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Table of Content

    28 October 2026, Volume 55 Issue 9
    Geodesy and Navigation
    Physically aware computation of local reference ellipsoid parameters
    Zhaoquan Li, Bing Ji, Tao Zhang, Dongquan Zhou, Shaofeng Bian
    2026, 55(9):  1521-1529.  doi:10.11947/j.AGCS.2026.20260224
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    In traditional geodesy, geometric projection methods are commonly employed to reduce terrestrial elevation points to the reference ellipsoid, implicitly assuming that the external equipotential surface of the normal gravity potential is geometrically similar to the reference ellipsoid. From the perspective of physical geodesy, however, the flattening of the external equipotential surfaces of the normal gravity field varies with height in a non-uniform geometric manner, and the constant-flattening assumption adopted in the traditional expanding-ellipsoid method fails to accurately characterize this physical property. To address this issue, this paper proposes a method for constructing a local reference ellipsoid based on the equipotential condition of the normal gravity potential. By determining the normal distances from a specific normal gravity equipotential surface to the equator and the poles, a local reference ellipsoid is redefined for each height, so as to more faithfully represent the local geometric characteristics of the normal-gravity equipotential surface. Theoretical derivations and numerical analyses show that this local ellipsoid model can more accurately capture the variation of equipotential surfaces in the gravity field in high-altitude regions, and exhibits better physical consistency than the traditional reference ellipsoid model.

    GNSS distribute suppression jamming method based on directional antennas
    Zhiheng Liu, Weiping Liu, Zhiwei Lü, Bo Jiao
    2026, 55(9):  1530-1541.  doi:10.11947/j.AGCS.2026.20260107
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    To address the issues of low energy utilization efficiency, high deployment costs, and poor concealment associated with traditional omnidirectional antennas in GNSS distribute suppression jamming, this paper proposes a GNSS distribute suppression jamming method based on directional antennas. This method is built upon the 3D space GNSS distributed suppression jamming method based on slope optimal point. By constructing a 3D ellipsoidal radiation model for directional antennas and establishing coordinate transformation relationships, an improved Euclidean clustering algorithm based on geometric constraints is designed to accurately solve the optimal beam pointing of the antenna. Meanwhile, the deployment process of jamming sources is reconstructed to match the characteristics of directional antennas. Furthermore, a combined omnidirectional-directional antenna jamming mode is designed, employing a sequential selection strategy to integrate the advantages of both antennas types. Simulation results show that the proposed method can reduce the number of jamming sources by 34.62% to 65.63% compared with the omnidirectional antenna scheme on the premise of achieving approximately 95% of the target jamming coverage. The combined omnidirectional-directional mode further reduces the number of jamming sources by 20% to 50% relative to the purely directional solution. Moreover, the higher the antenna gain, the more significant the deployment scale optimization. At the same time, it significantly reduces signal overlap redundancy, improving energy utilization efficiency and deployment concealment.

    Great earthquake magnitude estimation using HR-GNSS with epicentral-distance geometric auxiliary information and attention mechanisms
    Yanyan Li, Ao Wang, Rui Tu
    2026, 55(9):  1542-1554.  doi:10.11947/j.AGCS.2026.20260104
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    Rapid and accurate earthquake magnitude estimation is crucial for post-earthquake emergency response and earthquake disaster prevention and mitigation. High-rate global navigation satellite system (HR-GNSS) observations can rapidly capture near-field coseismic deformation and provide important data for magnitude estimation of large earthquakes. Existing methods, however, still face magnitude saturation, low signal-to-noise ratios, uneven station distributions, and insufficient use of source-station geometry in deep-learning models. To address these problems, this study proposes an epicentral-distance geometry-assisted station-wise attention convolutional neural network (EDGSA-CNN) model. The model uses a convolutional neural network (CNN) to extract three-component displacement waveform features from multiple stations. It employs a self-attention mechanism to model feature correlations among stations and a multilayer perceptron (MLP) to nonlinearly encode epicentral distances. The waveform features and geometric auxiliary information are then fused to estimate the moment magnitude of large earthquakes. Experiments using synthetic data from the Chilean subduction zone show that EDGSA-CNN generally yields lower magnitude-estimation errors than the waveform-only CNN baseline and peak ground displacement (PGD) empirical model, while maintaining relatively low errors for high-magnitude samples. Epicentral-distance perturbation tests indicate that the model is robust to early epicentral-location errors to some extent. Cross-regional synthetic tests and tests using observations from real large earthquakes further indicate that the model exhibits a certain degree of applicability and stability across different data sources and observational conditions. This study can provide potential technical support for accurate magnitude estimation of large earthquakes and earthquake early warning applications.

