[Significance] Multimodal remote sensing image matching has become a fundamental task in integrated Earth observation, enabling precise spatial alignment across heterogeneous image sources. [Progress] As the diversity of sensing modalities, acquisition geometries, and temporal conditions increases, traditional matching frameworks have proven inadequate for capturing complex variations in radiometric responses, geometric configurations, and semantic representations. This technological gap has driven a significant paradigm shift from handcrafted feature engineering to deep learning-based solutions, which now form the core of current research and application development. This paper provides a comprehensive and structured review of recent advances in deep learning methods for multimodal remote sensing image matching, with an emphasis on the evolution of methodological paradigms and technical frameworks. It establishes a clear dual-path classification: the single-session approach and the end-to-end approach. The former selectively replaces or enhances individual components of traditional pipelines, such as feature encoding or similarity estimation, using neural network modules. The latter integrates the entire matching process into a unified network architecture, enabling joint optimization of feature learning, transformation modeling, and correspondence inference within a closed loop. This progression reflects the field's transition from modular adaptation to holistic modeling, revealing a deeper integration of data-driven representation learning with geometric reasoning. The review further examines the development of architectural strategies supporting this evolution, including attention mechanisms, graph-based structures, hierarchical feature fusion, and modality-bridging transformations. These innovations contribute to improved robustness, semantic consistency, and adaptability across diverse matching scenarios. Recent trends also demonstrate a growing reliance on pretrained vision foundation models, which provide transferable feature spaces and reduce the dependence on large-scale labeled datasets. In addition to summarizing technical advancements, the paper analyzes representative datasets, performance evaluation strategies, and the current challenges that constrain real-world deployment. These include limited data availability, weak cross-scene generalization, computational inefficiency, and insufficient interpretability. [Prospect] By synthesizing methodological progress with practical demands, the review identifies key directions for future research, including the design of modality-invariant representations, physically-informed neural architectures, and lightweight solutions tailored for scalable, real-time image registration in complex operational environments.

[Significance] Street View Image-based Visual Place Recognition (SV-VPR) is a geographical location recognition technology that relies on visual feature information. Its core task is to predict and accurately locate unknown locations by analyzing the visual features of street view images. This technology must overcome challenges such as appearance changes under different environmental conditions (e.g., lighting differences between day and night, seasonal variations) and viewpoint differences (e.g., perspective deviations between vehicle-mounted cameras and satellite images). Accurate recognition is achieved through calculating image feature similarity, applying geometric constraints, and related methods. As an interdisciplinary field of computer vision and geographic information science, SV-VPR is closely related to visual positioning, image retrieval, SLAM, and more. It has significant application value in areas such as UAV autonomous navigation, high-precision positioning for autonomous driving, construction of geographical boundaries in cyberspace, and integration of augmented reality environments. It is particularly advantageous in GPS-denied environments. [Analysis] This paper systematically reviews the research progress of visual location recognition based on street view images, covering the following aspects: First, the basic concepts and classifications of visual place recognition technologies are introduced. Second, the foundational principles and categorization methods specific to street view image-based visual place recognition are discussed in depth. Third, the key technologies in this field are analyzed in detail. Furthermore, relevant datasets for street view image-based visual place recognition are comprehensively reviewed. In addition, evaluation methods and index systems used in this domain are summarized. Finally, potential future research directions for SV-VPR are explored. [Purpose] This review aims to provide researchers with a systematic overview of the technological development trajectory of SV-VPR, helping them quickly understand the current research landscape. It also offers a comparative analysis of key technologies and evaluation methods to support algorithm selection, and identifies emerging challenges and potential breakthrough areas to inspire innovative research.

