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Large language model-driven GIS analysis: methods, applications, and prospects Huayi WU, Zhangxiao SHEN, Shuyang HOU, Jianyuan LIANG, Anqi ZHAO, Haoyue JIAO, Zhipeng GUI, Xuefeng GUAN Acta Geodaetica et Cartographica Sinica    2025, 54 (4): 621-635.   DOI: 10.11947/j.AGCS.2025.20240468 Abstract （1405）   HTML （471）    PDF（pc） （3633KB）（1620）       Knowledge map    Save The rapid development of large language models (LLMs) provide a new approach for GIS analysis, leading to the large language model-driven GIS analysis technical architecture (LLM4GIS). Based on the latest research up to October 2024, this paper reviews the evolution of GIS analysis and summarizes the LLM4GIS technical architecture from 3 aspects: application modes, datasets and evaluation methods. It also summarizes the research progress of LLM in GIS analysis tasks such as knowledge question-answering, knowledge extraction, spatiotemporal reasoning, and analyzing and modeling. Finally, the paper prospects the future research directions of GIS4LLM in 5 aspects: collaborative understanding of multimodal spatio-temporal data, balancing generalization with depth, enhancing interpretability and credibility, transitioning to embodied intelligence and edge intelligence, and the development of intelligent and universal GIS analysis. This paper provides inspiration for achieving mutual empowerment between LLM4GIS and GIS4LLM. Table and Figures | Reference | Related Articles | Metrics

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Multi-modal remote sensing large foundation models: current research status and future prospect Yongjun ZHANG, Yansheng LI, Bo DANG, Kang WU, Xin GUO, Jian WANG, Jingdong CHEN, Ming YANG Acta Geodaetica et Cartographica Sinica    2024, 53 (10): 1942-1954.   DOI: 10.11947/j.AGCS.2024.20240019. Abstract （1883）   HTML （161）    PDF（pc） （10357KB）（1541）       Knowledge map    Save The increasing remote sensing capabilities for Earth observation have eased the access to abundant data and enabled the emergence and development of remote sensing foundation models (RSFMs). Designing distinct deep neural networks and optimizing for different data and task types require substantial development efforts and prohibitively high computational resources. In order to address these issues, researchers in the remote sensing field have shifted their focus to the study of RSFMs and presented many dedicated designed unified models. To enhance the generalizability and interpretability of RSFMs, the integration of extensive geographic knowledge has been recognized as a pivotal/key approach. While existing works have explored or incorporated geographic knowledge into the architecture design or pre-training methods of RSFMs, there lacks of a comprehensive survey to review the current status of geographic knowledge-guided RSFMs. Therefore, this paper starts with summarizing and categorizing large-scale pre-training datasets and then provides an overview of the research progress in this field. Subsequently, we introduce intelligent interpretation algorithms for remote sensing imagery guided by geographic knowledge, along with advancements in the exploration and utilization of geographic knowledge specifically tailored for RSFMs. Finally, several future research prospects are outlined to tackle the persisting challenges in this field, aiming to shed light on future investigations into RSFMs. Table and Figures | Reference | Related Articles | Metrics

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A survey on cloud removal in optical remote sensing images: progress, challenges, and future works Xinchang ZHANG, Ji QI, Chao TAO, Siyang FU, Mingning GUO, Yongjian RUAN Acta Geodaetica et Cartographica Sinica    2025, 54 (4): 603-620.   DOI: 10.11947/j.AGCS.2025.20230137 Abstract （715）   HTML （530）    PDF（pc） （1847KB）（1213）       Knowledge map    Save Optical remote sensing images (RSIs), which are widely used in various Earth observation tasks due to its rich geoinformation, are often significantly affected by varying degrees of cloud contamination, leading to a significant reduction in data quality and usability. Although extensive research has been conducted on cloud removal from optical RSIs, there is still a lack of systematic review and analysis in this field. To address this gap, this paper first employs bibliometric analysis to investigate the publication trends of relevant literature both domestically and internationally, revealing the long-term development dynamics of cloud removal research in RSIs. Subsequently, the paper then provides a comprehensive and systematic review of research on the removal of thin and thick clouds, thoroughly analyzing the core challenges, underlying assumptions, approaches, and fundamental principles of different cloud removal methods, while evaluating their strengths and weaknesses. Finally, this paper summarizes and discusses the common key challenges and future trends in current optical remote sensing cloud removal research. This paper not only offers crucial insights for readers to fully understand the research progress in optical remote sensing cloud removal over the past three decades but also serves as a valuable reference for grasping the development patterns and trends in this field. Table and Figures | Reference | Related Articles | Metrics