    A staged tunnel registration method integrating dynamic pooling-based adaptive registration and neighborhood feature-assisted DTW
    Haili Sun, Xinwu Yan, Ping Yang, Sheng Hai, Ruofei Zhong, Mingquan Wang
    2026, 55(9):  1555-1566.  doi:10.11947/j.AGCS.2026.20250550
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    To address cross-epoch misalignment of multi-epoch tunnel mobile laser scanning data caused by accumulated mileage errors and variations in acquisition poses, a staged registration method integrating dynamic pooling-based adaptive registration and neighborhood feature-assisted dynamic time warping (DTW) is proposed. First, two-dimensional tunnel images are generated from point-cloud intensity information, and local features are extracted using SuperPoint. Building upon the rotary positional encoding and attention-based matching framework of LightGlue, a dynamic pooling strategy tailored to the feature distribution of tunnel images is introduced to achieve coarse mileage alignment over large offsets between multi-epoch datasets. Subsequently, tunnel cross-sectional point clouds are transformed into radial sequences in polar coordinates, and dynamic time warping is employed to establish cross-epoch correspondences between tunnel cross-sections. Adaptive-neighborhood intrinsic shape signature (ISS) keypoint extraction and least-squares optimization are then applied to achieve refined cross-sectional registration. Experiments were conducted on multi-epoch mobile laser scanning data acquired from shield and mined tunnels of Chongqing Metro. The image-registration and point-cloud-registration errors were 1.3 and 1.2 cm, respectively, for the shield tunnel, and 3.7 and 3.6 cm, respectively, for the mined tunnel. After refined registration, the Chamfer distances converged to 0.1~0.3 cm, with an average of approximately 0.2 cm, representing a reduction of approximately 90% compared with those before registration. The results demonstrate that the proposed method can effectively accommodate cross-epoch alignment of tunnels with different structural characteristics and provide a reliable spatial reference for tunnel deformation monitoring, defect evolution analysis, and multi-epoch data fusion.

    Marine Surveying and Mapping
    Efficient interpretation algorithm for foreign nautical chart substrate annotations considering semantics and image features
    Jian Dong, Chaoyu Tan, Lulu Tang, Zeyuan Dai, Tian Xie, Changlin Chen, Er Ni
    2026, 55(9):  1567-1581.  doi:10.11947/j.AGCS.2026.20260115
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    In response to current issues in the automatic interpretation of foreign raster nautical chart substrate annotations, such as poor adaptability to complex scenarios, low efficiency, and the disconnection between semantics interpretation and image processing, this paper proposes an efficient interpretation algorithm for foreign nautical chart substrate annotations considering semantics and image features. Firstly, the algorithm constructs an image optimization strategy based on the visual representation features of substrate annotations. By applying land area masking and adaptive threshold segmentation, it effectively filters out interference from the complex background of raster charts. Subsequently, template matching technology is introduced to establish a substrate annotation template library based on Hu invariant moment features, achieving integrated interpretation for both character recognition and semantic parsing. Finally, to address the difficulty in constructing templates for composite annotations, a two-level annotation slice segmentation model considering semantic integrity is designed, solving the interpretation challenge of complex combined annotations. Experimental results show that: ①The proposed algorithm successfully achieves efficient interpretation of foreign chart substrate annotations, demonstrating high precision, recall, recognition completeness rate, and semantic accuracy during the processes of annotation recognition and semantic parsing.②Comparative experiments with existing methods under scenarios of varying complexity indicate that the proposed algorithm maintains higher recognition accuracy while further improving overall processing efficiency. Moreover, it effectively addresses issues of missed annotations and semantic parsing errors encountered by comparative methods in medium-and high-complexity scenarios, proving the superior robustness and efficiency of our approach.

