bias of interferograms from adjacent tracks. Then, we generated a large-scale land subsidence rate map of the whole province with good consistency. Furthermore, we found multiple sinking funnels over 50 mm/a. Based on Sentinel-2 multispectral remote sensing images, deformation time series and principal component analysis, we revealed the spatiotemporal change of the heterogeneous sedimentation funnel and its drivers. The results show that human activities related to groundwater pumping and coal mining are the main factors causing land subsidence in Shandong province. This study is expected to provide technical support and scientific basis for large-scale coastal subsidence monitoring and risk management, and further improve the understanding of coastal geological disaster risk. Table and Figures | Reference | Related Articles | Metrics

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Research on key technologies of remote sensing based natural resources monitoring and supervision platform supported by dynamic service computing Hao WU, Dongyang HOU, Jun ZHANG, Ping ZHANG, Yuxuan LIU, Lei DU, Lu KANG, Tao CHENG, Jun CHEN Acta Geodaetica et Cartographica Sinica    2025, 54 (11): 1992-2008.   DOI: 10.11947/j.AGCS.2025.20250138 Abstract （1238）   HTML （18）    PDF（pc） （6914KB）（193）       Knowledge map    Save Remote sensing monitoring and supervision is the core technology supporting the protection, development, and utilization of natural resources. Currently, the digitalisation of processes and the necessity for high-quality protection have led to an increased demand for remote sensing monitoring and supervision to be carried out in a timely, accurate, knowledgeable, and dynamic manner. It is imperative to enhance the automation and intelligence levels of the operational support system and construct an integrated monitoring and supervision platform capable of addressing diverse elements, scenarios, and levels. This paper, based on an analysis of the current research status, identifies the main technical bottlenecks that need to be overcome for engineering applications of natural resources remote sensing monitoring and supervision. Guided by the principles of decentralisation and service autonomy, the paper proposes a framework for integrated natural resource monitoring and supervision supported by dynamic service computing. The paper outlines the design of a dynamic service computing architecture and key tasks, recent research advancements and application outcomes in natural resource remote sensing monitoring and supervision scenarios, following the thread of “ready-to-use data-precision monitoring-knowledge-driven supervision-dynamic platform”. Finally, the paper discusses the broader implications for the dissemination and application of related research efforts. Table and Figures | Reference | Related Articles | Metrics

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Intelligent methods for 3D terrain reconstruction of the Moon and near-Earth planets: a review of current advances and future perspectives Xiaohua TONG, Rong HUANG, Jiarui CAO, Chen LIU, Rong WANG, Yusheng XU, Zhen YE, Yanmin JIN, Shijie LIU, Sicong LIU, Yongjiu FENG, Huan XIE Acta Geodaetica et Cartographica Sinica    2025, 54 (11): 1917-1933.   DOI: 10.11947/j.AGCS.2025.20250337 Abstract （1213）   HTML （85）    PDF（pc） （3680KB）（621）       Knowledge map    Save 3D terrain reconstruction of extraterrestrial bodies is a core element of deep space exploration, providing essential spatial information for landing site selection, rover navigation, and resource exploration. Traditional techniques—such as photogrammetry, photoclinometry, and laser altimetry interpolation—have been extensively applied to the Moon, Mars, and asteroids, achieving significant progress in building high-precision terrain models, interpreting geomorphological features, and supporting resource prospecting. However, these methods remain constrained by limited imaging conditions, the absence of reliable control references, and the complexity of terrain and illumination, often resulting in issues such as low data quality, difficult feature matching, missing observations, and limited automation. In recent years, artificial intelligence (AI) techniques—including convolutional neural networks (CNNs), generative adversarial networks (GANs), attention-based models (Transformers), and neural radiance fields (NeRF)—have shown growing potential in extraterrestrial 3D reconstruction. This review synthesizes three major AI-driven approaches: ①Feature extraction and image matching. ②Depth estimation from single-view images. ③Radiance field modeling from multi-view observations. We further compare their underlying mechanisms, representative applications, applicable scenarios, and performance characteristics. Finally, we outline key technical challenges and discuss future directions in multi-source data fusion, self- and weakly supervised learning, foundation models, and real-time processing, aiming to foster broader applications of AI in extraterrestrial 3D terrain reconstruction. Table and Figures | Reference | Related Articles | Metrics