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Singular value decomposition normalization prediction method for non-steady landslide displacement Wei QU, Rongtang XU, Jiuyuan LI, Xingyou TANG, Peinan CHEN Acta Geodaetica et Cartographica Sinica    2025, 54 (9): 1647-1663.   DOI: 10.11947/j.AGCS.2025.20240463 Abstract （469）   HTML （708）    PDF（pc） （4666KB）（1108）       Knowledge map    Save The reasonable establishment of high-precision landslide displacement prediction model has important reference value for landslide disaster prevention and early warning. In this study, a simple normalization method based on singular value decomposition is developed for the current data-driven landslide displacement prediction model, which has a strong dependence on the amount of data and limitations in dealing with the distributional drift characteristics of non-stationary landslide displacement monitoring data. This method can effectively solve the distribution drift problem of non-stationary landslide displacement data by segmentally normalizing the landslide displacement monitoring data and then combining the statistical characteristics of the extrapolation model for the inverse normalization process, and does not need to rely on large-scale data training, which can significantly improve the prediction ability of the prediction model for non-stationary landslide displacement. Tests with measured data of Heifangtai landslide in Gansu, a typical landslide domain in China, show that compared with the traditional z-score normalization method and no normalization, the method developed in this study can significantly improve the prediction accuracy of multi-class models, such as (multi-layer perceptron MLP), (long short-term memory LSTM), (gated recurrent unit GRU), and (temporal convolutional network TCN), and the average enhancement rate of (root mean square error RMSE) and (mean absolute error MAE) is more than 50%. The method in this study can significantly improve the stability of the model training process, effectively predict the sudden change of landslide displacement, and has a high value of practical popularization and application. Table and Figures | Reference | Related Articles | Metrics