    Photogrammetry and Remote Sensing
    An open-vocabulary semantic-enhanced 3DGS-SLAM method for complex underground spaces
    Xiaohu Lin, Tao Yan, Bisheng Yang, Chi Chen, Yinan Gao, Xiang Wang
    2026, 55(9):  1582-1598.  doi:10.11947/j.AGCS.2026.20260244
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    Fine-grained mapping and intelligent inspection of underground spaces are fundamental to autonomous robotic operation, emergency rescue, and safety maintenance. However, existing 3D Gaussian Splatting SLAM (3DGS-SLAM) methods mainly emphasize geometric reconstruction and photorealistic rendering, while their semantic understanding capability remains limited in complex underground environments. Moreover, the storage and optimization of high-dimensional semantic features introduce substantial memory overhead, and degraded observations caused by low illumination and incomplete depth measurements often lead to inconsistent semantic representations across multiple views. To address these issues, this paper proposes an open-vocabulary semantic-enhanced 3DGS-SLAM method for complex underground spaces. First, a multi-scale open-vocabulary semantic feature field is constructed using SAM (segment anything model) and CLIP (contrastive language-image pretraining), together with depth completion and confidence estimation. An autoencoder is further introduced to compress CLIP features, reducing GPU memory consumption while preserving semantic discriminability. Second, a semantic uncertainty quantification model is proposed by integrating an image-enhancement residual prior and a depth-confidence prior. A temporal smoothing mechanism is also introduced to reduce the semantic weights of low-quality observations, thereby mitigating multi-view semantic inconsistency. Finally, photometric, depth, Gaussian-scale regularization, illumination-stability, and uncertainty-weighted semantic losses are combined to form a joint optimization objective for geometry, appearance, and semantics, while supporting open-vocabulary semantic queries. To validate the effectiveness of the proposed method, experiments were conducted in multiple representative underground spaces using a self-integrated mobile robotic platform. The proposed method achieves a peak signal-to-noise ratio (PSNR) of 23.0 dB and a structural similarity index measure (SSIM) of 0.88 in underground spaces, outperforming representative methods such as SplaTAM and GS3LAM. On the ScanNet dataset, it achieves an open-vocabulary segmentation mIoU of 39.08%, showing clear improvement over competing methods. The results demonstrate that the proposed method can achieve efficient and stable open-vocabulary semantic mapping in complex underground environments, providing fine-grained semantic support for intelligent underground inspection and human-robot interaction.

    Research on rational function model for Tianhui-2 bistatic imaging geometry
    Xin Xiong, Guowang Jin, Jiahao Li, Hao Ye, Le Yang
    2026, 55(9):  1599-1611.  doi:10.11947/j.AGCS.2026.20260075
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    This study investigates a three-equation rational function model (RFM) for the bistatic imaging geometry of the Tianhui-2 (TH-2) satellite system. We rigorously derive both forward and inverse RFM formulations and design the least-squares-based RFM parameter solution method. Departing from the conventional two-equation RFM, our formulation incorporates an interferometric phase equation, achieving a two-way precise mapping between image-space observations (azimuth coordinate, range coordinate, and interferometric phase) and geodetic coordinates (latitude, longitude, and height). Experimental validation is conducted using 6 independent TH-2 bistatic imaging InSAR datasets. The results the proposed three-equation RFM can accurately fit the strict imaging model. The geocoding experiments further verified the practicality of the model.