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A minimal-interaction framework for accurate and batch extraction of geospatial objects from remote sensing imagery Zhili ZHANG, Huiwei JIANG, Xiangyun HU Acta Geodaetica et Cartographica Sinica    2025, 54 (10): 1863-1876.   DOI: 10.11947/j.AGCS.2025.20250161 Abstract （1195）   HTML （14）    PDF（pc） （15205KB）（174）       Knowledge map    Save High-resolution remote sensing image object extraction is a critical technology supporting key areas such as smart city development and natural resource monitoring. However, existing fully automatic methods still face dual challenges in practical applications—limited model adaptability and high manual annotation costs. To address these issues, this paper proposes a high-precision object extraction framework for remote sensing imagery based on minimal interactions (e.g., points, strokes, boxes). By systematically analyzing the limitations of current interactive segmentation techniques, we innovatively construct a unified extraction framework that integrates precise interactive segmentation and batch identical-object detection. The framework comprises two core algorithms: ①A one-shot precision extraction algorithm based on fine-tuning strategies, enabling high-quality object segmentation under minimal interaction; ②A rapid detection algorithm for identical objects, which leverages existing segmentation masks to achieve efficient batch annotation of identical objects. In addition, the extraction framework includes empirical post-processing of geospatial extraction results to obtain vector extraction results. Experimental results on typical facet objects such as buildings, water bodies, and forested areas demonstrate that the proposed method significantly reduces user interactions while maintaining high segmentation accuracy. It outperforms state-of-the-art general-purpose segmentation models, such as segment anything model (SAM) and EISeg. This study provides an innovative solution for efficient annotation of remote sensing image samples and offers significant potential for advancing the automation and practical utility of intelligent remote sensing interpretation. Table and Figures | Reference | Related Articles | Metrics

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BDS-3/GNSS PPP-RTK augmented products estimation and credible positioning methods Bo LI Acta Geodaetica et Cartographica Sinica    2025, 54 (11): 2097-2097.   DOI: 10.11947/j.AGCS.2025.20240137 Abstract （1154）   HTML （14）    PDF（pc） （874KB）（111）       Knowledge map    Save Reference | Related Articles | Metrics

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DRformer: a progressive coupled multiscale CNN and condensed attention Transformer method for hyperspectral image super-resolution Qing CHENG, Boxuan WANG, Hongyan ZHANG Acta Geodaetica et Cartographica Sinica    2025, 54 (7): 1230-1242.   DOI: 10.11947/j.AGCS.2025.20240485 Abstract （1145）   HTML （21）    PDF（pc） （8109KB）（200）       Knowledge map    Save The super-resolution technology of hyperspectral image aims to enhance the spatial detail and quality of low-resolution hyperspectral images for better applications in areas such as environmental monitoring. In recent years, machine learning techniques based on deep convolutional neural networks have made significant progress in single hyperspectral image super-resolution. However, challenges remain in balancing the learning of spatial multi-scale local features and global detail features. This paper presents a fusion network, DRformer, that integrates convolutional neural networks and Transformer architecture using a progressive sampling strategy. The network employs a multi-scale adaptive weighted spectral attention module for local feature extraction and selective emphasis of spectral information, followed by an initial upsampling. Subsequently, a CADR module based on the Transformer architecture is incorporated after a second upsampling to process global image features and enhance effective information. To verify the effectiveness and robustness of the network, experiments were conducted on the Chikusei and Houston2013 datasets. The results demonstrate that DRformer outperforms existing deep learning methods, including GDRRN, SSPSR, EUNet and MSDformer in terms of super-resolution performance. Additionally, ablation experiments were carried out to validate the effectiveness of each module in the network. Table and Figures | Reference | Related Articles | Metrics