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Research progress and trend of intelligent remote sensing large model Qin YAN, Haiyan GU, Yi YANG, Haitao LI, Hengtong SHEN, Shiqi LIU Acta Geodaetica et Cartographica Sinica    2024, 53 (10): 1967-1980.   DOI: 10.11947/j.AGCS.2024.20240053. Abstract （1301）   HTML （97）    PDF（pc） （14566KB）（969）       Knowledge map    Save AI large models, with their advantages in generalization, universality, and high accuracy, have become the cornerstone of various AI applications such as computer vision, natural language processing. Based on the analysis of the development process, value, and challenges of AI large models, this article first discusses the research progress of remote sensing large models from three perspectives: data, model, and downstream tasks. At the data level, there is a transition from single modality to multi-modality; at the model level, there is a shift from small models to large models; and at the downstream task level, there is a development from single-task to multi-task. Next, the article explores three key development directions for remote sensing large models: multi-modal remote sensing large models, interpretable remote sensing large models, and reinforcement learning from human feedback(RLHF). Furthermore, it realizes a construction approach for remote sensing large models, namely “construction of unlabeled dataset-self-supervised model learning-downstream transfer application”. Technical experiments have been conducted to validate the significant advantages of remote sensing large models. Finally, the article concludes and provides prospects, emphasizing the need to focus on application tasks and combine theoretical methods, engineering technology, and iterative applications to achieve low-cost training, efficient and fast inference, lightweight deployment, and engineering-based applications for remote sensing large models. 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 （991）   HTML （139）    PDF（pc） （6221KB）（866）       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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Analysis of heavy rainstorm in Beijing in 2023 based on GNSS observations Fei YANG, Yingying WANG, Zhicai LI, Boyao YU, Junli WU, Yunchang CAO, Shu ZHANG Acta Geodaetica et Cartographica Sinica    2025, 54 (1): 14-25.   DOI: 10.11947/j.AGCS.2025.20230548 Abstract （829）   HTML （149）    PDF（pc） （9638KB）（782）       Knowledge map    Save By the end of July 2023, Beijing and its surrounding areas were severely impacted by an extreme rainstorm, which was the result of a combination of typhoon “Doksuri” “Khanun”, and geographic factors. The precipitable water vapor (PWV) is one of the key factors influencing rainfall, to explore its relationship with rainfall in different process of the rainstorm is of great significance for further establishment of a rainstorm warning model. In this study, 34 GNSS stations, 34 meteorological stations, 1 radiosonde station and ERA5 datasets in and around Beijing were utilized, the GNSS-PWV data with high accuracy from July 25th, 2023 to August 1st, 2023 were obtained using GAMIT 10.71. An improved interpolation algorithm has been proposed to retrieve gridded PWV data with a high spatiotemporal resolution. Then, the accuracy of the GNSS-PWV was evaluated from multiple perspectives using the radiosonde and ERA5 data as references. Finally, the relationship between the PWV variation and extreme rainfall and the relationship between tropospheric delay gradient and rainfall trend are analyzed from the perspective of time and space by the combination of the rainfall data from the meteorological stations. Results showed that the correlation coefficient between GNSS-PWV and RS-PWV was up to 0.99, the root mean square error (RMSE) and bias were about 0.52 mm and -0.52 mm, respectively. In the comparison with ERA5 data, the RMSE of GNSS-PWV is less than 6 mm and the bias range is -4~1.5 mm, the gridded PWV has a RMSE of about 4 mm and a bias about 1 mm. The spatiotemporal analysis shows that the PWV increases sharply before the occurrence of this rainstorm, keeps increasing during the rainstorm, and could not dissipate immediately after the end of the rainstorm. This phenomenon is related to the co-influence of “Doksuri” and “Khanun”. In addition, the tropospheric delay gradient at each station shows a consistent northeastward direction, which is consistent with the transport trend of PWV high value from southwest to northeast in space, and is consistent with the actual precipitation route. Table and Figures | Reference | Related Articles | Metrics

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Autonomous situatedness map representation: a theoretical discussion on intelligent cartography in the era of large models Zhilin LI, Zhu XU, Li SHEN, Jingzhong LI, Tian LAN, Jicheng WANG, Tingting ZHAO, Tinghua AI, Peng TI, Wanzeng LIU, Jun CHEN Acta Geodaetica et Cartographica Sinica    2024, 53 (11): 2043-2052.   DOI: 10.11947/j. AGCS.2024.20240222. Abstract （1007）   HTML （92）    PDF（pc） （2933KB）（772）       Knowledge map    Save Making mapping system automatically conducting map design and production through intelligent techniques has always been the goal pursued by the cartographic community and the frontier research direction of the International Cartographic Association. Since the 1980s, artificial intelligence has been applied in cartography, gradually solving the automation problems of some processes and improving the production efficiency of map making. However, the level of automation in key steps such as map design is still extremely low, which cannot meet the “customized” and “ubiquitous” mapping demand in the information age. Fortunately, since 2023, artificial intelligence technology represented by large language models such as GPT-4 and Gemini has made breakthroughs and achieved “quasi-general artificial intelligence”, which shows strong language comprehension, reasoning and expression ability. This paper explores the use of large models to improve the intelligence level of map making systems, aiming to establish a new generation of intelligent mapping theory and method system. This paper first analyzes the bottleneck problems of the existing digital mapping system and points out the necessity of establishing a new generation of intelligent mapping technology; then it analyzes the nature and capabilities of large models and demonstrates the sufficiency of establishing such a new generation; then it further analyzes the possibility and methods of combining them, proposes an intelligent mapping framework in the era of large models (e.g. situatedness map representation); finally, it discusses the key technical issues of situatedness map representation: “autonomous consciousness of mapping context”, “autonomous design and production of maps” and “autonomous human-computer interaction in situatedness ”. Table and Figures | Reference | Related Articles | Metrics