    Cultivated land extraction in mountainous and hilly areas based on MSFE-OCNet algorithm
    Shuhan Jia, Yuxiang Feng, Quanhua Zhao, Yu Li
    2026, 55(9):  1612-1629.  doi:10.11947/j.AGCS.2026.20260129
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    Cultivated land serves as the core guarantee of food security, and its accurate extraction is of great significance for optimizing the allocation of land resources and advancing sustainable agricultural development. Although the object context network (OCNet) can capture contextual information from remote sensing images to a certain extent, it suffers from weak extraction capability and single-scale limitations in semantic aggregation when extracting cultivated land in mountainous and hilly regions characterized by fragmented patches, large scale disparities and uneven spatial distribution. To address these drawbacks, this study proposes a multi-scale feature enhancement-OCNet (MSFE-OCNet) algorithm. To strengthen the model's ability to identify cultivated land of diverse sizes, the algorithm constructs an MSFE block integrating the atrous spatial pyramid pooling (ASPP) module for local detail feature extraction and a multi-branch OC module for global contextual modeling. The ASPP module adopts dilation rates of (12, 24, 36), while the multi-branch OC module utilizes receptive field combinations of (1, 2, 4). Meanwhile, a weighted cross entropy-dice combined loss function is introduced during the training phase to mitigate insufficient training of small-sample land cover categories. To validate the performance of the proposed algorithm, this study takes Nanchang, Jingdezhen and Yingtan as representative areas for cultivated land extraction, and conducts a series of comparative and ablation experiments. Across three typical mountainous and hilly study areas (Nanchang, Jingdezhen and Yingtan), the MSFE-OCNet outperforms competing algorithms including U-Net, GTNet, SAM and DeepLabV3+ in all accuracy metrics. For MSFE-OCNet, the overall accuracy (OA), Kappa coefficient and mean class accuracy (MCA) reach 92.87%, 90.50% and 91.33% in Nanchang; 93.69%, 91.56% and 92.15% in Jingdezhen; and 93.71%, 91.59% and 92.22% in Yingtan. For the cultivated land category specifically, the intersection over union (IoU) and F1-score of the proposed algorithm are 90.03% and 94.76% in Nanchang, 92.73% and 96.23% in Jingdezhen, and 92.05% and 95.86% in Yingtan. Experimental results demonstrate that the proposed algorithm effectively improves the identification integrity and classification accuracy of fragmented cultivated land in mountainous and hilly regions.

    Heterogeneous building point cloud registration via deformable self-attention and density-invariant keypoint detection
    Sihan Liu, Shiwei Shao, Yuefeng Lu, Ziqi Ding, Chunlin Mu, Xiaolei Dou, Chengzhe Lü, Zhirui Jia, Zhenjie Jiao
    2026, 55(9):  1630-1640.  doi:10.11947/j.AGCS.2026.20260177
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    To address the problems of insufficient registration accuracy and poor robustness caused by significant differences in acquisition modality, point cloud density, viewing perspective, and spatial coverage between terrestrial laser scanning and unmanned aerial vehicle (UAV) oblique photogrammetry point clouds, a heterogeneous point cloud registration method based on deformable self-attention and density-invariant keypoint detection is proposed. In the feature extraction stage, a spatial deformable self-attention mechanism is introduced to dynamically adjust the receptive field, enabling collaborative modeling of global contextual information and local geometric features, thereby enhancing the discriminative capability of feature representations. In the keypoint detection stage, a density-invariant saliency scoring strategy is designed to effectively suppress the influence of point cloud density variations on keypoint selection, achieving stable keypoint extraction across point clouds with different densities. In the matching stage, an edge-relaxation mechanism is incorporated to optimize the keypoint similarity matrix, reducing mismatches caused by non-overlapping regions, while the rigid transformation is estimated using the RANSAC algorithm. Experimental results demonstrate that the proposed method achieves a feature matching recall of 98.3% and a registration recall of 91.6% on the 3DMatch dataset. On the low-overlap 3DLoMatch dataset, it attains a registration recall of 75.9%, outperforming GeoTransformer by 2.9 percentage points and Predator by 17.8 percentage points. Furthermore, experiments conducted on a self-constructed heterogeneous building scanning dataset demonstrate that the proposed method exhibits robust performance under significant density variations and low-overlap conditions, validating its effectiveness and applicability for practical three-dimensional building reconstruction.

    Cartography and Geographic Information
    A spatial prediction method for street crimes by integrating causal discovery
    Zhanjun He, Yina Hu, Wantong Meng
    2026, 55(9):  1641-1652.  doi:10.11947/j.AGCS.2026.20260124
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    Spatial prediction of street robbery is a central task in criminal geography, providing critical guidance for the efficient governance of urban public security. However, most existing spatial crime prediction models rely on correlation analysis to establish relationships between environmental variables and criminal incidents, often overlooking the underlying causal mechanisms linking urban environments to street robbery. To bridge this gap, this study proposes a novel spatial prediction framework that integrates causal inference with geographic machine learning. First, the greedy equivalence search (GES) algorithm is employed to identify points of interest (POI) features that exert potential causal effects on robbery. Second, the geographically weighted random forest (GWRF) model is utilized to quantify the nonlinear and spatially heterogeneous relationships between the environmental features and robbery incidents. Using empirical data from Manhattan, New York city, the proposed framework demonstrates superior performance. The experimental results reveal that the GES algorithm effectively filters out POIs with causal implications, while the GWRF model successfully captures complex spatial variations. By incorporating causal inference, the proposed method not only enhances the precision of spatial street robbery prediction but also provides more scientific and actionable decision support for urban crime prevention and targeted interventions.

