[Objectives] The rapid advancement of Large Language Models (LLMs) has created new opportunities for intelligent geospatial data processing. However, current integration approaches between LLMs and Geographic Information Systems (GIS) still face key challenges, such as data privacy risks associated with cloud-only architectures, incomplete integration with native GIS toolchains, and the lack of standardized communication protocols for cross-platform interoperability. To address these limitations, this study proposed Smart-QGIS, an agent prototype system for geospatial data processing and mapping built on the Model Context Protocol (MCP). The system supports localized deployment while maintaining open protocol compatibility, enabling flexible integration with both local and cloud-based LLMs. The primary objective is to establish a secure, extensible, and functionally complete intelligent GIS framework that bridges natural language interaction and professional spatial analysis workflows. [Methods] Smart-QGIS was developed on the QGIS platform and uses MCP as a standardized communication bridge between LLMs and native GIS functional interfaces. The system enables end-to-end task execution, allowing users to convert natural language instructions directly into executable spatial analysis operations. It adopted a multi-process modular architecture consisting of five coordinated layers: a user interaction layer, a plugin mediation layer, an MCP communication layer, a local execution layer, and a model inference layer. This architecture ensures system scalability, functional extensibility, and operational stability, while supporting integrated workflows including data loading, spatial analysis, and cartographic visualization. [Results] System performance was evaluated using the vector administrative boundary of Shaanxi Province and digital elevation model raster data. Two model deployment strategies were tested, including a locally deployed open-source OpenAI-compatible GPT model via Ollama and a cloud-based Alibaba Qwen LLM. Through Smart-QGIS, representative GIS tasks such as data loading, clipping, slope calculation, layer visualization, and automated map production were executed interactively. Results demonstrated that Smart-QGIS can accurately interpret complex, multi-step instructions while maintaining an efficient response time, typically below 75 seconds. For routine geospatial processing and visualization tasks, system performance is generally equivalent to or exceeds that of typical professional GIS users, while cloud-based models show higher efficiency in complex task execution. [Conclusions] Overall, the MCP-based localized LLM-GIS integration framework demonstrated advantages in privacy protection, functional coverage, and protocol universality. The system significantly lowers the technical barrier for geospatial data processing, enabling non-specialist users to perform complex spatial analysis tasks efficiently. The proposed architecture provides a practical technical pathway toward building open, collaborative, and intelligent GIS ecosystems, with strong potential for applications in intelligent spatial decision support, automated geospatial data services, and next-generation human-AI collaborative geospatial analysis environments.

[Objectives] SWOT satellite altimetry provides a new capability for large-scale river network water level monitoring; however, the original products are constrained by outlier noise, near-shore mixed pixels, and inter-orbit datum inconsistencies, which limit their suitability for high-accuracy hydrological applications. [Methods] Targeting the middle and lower reaches of the Yangtze River characterized by low gradients, slow flow, and complex boundary conditions, this study proposes a SWOT-based water level harmonization and modeling framework. Inter-orbit alignment is achieved by constructing a bias field from orbit overlap regions, thereby mitigating systematic datum inconsistencies across tracks. High-confidence RiverSP nodes are employed as reference observations, and pixel-level errors in PIXC water levels are modeled using a random forest approach that integrates backscatter characteristics, terrain attributes, and river morphology features. On this basis, a variance-guided α-weighted adaptive fusion strategy is implemented to balance the structural stability of RiverSP with the spatial detail preserved by PIXC. The framework ultimately produces a water level field referenced to a unified vertical datum, spatially continuous along the river network, and suitable for hydrological analyses in large, low-slope river systems. [Results] The correction results demonstrate a substantial improvement in the accuracy and consistency of the SWOT water level products. Specifically, the MAE of the PIXC and RiverSP products decreases from 0.41 m and 0.32 m to 0.15 m and 0.09 m, respectively, while the agreement between pixel-level water levels and hydrological gauge observations is markedly enhanced, with the coefficient of determination increasing from 0.73 to 0.96. Based on the corrected water-level fields, monthly longitudinal profiles and intra-annual time series at representative cross-sections are constructed to examine the spatiotemporal behavior of river water levels. The results reveal distinct hydrological response characteristics, including the along-river attenuation of flood peaks and the backwater effects induced by tidal forcing in the estuarine region. In the middle reaches of the Yangtze River, the intra-annual water-level variability is pronounced, with peak-to-trough differences exceeding 20 m, reflecting the combined influence of seasonal runoff regulation and channel geometry. In contrast, the estuarine cross-section exhibits strong tidal modulation, with monthly mean water levels reaching a maximum of approximately 5.44 m and a minimum of about 0.8~0.9 m, corresponding to an annual amplitude of roughly 4~5 m. These findings indicate that the corrected SWOT-derived water-level fields are capable of capturing both fluvial flood dynamics and estuarine tidal influences, thereby providing a reliable basis for hydrological analysis in large river systems. [Conclusions] These results confirm that the framework can produce spatially continuous, datum-consistent water level time series, suitable for large-scale hydrological analysis and flood monitoring.