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The transformation of the scientific concept of GIS: from Map-based GIS to Space-oriented GIS Renzhong GUO, Yebin CHEN, Zhigang ZHAO, Ding MA, Biao HE, Weixi WANG, Wuyang HONG, Minmin LI Acta Geodaetica et Cartographica Sinica    2024, 53 (10): 1853-1862.   DOI: 10.11947/j.AGCS.2024.20240152. Abstract （665）   HTML （96）    PDF（pc） （12116KB）（700）       Knowledge map    Save From the 1960s to the present, GIS has undergone a development journey of over 60 years, during which its connotations have evolved from geographic information system (GISystem) to geographic information science (GIScience), and further to geographic information service (GIService). During this period, GIS research has primarily been based on the two-dimensional abstract expression logic of cartography (Map-based GIS), achieving abstract analysis and representation of the real world. However, with the continuous emergence of new technologies and new demands such as 3D real scene, digital twins, and city information modeling (CIM), the original two-dimensional logic of GIS is facing challenges in the collection, processing, and fusion of multi-source heterogeneous spatiotemporal big data, the representation of complex spatiotemporal dynamic processes, and the mining of potential spatiotemporal patterns. How to adjust the scientific positioning of GIS to adapt to the multi-type, multi-level, and multi-role needs of spatial object expression and analysis in the digital society has become an important issue that GIS development urgently needs to consider. From a methodological perspective, we deeply analyzes the bottlenecks of Map-based GIS in spatial representation, spatial analysis, and comprehensive application. Furthermore, based on the development needs of GIS in the new era, we propose a scientific concept transformation model from Map-based GIS to Space-oriented GIS, integrating the logical thinking of Map-based GIS from the perspectives of theoretical foundations, management models, visualization forms, and functional positioning, and innovative applications under the transformation of GIS scientific concepts. This research aims to provide reference ideas for the development of GIS in the new era. Table and Figures | Reference | Related Articles | Metrics

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Digital twin and spatio-temporal intelligence of geospatial information system Yuanxi YANG Acta Geodaetica et Cartographica Sinica    2025, 54 (2): 213-220.   DOI: 10.11947/j.AGCS.2025.20240515 Abstract （638）   HTML （65）    PDF（pc） （1079KB）（692）       Knowledge map    Save The digital twin system of geospatial information is an important support system for geospatial information service and an important foundation for the development of intelligent society. The digital twin system of geospatial information has more special requirements in terms of accuracy, systemization and reliability, compared to other industrial digital twin systems. This paper describes the basic rules of the establishment of the geospatial digital twin system from perception, description and mapping to statistics, prediction and deduction. It is emphasized that the perception of geographic entities should be accurate, the space-time reference should be consistent, the attribute description should be correct, the historical information should be dependable, the mapping relationship should be complete, the statistic trend should be systematic, the variation prediction should be rigorous, and the auxiliary decision making should be scientific. The related research topics of the geospatial digital twins are generally classified. The problems to be paid attention are listed. Finally, the relationship between the geospatial digital twin and spatio-temporal intelligence is discussed. The basic process and key technologies in the construction of geospatial digital intelligence system are pointed out. Table and Figures | Reference | Related Articles | Metrics