    Conceptual framework and construction mechanism of the pan-map symbol system
    Zhigang Zhao, Siyao Jiang, Yebin Chen, Yongli Chen, Renzhong Guo
    2026, 55(9):  1653-1665.  doi:10.11947/j.AGCS.2026.20260166
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    With the rapid development of new-generation information and communication technologies, such as artificial intelligence, big data, digital twins, and AR/VR, maps has exhibited a significant trend toward diversification, giving rise to a large number of pan-map forms that are distinct from traditional maps. The representational space of pan-maps has expanded from the “physical-social cultural” dual space to a ternary space encompassing the physical, social cultural, and information domains. To address the challenge that traditional two-dimensional static map symbol systems are inadequate for the multi-dimensional, multi-modal, and multi-scenario expressive needs of pan-maps in the ternary space, this study draws upon cartographic linguistics and the theory of visualization dimensions for pan-maps to construct a pan-map symbol system oriented toward objects in ternary space, and systematically elucidates its underlying expressive mechanisms. By integrating Saussure's dyadic semiotics and Peirce's triadic semiotics, we first clarify the essence of pan-map symbols as carriers of meaning construction and information transmission. We then extend the connotations of image, graphic, and textual symbols, revealing the continuous transition of pan-map symbols from precise geometry to realistic (concrete) and freehand (abstract) expression, and construct a continuous spectrum in which image symbols shift from “two-dimensional images” to “three-dimensional realistic representations”, while graphic symbols transition from “geographic geometry” to “conceptualized expression”. Finally, we systematically analyze the operational logic of the semantic, syntactic, and pragmatic dimensions of pan-map symbols. At the semantic level, they exhibit a tripartite signifying relationship of iconicity, indexicality, and symbolism, along with rhetorical heuristicness. At the syntactic level, visualization dimensions serve as the underlying grammatical units, establishing a hierarchical reconstruction rule of “basic morpheme—symbol word—symbol sentence” and distilling visual rhetorical rules encompassing visual enhancement, semantic transformation, and spatiotemporal narration. At the pragmatic level, we summarize four core modes—geometric convention, scene realism, cognitive reinforcement, and intertextual metaphor—that drive the spatial meaning construction of symbols across diverse scenarios. This study reveals the visualization logic of pan-maps in the information age and is expected to provide theoretical support for the expansion of the theoretical framework of pan-cartography.

    Building semantics-driven UAV geographic-matching localization in GNSS-denied urban environments
    Youneng Su, Zhiyong Lin, Yi Liu, Fubing Zhang, Xinming Zhu, Pengfei Pan
    2026, 55(9):  1666-1678.  doi:10.11947/j.AGCS.2026.20260161
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    UAV geographic-matching localization in GNSS-denied urban environments is a challenging problem in the navigation field. Existing image-matching-based methods are sensitive to texture variations and rely heavily on large-scale image databases. To address these issues, this paper proposes a building semantics-driven localization method. Taking pre-extracted building vector outlines from UAV orthophotos as input, the method formulates localization as a similarity search in a semantic embedding space. The proposed approach consists of two phases: offline database construction and query localization. In the offline phase, geometric and spatial-contextual semantic features are extracted from building outlines to construct semantic graphs; a specially designed semantic embedding model then learns discriminative embeddings, which are stored together with geographic coordinates to establish a reference library. In the query phase, a semantic graph is constructed from building outlines in the query image, and the UAV position is estimated through embedding retrieval, spatial clustering, and geometric consistency estimation. Experiments on both homologous and heterogeneous datasets demonstrate that the proposed method achieves median localization errors of 0.74 and 4.13 m, with success rates (error<20 m) of 98.30% and 77.00%, respectively, and per-query processing times of 0.938 and 0.957 s, outperforming nine competing methods including GCN, GraphSAGE, and SGFormer. Cross-city generalization experiments further validate its strong generalization capability in unseen urban environments.