[Significance] Space is not merely a physical place, but a productive arena of social relations. Social phenomena are inherently endowed with spatial attributes, making the spatial perspective a critical pathway for understanding complex social issues. With the deepening "spatial turn" in the social sciences and continuous advancements in Geographic Information Systems (GIS)—particularly in data acquisition, spatial analysis and modeling, and spatial visualization—GIS has become an essential tool for addressing social issues. However, disciplinary differences in theoretical paradigms, methodological logic, and scale cognition between geography and the social sciences constrain their deeper integration. Existing literature lacks a systematic synthesis of integration trends, underlying challenges, and empowerment pathways, necessitating a comprehensive clarification of fusion mechanisms, core obstacles, and emerging opportunities. [Progress] This paper identifies five key advantages of GIS in empowering social science research: expanding spatial analytical thinking, supporting spatiotemporal data, enhancing survey techniques, enriching representational forms, and strengthening analytical capabilities. We review representative GIS applications in economics, political science, and sociology. From dimensions such as spatial cognition, data capacity, methodological adoption, and research hotspots, we distill application characteristics across these disciplines, revealing both commonalities and differences. While all three disciplines recognize spatial effects, their theoretical orientations shape distinct technical approaches—economics emphasizes causal identification, political science focuses on geopolitical structures, and sociology prioritizes contextual representation. Through a three-dimensional analysis—data, methodology, and cognition—we examine three major challenges in addressing social issues: the mismatch between data and research questions, the difficulty of integrating methods with causal mechanisms, and the contextual misalignment of place and scale, which reflect deeper issues of data suitability, methodological coherence, and the validity of spatial reasoning. [Prospects] The advancement of artificial intelligence, especially large models, injects new methodological momentum into GIS-based spatial analysis and brings threefold opportunities for addressing social issues. First, large models are driving spatial analysis from correlation-based description toward transparent causal inference; Second, multi-source data fusion and the generation of "silicon-based samples" help overcome the limitations of traditional survey data. Third, an emerging "space-survey" integrated framework is constructing a "spatial cognitive infrastructure" to support social research. Future efforts should establish a synergistic "large model-spatial analysis" paradigm that integrates these three opportunities. By simultaneously addressing challenges of data matching, method integration, and contextual misalignment, this paradigm can elevate GIS from a supportive tool to a core engine for theory generation and mechanism interpretation. This transformation will enhance the scientific value and practical effectiveness of GIS and spatial analysis in addressing complex social issues, fostering a bidirectional interaction between methodological innovation and theoretical advancement.

[Objectives] Street space serves as the primary perceptual interface for pedestrians in urban environments, and the visual quality of these spaces plays a crucial role in enhancing their vitality. Traditional evaluation methods often rely on single-objective indicators, making it difficult to effectively link objective environmental features with pedestrians' subjective perceptions. [Methods] This study proposes a novel evaluation framework based on Large Language Models (LLMs), incorporating the style dimension of subjective perception and extending traditional single-indicator quantitative analysis to a comprehensive approach that integrates both quantification and stylization. This framework utilizes Baidu Street View imagery to quantitatively assess two objective indicators, namely green view index and sky view factor, through semantic segmentation techniques. Additionally, it evaluates six subjective indicators, including vegetation diversity, building typology, building continuity, sidewalk usage, roadway usage, and signage usage, by leveraging prompt-optimized LLMs. The study then categorizes street space visual quality features within the research area using the Latent Dirichlet Allocation (LDA) topic model, aiming to explore the spatial characteristics of different streets and identify optimization strategies. [Results] Using Beijing's Xicheng District as the study area, the results reveal spatial distribution patterns of vegetation density and sky openness, along with pedestrians' subjective evaluations of indicators such as vegetation diversity and building type. Cluster analysis identified comprehensive service streets centered around Xidan North Street, characteristic streets centered around Xihuangchenggen South Street, and mixed-type streets centered around Lingjing Hutong. [Conclusions] This study innovatively introduces a large language model with human-like perceptual capabilities, enhancing its performance through prompt engineering. The resulting framework enables efficient and integrated evaluation of street visual quality by combining both objective and subjective factors. This approach provides a practical reference for large-scale, automated analysis of street view imagery.

[Significance] Cross-view geolocalization is the process of using a satellite image with coordinate metadata as reference to determine the geographic coordinates of an unknown ground-view image. This problem is often viewed as an image matching task, where an overhead satellite image is segmented into a number of square blocks of satellite patches, and the ground image is matched with candidate satellite patches to retrieve the most similar satellite patch, using the position of the center pixel in that patch as the query location. [Progress] With the development of cross-view geolocalization, the technique has been extended to fine-grained metric localization of ground imagery, i.e., identifying which image coordinates in a satellite patch correspond to a ground-measured location. Given that satellite images have global coverage and are easy to obtain, their application as reference images in image positioning has significantly broadened the application scope of image geolocation technology. This trend has prompted growing academic interest and attention to cross-view geolocalization research. Along with the development of various algorithmic techniques, cross-view geo-localization has evolved from the manual extraction of features, which was mainly based on the geometric features of buildings, to deep learning approaches that are applicable to richer scenarios, such as suburban and urban areas. The specific localization idea has progressed from the image-level cross-view localization, which uses the retrieval method to directly mark the retrieved center coordinate of the satellite image as the location of the ground image, to pixel-level fine-grained localization, which more accurately assigns the coordinates of the corresponding pixel location of the satellite image to the ground image. However, the drastic change in the viewing angle of ground and satellite images results in a huge difference in visual content, making cross-view image localization more challenging. To improve the accuracy of cross-view geo-localization, various scholars have made algorithmic improvements, such as representation learning and metric calculation. Additionally, for the huge viewpoint differences, some scholars study specialized geometric transformation, image generation, and other viewpoint conversion methods between cross-view images. Others improve localization accuracy with the help of directional information, intermediate viewpoint connection of UAV image information, and more. [Purpose] This paper summarizes the development process of cross-view geolocation, the different methods for improving accuracy, the various data sets involved, and the evaluation methods at different stages. On this basis, we discuss the future development trends and provide corresponding summaries.