[Significance] Fine-grained orientation and localization algorithms for ground images via cross-view image matchingis a high-precision geographic positioning technology that realizes sub-meter position estimation and sub-degree direction (heading) estimation by matching ground images with satellite reference images. The core challenge of this technology is to overcome the huge difference in geometry and appearance between the ground view and the satellite view, and realize accurate position and heading estimation through the methods of view unification, feature matching and attitude estimation. This technology is closely related to visual positioning, image retrieval, etc. It has important application value in GNSS rejection scenes such as automatic driving high-precision positioning and mobile robot navigation, and provides an absolute positioning solution for intelligent systems without relying on special infrastructure. [Analysis] Through literature collection, this paper systematically summarizes the research progress onfine-grained orientation and localization algorithms for ground images via cross-view image matchingfrom 2021 to 2025. In the context of technological development, the definition, technical route and domain boundary of this technology are clarified, the dynamic evolution process of this field from early exploration and technical differentiation to breakthrough in supervision mode is revealed, and a systematic classification system is constructed from three dimensions: unified perspective mode, orientation and positioning task type and supervision mode, and the core principles, technical details and advantages and disadvantages of various algorithms are deeply analyzed, and the types of feature extraction backbone networks are systematically sorted out. This paper summarizes the four public data sets resources and evaluation index systems of VIGOR, OxfordRobotCar, KITTI and Fordmulti-AV, and quantitatively analyzes the performance of various algorithms. In the analysis of core challenges, the key problems such as bridging the geometric and semantic differences across perspectives, inefficient fusion of weak supervised signals, attenuation of joint estimation accuracy under high directional noise, limited generalization of dynamic scenes, and limitations of timeliness and scene coverage of existing data sets are extracted. In the future research direction, five breakthrough directions are put forward: constructing BEV representation and efficient matching mechanism with geometric truth and semantic consistency, realizing hierarchical fusion and cross-domain generalization enhancement of weak supervision signals, overcoming the problem of robust joint estimation in high-direction noise and symmetrical scenes, improving the adaptability of the algorithm in dynamic and complex environments, and promoting technical engineering and improving the evaluation system, which provides clear guidance for the development of the field. [Conclusions] This paper systematically clarifies the technological evolution of this field from 2021 to 2025, constructs a multi-dimensional algorithm classification system, quantitatively analyzes the performance of mainstream algorithms and extracts the core technical challenges, and puts forward five breakthrough directions to establish a clear path for field research. This research provides reference for theoretical research, algorithm design and technology selection in this field, and also provides theoretical support for the engineering landing and large-scale application of technology in GNSS rejection scenarios.

[Objectives] Image feature matching is a method that establishes correspondences between images by identifying homologous image points across different images. High-precision image feature extraction and robust matching are fundamental tasks in photogrammetry, remote sensing, and computer vision. However, prevailing image feature extraction and matching algorithms primarily focus on improving matching completeness under various complex conditions, often neglecting the critical issues of insufficient feature localization accuracy and a scarcity of high-precision matching points. Although the sparse correspondences obtained by existing feature matching algorithms are sufficient for most tasks, issues such as insufficient accuracy in feature point localization and a scarcity of high-precision matches can still compromise the reliability of subsequent practical applications. [Methods] To address these limitations, this paper proposes a modular feature matching framework enhanced by arbitrary-scale super-resolution. A typical image feature matching workflow comprises three core steps: feature extraction, feature description, and feature matching. The framework integrating an image super-resolution reconstruction module into the traditional feature extraction and matching process enhances the localization accuracy and detectability of image feature points. This approach effectively increases the number of high-precision matching points and improves overall matching accuracy. Furthermore, the modular architecture allows for the substitution of individual algorithmic components to adapt to diverse matching scenarios, ensuring broad applicability. [Results] Comparative experiments were conducted against current mainstream feature extraction and matching methods on the public datasets MatrixCity and ETH3D. On the MatrixCity dataset, our method achieves both an increased number of high-precision match points and improved feature matching accuracy, surpassing the AKAZE algorithm by up to 7 times and 58.65%, respectively. Performance gains were observed across all tested super-resolution scales, with the 6× super-resolution scale yielding the highest number of correct matches and the 20× scale achieving the lowest Root Mean Square Error (RMSE). On the ETH3D dataset, the number of feature points has significantly increased while maintaining a high number of accurately matched points. The AKAZE algorithm achieved a 54 times increase in the number of matching points. [Conclusions] Compared to existing mainstream methods, the proposed approach shows significant advantages in both matching accuracy and the quantity of matching points. The relevant modular algorithms can be directly applied to enhance the performance of current feature extraction and matching pipelines. Experimental results in complex scenarios demonstrate the robustness of the proposed algorithm. This provides a reference for enabling high-precision feature matching technology to serve more robustly and extensively in practical applications that rely on high-accuracy matching, such as 3D reconstruction, remote sensing surveying, and visual positioning.