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Positioning performance analysis and evaluation for standalone BDS receivers Chuang SHI, Chenlong DENG, Lei FAN, Fu ZHENG, Tao ZHANG, Yuan TIAN, Guifei JING, Jie MA Acta Geodaetica et Cartographica Sinica    2025, 54 (1): 1-13.   DOI: 10.11947/j.AGCS.2025.20240127 Abstract （1184）   HTML （66）    PDF（pc） （5525KB）（645）       Knowledge map    Save China's BeiDou navigation satellite system (BDS) has completed its global constellation establishment and began to provide positioning, navigation, and timing (PNT) services to global users. Based on the early principle of multi-system compatibility and interoperability, in the current market all the mainstream GNSS receivers support multi-system satellite signal reception. In order to improve the autonomy and security of the BDS PNT services, the government departments have issued opinions on accelerating the research, development and promotion, utilization of homemade standalone BDS positioning terminals. Since the standalone BDS receiver can no longer rely on the guidance of other system's signals during signal acquisition, its hardware and positioning performance may be changed, thus it is urgent to evaluate the navigation and positioning performance of the homemade standalone BDS receiver. In this paper, the M300 Pro standalone BDS receiver is selected to carry out a series of test and evaluation experiments, and the hardware performance of the receiver such as time to first fix (TTFF), signal quality and observation noise are evaluated first. Then the positioning performance such as station coordinate estimation, single point positioning (SPP), precise point positioning (PPP), static baseline solution and real-time kinematic (RTK) positioning are analyzed and discussed by using the self-developed BDS precise data processing software platform named space Geodetic spatio-temporal data analysis and research software (GSTAR). The experimental results show that the cold TTFF of the selected standalone BDS receiver is lower than 40 s, the average ratio of the intact observation data is more than 95%, and standard deviations of pseudorange and carrier phase measurement noise are 0.051 7 m and 0.003 4 cycles, respectively, which is basically consistent with the hardware performance of multi-GNSS receivers at home and abroad. Using the selected standalone BDS receiver, the single-day solution precision of the horizontal directions of station coordinates is 3.5 mm and the up direction is 9.9 mm; the precision of 2.208 m in horizon and 2.502 m in vertical can be realized with single-epoch pseudorange SPP; the precision of horizontal directions of kinematic PPP with ambiguity resolution (PPP-AR) is better than 3 cm and the up direction is better than 5 cm; the convergence time of the PPP-AR is better than 27 min; the repeatability accuracy of single-day solution for baselines shorter than 20 km is better than 0.7 cm in the horizontal direction and 1.8 cm in the vertical direction, and the RTK positioning accuracy for short baselines will not exceed 3 cm in the horizontal direction and 5 cm in the vertical direction. The homemade standalone BDS receiver has initially possessed the ability to independently provide reliable high-precision positioning services. Table and Figures | Reference | Related Articles | Metrics

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Six geographic application paradigms of big data Lun WU, Yuanqiao HOU, Yu LIU Acta Geodaetica et Cartographica Sinica    2024, 53 (8): 1465-1479.   DOI: 10.11947/j.AGCS.2024.20230199 Abstract （580）   HTML （78）    PDF（pc） （16210KB）（596）       Knowledge map    Save With the advent of the big data era, multi-source big data is on the rise, leading to the integration of data-driven research paradigms with geography. Geospatial big data based on individual behavior offers observations of massive individual behavior patterns, thereby achieving “from people to places” social perception and supporting various applications such as urban management, transportation, and public health. This article delineates six application paradigms focusing on geospatial big data from an application perspective, ranging from describing spatio-temporal distributions at a low level to optimizing spatial decision-making at a high level. The first direction involves a simple characterization of the spatio-temporal features of geographic phenomena and elements, while the second to fourth directions focus on exploring the rules and mechanisms behind spatio-temporal distribution characteristics. The last two directions provide support at the decision-making level. Furthermore, this article highlights issues in data acquisition, analysis methods, and application goals in big data applications. 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 （606）   HTML （76）    PDF（pc） （9549KB）（587）       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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Large models enabling intelligent photogrammetry: status, challenges and prospects Mi WANG, Xu CHENG, Jun PAN, Yingdong PI, Jing XIAO Acta Geodaetica et Cartographica Sinica    2024, 53 (10): 1955-1966.   DOI: 10.11947/j.AGCS.2024.20240068. Abstract （624）   HTML （40）    PDF（pc） （9351KB）（578）       Knowledge map    Save Developed from deep learning and transfer learning techniques, large models leverage vast training datasets and immense parameter capacities to create scale effects, thus inspiring the model's emergent capabilities and demonstrating strong generalization and adaptability in numerous downstream tasks. Large models, represented by ChatGPT and SAM, signify the arrival of the era of general artificial intelligence, providing new theories and techniques for the automation and intelligence of Earth's spatial information processing. To further explore the methods and pathways for large models to empower the field of photogrammetry, this paper reviews the basic problems and mission tasks in the field of photogrammetry, summarizes the research achievements of deep learning methods in intelligent photogrammetric processing, analyzes the advantages and limitations of supervised pre-training methods aimed at specific tasks; Besides, we elaborates on the characteristics and research progress of general artificial intelligence large models, focusing on the generalizability of large models in basic visual tasks and the potential in three-dimensional representation; Finally, this paper explores the current challenges and future trends of large model technologies in the field of photogrammetry, from the perspectives of training data, model fine-tuning strategies, and heterogeneous multi-modal data fusion strategies. Table and Figures | Reference | Related Articles | Metrics