    Building shape encoding model integrating skeleton and contour features
    Xiechun Lu, Peiyang Hou, Kai Ma, Zhanlong Chen, Yongyang Xu
    2026, 55(9):  1679-1694.  doi:10.11947/j.AGCS.2026.20260175
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    Building shape plays an important role in the cartographic generalization of buildings, and is essential for building feature selection, simplification, shape preservation, and multi-scale representation. Existing methods mostly perform modeling based on local or regional features of buildings. Although they can characterize local geometric variations in building contours, they lack effective constraints on global structure, making it difficult to simultaneously preserve contour details and global morphological characteristics. This limitation hinders the overall representation capability regarding complex building forms. This paper proposes an unsupervised shape representation learning network, skeleton-contour shape representation network model (SCSR-Net), which integrates building skeleton and contour features. By constructing a skeleton point-set autoencoder to learn skeleton features reflecting the global structure of buildings and a graph convolutional autoencoder to learn contour features reflecting local building details, the model achieves joint learning and fusion through a joint encoding and feature aggregation mechanism, which yields shape encodings that capture both skeleton and contour characteristics of building shapes. Experimental results demonstrate that the SCSR-Net effectively integrates global and local information, overcomes the limitations of single-feature encoding, and achieves excellent performance in shape matching and retrieval tasks, thereby validating the effectiveness of the proposed model.

    A graph Transformer method for river network pattern recognition incorporating three-dimensional terrain features
    Runze Chai, Jingzhong Li, Haowen Yan, Xiaolong Wang, Zhenyue Liu, Ling Wang
    2026, 55(9):  1695-1705.  doi:10.11947/j.AGCS.2026.20260139
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    This study proposes a graph Transformer method for river network pattern recognition incorporating three-dimensional terrain features. First, a dual graph is constructed from vector drainage network data, in which river segments are represented as graph nodes and the topological connections between river segments are represented as graph edges. Second, in addition to two-dimensional topological and geometric morphological features, three-dimensional terrain features, including elevation difference, slope, and channel gradient, are introduced to enhance the representation of the terrain context associated with river-segment nodes. Third, GraphSAGE is employed to sample and aggregate neighborhood information, thereby learning local confluence relationships as well as morphological and terrain differences between adjacent river segments. A Transformer-based self-attention mechanism is then introduced to model global structural dependencies within the drainage network graph, enabling the collaborative representation of local neighborhood relationships and overall spatial structural patterns. Finally, graph classification is performed to identify five drainage patterns: dendritic, fan-shaped, parallel, skeletal, and grid-like patterns. Experimental results show that the proposed method achieves an accuracy of 95.36% in recognizing the five drainage patterns, outperforming GCN, GraphSAGE, and the Transformer methods. The proposed method provides an approach to drainage network spatial structure representation, watershed classification, and hydrological process simulation.

    Summary of PhD Thesis
    Multi-scale forest above ground biomass estimation based on spaceborne, drone and terrestrial LiDAR data
    Baokun Feng
    2026, 55(9):  1706-1706.  doi:10.11947/j.AGCS.2026.20250242
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    Research on GNSS observation data quality intelligent assessment technology
    Mengyuan Li
    2026, 55(9):  1707-1707.  doi:10.11947/j.AGCS.2026.20250255
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    Modeling refinement of precise orbit determination for low Earth satellites based on GNSS
    Youcun Wang
    2026, 55(9):  1708-1708.  doi:10.11947/j.AGCS.2026.20250264
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    Ground- and space-based GNSS ionosphere inversion technique and its application in space weather monitoring
    Lei Liu
    2026, 55(9):  1709-1709.  doi:10.11947/j.AGCS.2026.20250265
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    Research on key technology of multi-source fusion autonomous positioning for unmanned underwater vehicles
    Kaidi Jin
    2026, 55(9):  1710-1710.  doi:10.11947/j.AGCS.2026.20250270
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