[Objectives] Remote sensing change detection technology is crucial for urban management, environmental monitoring, and disaster emergency response, as it provides timely and accurate information on changes in the Earth's surface. However, traditional change detection methods based solely on remote sensing images face limitations such as the inability to fully capture complex changes and the lack of effective integration of multi-source data, leading to inaccurate detection results. The integration of remote sensing images with Digital Surface Models (DSM) is a promising solution, but challenges remain in effectively fusing data from different modalities and accurately aligning their spatiotemporal features. This paper aims to address these issues by proposing a novel multimodal remote sensing change detection method that enhances the accuracy and robustness of change detection through effective data fusion and spatiotemporal feature integration. [Methods] To address the limitations of traditional change detection methods, this paper proposes a multimodal remote sensing change detection method named Cot-FresUNet, based on a Siamese neural network and a spatiotemporal dependency model. The method leverages optical images and DSM data as inputs and employs a two-dimensional twin neural network architecture in the encoding stage. This architecture is enhanced by a multi-scale feature fusion mechanism, which significantly improves the model's ability to integrate common features from multimodal data and distinguish subtle similarities and differences in feature representations. Additionally, the proposed spatiotemporal dependency model fully integrates the temporal continuity and spatial correlation of surface changes through multi-temporal feature interaction, thereby enhancing the model's capacity to represent and detect changes accurately. In the decoding stage, spatial pyramid pooling is utilized to obtain multi-scale features, which further enhances the deep fusion of local features with global contextual information. This comprehensive approach ensures that the model can effectively capture both fine-grained and large-scale changes in the monitored areas. [Results] The effectiveness of the proposed Cot-FresUNet method was evaluated on the publicly available 3DCD dataset and compared with eight existing methods. The results demonstrated significant improvements, with the proposed method achieving a 14.45% increase in F₁-score and a 15.77% increase in Mean Intersection over Union (mIoU) compared to the previous best-performing method. Detailed analysis of the feature maps and learning effects at different stages of the model revealed the strengths of the proposed architecture. Furthermore, the performance of four fusion strategies, including early fusion and late fusion, was compared, and the fusion of optical imaging and DSM inputs was proven to achieve the highest accuracy in change detection through ablation experiments. Finally, the complexity metrics of eight types of models were compared, and the proposed method showed significant advantages in terms of parameter count and computational cost, making it highly efficient for practical applications. [Conclusions] The results indicate that the proposed Cot-FresUNet method can effectively improve the accuracy of multimodal change detection and address the issues of feature confusion, reliance on single-feature representations, and insufficient interaction of different spatiotemporal information that are commonly found in traditional twin neural network models. This study not only provides a robust solution for remote sensing change detection but also opens new avenues for future research in multimodal data integration and spatiotemporal analysis. The proposed method's ability to integrate optical images and DSM data through an advanced fusion mechanism and its enhanced representation of spatiotemporal features make it a valuable tool for applications in urban management, environmental monitoring, and disaster response.

[Objectives] Existing quadrilateral Discrete Global Grid Systems (DGGS) predominantly employ solidified and fixed subdivision rules. Although there is a proliferation of numerous grid systems, they frequently operate independently of one another. This fragmentation has unfortunately led to a series of critical issues, including a wide diversity of incompatible standards, rigidly solidified subdivision rules, and significantly poor algorithm reusability. These persistent challenges severely hinder and obstruct the effective cross-grid reuse of upper-layer application algorithms within the broader geospatial domain. [Methods] To effectively breakthrough and overcome these formidable technical bottlenecks, this paper comprehensively proposes a novel multi-scale universal quadrilateral DGGS generation method that is fundamentally based on configurable subdivision radix sequences. This innovative approach effectively decouples the generic grid generation logic from specific concrete subdivision structures. The method proceeds through the following steps: First, the core grid subdivision rules are systematically abstracted into configurable external parameters, which are referred to as subdivision radix sequences. This step realizes the complete separation of the abstract grid generation logic from the specific physical structures. Second, a specialized cardinality extension strategy is meticulously designed. By establishing a robust virtual base mapping mechanism, this strategy successfully resolves the difficult compatibility challenges associated with non-standard integer-multiple subdivisions found in complex grid systems, such as the GeoSOT grid system. Finally, a unified logical index space is constructed to strictly ensure that, even when heterogeneous subdivision strategies are adopted at different hierarchy levels, their internal logical structures, algebraic encoding operations, and topological relationships still strictly adhere to a unified and consistent algorithmic workflow. [Results] Experiments concerning grid compatibility, on-demand generation capability, and algorithm reusability were conducted using global simulated discrete coordinate point data and real-world regional polygon data. The experimental results demonstrate the following: Regarding grid compatibility, the proposed method generates subdivision results identical in quantity to those of existing mainstream grid structures (GeoSOT and GMGICM). Furthermore, the encoding and decoding time costs are comparable to those of mainstream methods, verifying the proposed method's compatibility with existing grid frameworks. Regarding flexibility, various novel grid systems, such as "all-3", "all-5", and "2-3-5 cyclic" subdivisions, which can be constructed solely by adjusting external parameters, validating the method's capability for on-demand grid generation. Regarding algorithm reusability, the proposed algorithm supports multi-scale vector polygon gridding applications across seven heterogeneous grid systems using a single set of application code. This effectively obviates the linear growth of code volume associated with traditional methods, thereby validating the method's capability for low-cost migration and reuse across heterogeneous grids.