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Analysis of InSAR time-series deformation monitoring accuracy of domestic satellite Bing XU, Yan ZHU, Zhiwei LI, Huiwei YI, Miaowen HU, Qi CHEN, Kun HAN, Xun DU Acta Geodaetica et Cartographica Sinica    2024, 53 (10): 1930-1941.   DOI: 10.11947/j.AGCS.2024.20230572. Abstract （664）   HTML （45）    PDF（pc） （10153KB）（569）       Knowledge map    Save The successful launch of the Lutan-1 satellite group (LT-1) has achieved the development of China's L-band interferometric SAR satellite from scratch. For obstacle avoidance, a small part of the spatial baselines of LT-1 satellite was long, but the length of the baseline has been controlled to within 400 meters after the orbit adjustment. In order to verify the availability and accuracy of LT-1 satellite data, this article takes the Datong mining area in Shanxi Province as an example and obtains 25 LT-1 strip pattern image data from December 23, 2022 to May 20, 2023, respectively, for SBAS-InSAR and PS-InSAR data processing. By comparing and analyzing the deformation monitoring results of time-series InSAR and GPS stations in the light of sight, the standard deviations of the two are 5.7 mm/a (SBAS-InSAR) and 3.4 mm/a (PS-InSAR), respectively. The root mean square error of the time series is less than 5 mm, indicating high consistency. The research has shown that domestically produced LT-1 satellites have high-precision deformation monitoring capabilities, providing reliable data assurance for domestic terrain surveying and deformation monitoring. Table and Figures | Reference | Related Articles | Metrics

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3D Gaussian radiation field modeling for real-scene bridges Wei MA, Qiang TU, Jianping PAN, Lidu ZHAO, Wei TU, Qingquan LI Acta Geodaetica et Cartographica Sinica    2024, 53 (9): 1694-1705.   DOI: 10.11947/j.AGCS.2024.20240071 Abstract （715）   HTML （73）    PDF（pc） （25350KB）（557）       Knowledge map    Save Realistic 3D modeling and digital twins have become essential foundations for bridge operation and management. However, given the complex geometric structures of bridges, current 3D modeling methods face issues such as large amounts of raw data collection, low modeling efficiency, and missing or deformed model details. In response to these challenges, this paper investigates a bridge realistic 3D reconstruction method based on 3D Gaussian radiance fields. This method utilizes 3D Gaussian functions to construct a Gaussian radiance field from sparse point clouds generated by captured images. Adaptive optimization of radiance field parameters is performed based on stochastic gradient descent, and real-time visualization of the 3D model is achieved through differentiable rasterization, resulting in high-quality bridge 3D reconstruction and rendering. The study explores the impact of different image resolutions and various parameter changes on bridge modeling. Comparisons with traditional methods are made to provide theoretical and technical support for further bridge applications, promoting efficient and accurate realistic 3D reconstruction of complex bridge structures. Table and Figures | Reference | Related Articles | Metrics