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A composite drought index derived from a combination of GNSS PWV/vertical deformation and GRACE/GRACE-FO data Chaolong YAO, Hongrui YOU, Xuanhui HE, Junya LU, Yiqian XIE, Qiong LI, Shuang ZHU, Zhicai LUO Acta Geodaetica et Cartographica Sinica    2025, 54 (10): 1757-1768.   DOI: 10.11947/j.AGCS.2025.20250129 Abstract （1140）   HTML （29）    PDF（pc） （4265KB）（160）       Knowledge map    Save Developing a composite drought index (CDI) by combining multiple drought related variables is crucial for comprehensively and accurately assessing drought conditions. In this study, based on the global navigation satellite system (GNSS) precipitable water vapor (PWV)/vertical deformation and Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) satellite gravimetric data spanning from 2011 to 2022, we developed a novel hydro-meteorological CDI in southwestern China through a data fusion model combining robust estimation and joint distribution function (Copula function). The data fusion model was built to reduce the impacts of the possible outliers and considering the complex response relationship between meteorological and hydrological droughts. The results showed that ① The meteorological drought index constructed from GNSS PWV and precipitation data had good consistency with precipitation anomalies and the standardized precipitation evapotranspiration index (SPEI), with correlation coefficients of 0.88 and 0.73, respectively; ② The methods of Helmert robust variance estimation based on the IGGⅢ and robust principle component analysis (RPCA) can effectively overcome the impact of outliers and improve the accuracy of data fusion, but the overall precision of Helmert robust variance estimation was better than that of RPCA; ③ The Copula-based CDI constructed in our study contains information on atmospheric water vapor, precipitation, and terrestrial water storage, which can effectively reflect the evolution process of meteorological and hydrological droughts simultaneously. The research results provide a new way for expanding and deepening the interdisciplinary research and applications of GNSS meteorology and hydro-geodesy in comprehensive drought monitoring. Table and Figures | Reference | Related Articles | Metrics

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A novel architecture of global navigation satellite system for accurate and trusted PNT services Shuren GUO, Hongliang CAI, Weiguang GAO, Wei ZHOU, Changjiang GENG, Gang LI, Ming DONG, Chengeng SU, Kun JIANG, Yinan MENG, Lei CHEN, Junyang PAN, Kai LI, Qifen LI, Xiaomei TANG, Shuangna ZHANG, Xiaogong HU Acta Geodaetica et Cartographica Sinica    2025, 54 (11): 1934-1953.   DOI: 10.11947/j.AGCS.2025.20250175 Abstract （1126）   HTML （53）    PDF（pc） （6356KB）（347）       Knowledge map    Save GNSS is the important provider for the positioning, navigation and timing (PNT) services in today's society. After decades of development and iteration, the performance of GNSS services in current architecture has approached theoretical and engineering practical limitation, and it cannot fully fill the growing demand of global decimeter-level as well as high-trust navigation service. This study proposes a novel GNSS architecture that builds a measurement and communication network based on inter-satellite links and integrates a hybrid constellation of high orbit (GEO/IGSO), medium earth orbit (MEO), and low earth orbit (LEO). With minimal ground support, this architecture achieves global decimeter-level real-time positioning and integrity services through means such as space-based spatiotemporal reference, LEO augmented signals, and communication assistance. Simulation results show that, on the premise of being compatible with existing GNSS user terminals, the system can achieve a positioning accuracy better than 5 cm, shorten the convergence time to 1 minute, and significantly improve anti-jamming and anti-spoofing capabilities. Meanwhile, this system architecture can be compatible and interoperable with the existing GNSS and can evolve from it, enabling a smooth upgrade of user experience. Table and Figures | Reference | Related Articles | Metrics