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Key technologies for spaceborne SAR payload of LuTan-1 satellite system Yunkai DENG, Yu WANG, Kaiyu LIU, Naiming OU, Dacheng LIU, Heng ZHANG, Jili WANG Acta Geodaetica et Cartographica Sinica    2024, 53 (10): 1881-1895.   DOI: 10.11947/j.AGCS.2024.20230263. Abstract （741）   HTML （33）    PDF（pc） （20110KB）（528）       Knowledge map    Save LuTan-1 (referred as LT-1) is China's first civil synthetic aperture radar (SAR) satellite mission to monitor the ground deformation with high precision by differential interferometry technology. The LT-1A and LT-1B have been success-fully launched on January 26 and February 27, 2022, respectively. The data acquisition schedule of LT-1 mission is divided into two stages, which corresponding to two specific orbit configurations. In the first stage, two satellites fly in a compact formation to get the digital elevation model (DEM) using the bistatic InSAR strip mode. In the second stage, both satellites fly in a common reference orbit with 180° separation. The revisit time of the individual satellite is 8 days, and it can be reduced to 4 days with two satellites. LT-1 satellite constellation can stably obtain time series data, so that we can monitor the ground deformation with high precision. Moreover, the multi-mode polarimetric payload will be utilized to obtain single-pass multi-polarimetric InSAR and hybrid polarimetric SAR data for forestry, land resource surveys, disaster monitoring, etc. In this paper, the key technologies of the LT-1 SAR payload, including phase synchronization, ambiguity suppression and system calibration, are systematically described and analyzed.The maximum resolution of LT-1 is 3 m, and the maximum swath width is 400 km, respectively. The azimuth ambiguity-to-signal ratio (AASR) of the interference wave position is better than -20 dB.The performance is partially demonstrated by ground testing and on-orbit actual measurement data. 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 （468）   HTML （83）    PDF（pc） （4381KB）（519）       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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The design and implementation of the open geospatial engine (OGE) Jianya GONG, Peng YUE, Longgang XIANG, Haoru WU, Kaixuan WANG, Ruixiang LIU, Baoxin TENG Acta Geodaetica et Cartographica Sinica    2025, 54 (4): 587-602.   DOI: 10.11947/j.AGCS.2025.20250051 Abstract （701）   HTML （93）    PDF（pc） （14201KB）（504）       Knowledge map    Save The construction of new spatiotemporal information infrastructure relies on innovative software middleware and geographic information service models. Driven by cloud computing, artificial intelligence, and big data, traditional spatiotemporal data infrastructure is evolving from data services to intelligent computing services. This paper focuses on a spatiotemporal intelligent computing service platform, aiming to develop a Digital Earth Cube-ready service. It explores key technologies in organizing, computing, and reasoning with geospatial big data, and develops the open geospatial engine (OGE) system, which deeply integrates computing power, data, and algorithms while promoting openness and sharing. The system provides a data-ready, analysis-ready, and decision-ready geospatial data-information-knowledge service framework, forming an open geospatial engine (OGE) and establishing a new type of spatiotemporal information infrastructure based on spatiotemporal cube management and geospatial big data analysis. Based on this foundation, an OGE prototype system has been developed, integrating various types of Earth observation data accumulated by Wuhan University and related institutions. A series of typical spatiotemporal analysis experiments covering raster, vector, and thematic data were conducted, validating OGE's capabilities in managing and analyzing geospatial big data. Table and Figures | Reference | Related Articles | Metrics

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Robust GNSS/SINS positioning based on the SE2(3)-EKF framework LI Xin, MENG Shuolin, HUANG Guanwen, ZHANG Qin, LI Hanxu Acta Geodaetica et Cartographica Sinica    2023, 52 (10): 1640-1649.   DOI: 10.11947/j.AGCS.2023.20220526 Abstract （537）   HTML （35）    PDF（pc） （6055KB）（490）       Knowledge map    Save For GNSS/SINS integrated navigation, the large errors, such as attitude misalignment angle, will cause the inconsistent coordinates definition of state error and large linearization error, thus the performance of traditional filtering and positioning is reduced, especially in the complex GNSS observation environment. In this paper, the attitude, velocity, and position states are reconstructed as a special SE2(3) group element, considering the bias of gyro and accelerometer, a group-vector mixed error model is formed, and then one GNSS/SINS robust filtering algorithm (RLIEKF) based on left invariant measurement is studied. The superiority of the proposed method is validated via the vehicle integrated navigation experiment with large misalignment angle error and GNSS outliers in urban environment. The experimental results show that, compared with traditional EKF method, the attitude angle error is considered in time update and GNSS measurement update of the proposed RLIEKF, thus it has a fast convergence speed under different large misalignment angles, without complicated and long-time attitude alignment steps, which can better deal with the problem such the interrupt GNSS signal during a short time. Because the accuracy of innovation is significantly improved, thus it is more robust to complex observation environment, and with a fast computational efficiency, therefore it has excellent engineering practical value. Reference | Related Articles | Metrics