[Objectives] Sustainable development is an important issue for countries worldwide, encompassing key aspects such as sustainable transportation systems and inclusive, sustainable urbanization. As a crucial component of urban public service infrastructure, the public transportation network serves as a cornerstone of a city's stable operation, with the distribution of its stops and routes directly influencing residents' travel patterns. However, existing studies mainly focus on accessibility analysis, site selection optimization, and spatial coupling with factors such as population and land use, while lacking in-depth optimization approaches and clear mechanisms that address spatial heterogeneity and facility redundancy. [Methods] Taking Beijing as a case study, with a focus on Dongcheng and Xicheng Districts, this study constructs a system of influencing factors based on multi-source data, including public transportation networks, topography, and economic indicators, and employs the XGBoost machine learning method to reveal the impact weights of these driving factors on the distribution of bus stops. On this basis, a mathematical model incorporating stop redundancy is proposed to optimize the spatial layout of upstream and downstream stops, producing a spatial optimization map of bus stops in Beijing. [Results] The findings indicate that: (1) There is an imbalance in the distribution of public transportation facilities in Beijing, with the proportion of the population having convenient access to public transportation differing by more than 30% between central and peripheral urban areas. (2) Among the 19 influencing factors, population density is the key driving factor, accounting for 27.77%, while the number of scenic spots and parking facilities have minimal impact, with feature importance scores below 0.5%. (3) Compared to the p-median model, the proposed redundancy optimization model significantly reduces the redundancy of optimized stops while maintaining performance in minimizing weighted distance. The optimized stop layout is more evenly distributed along existing bus routes. [Conclusions] These findings provide valuable reference and theoretical support for the layout of bus stops and other public service facilities, contributing to the efficient utilization of public resources and promoting sustainable urban development.

[Objectives] Global climate change, accelerating sea-level rise, and intensifying anthropogenic pressures are rendering the intricate human-land-sea nexus within coastal zones increasingly complex, sensitive, and vulnerable. This growing challenge underscores the urgent need for integrated coastal research frameworks capable of synthesizing environmental sensing, dynamic process simulation, and scenario projection. Addressing this critical gap, Digital Twin (DT) technology emerges as a transformative paradigm. By integrating multi-source data, sophisticated models, and domain knowledge into intelligent systems, DT offers unprecedented potential for creating precise virtual replicas and enabling intelligent management of complex coastal socio-ecological systems. [Analysis] This paper systematically analyzes the state of coastal zone digitalization, highlighting the pressing need for robust digital frameworks that can effectively represent and analyze the strong coupling between natural processes and human activities under multifaceted pressures. Building on this foundation, we propose a novel conceptual framework and implementation pathway for constructing a Digital Twin Coastal Zone (DTCZ). This framework explicitly positions land-sea interface processes as the foundational scenario and centers on human-land-sea feedback mechanisms as the core analytical thread. The proposed DTCZ system architecture is articulated across four pivotal dimensions: (1) Comprehensive information integration and knowledge aggregation; (2) Simulation of natural processes integrated with coupled human-nature decision support; (3) Synergistic short-term forecasting and long-term monitoring capabilities; and (4) Realistic multidimensional representation enabling intelligent interaction. We critically discuss the key technological enablers supporting this vision, encompassing coastal data governance and fusion, multi-scale scenario modeling, predictive analytics for critical coastal elements, persistent long-term monitoring strategies, and the development of the integrated DTCZ platform itself. At its core, the envisioned DTCZ leverages spatiotemporally fused multi-source data as its foundation and prioritizes enhanced scenario simulation and intervention capabilities. [Prospects] This framework is designed to overcome the limitations, such as fragmented data and limited predictive power, that constrain traditional coastal digital systems. By significantly advancing the computational tractability and overall manageability of coastal systems, the DTCZ paradigm offers a powerful new methodological tool and operational framework. It holds strong potential for supporting sustainable coastal development and modernizing governance structures in the face of ongoing climate change, providing a robust platform for evidence-based planning and adaptive management.

[Significance] With rapid advances in remote sensing, surveying and mapping, and autonomous driving technologies, 3D point cloud semantic segmentation, a core technology of digital twin systems, is attracting increasing research attention. Airborne point cloud semantic segmentation is regarded as a key technology for enhancing the automation and intelligence of 3D geographic information systems. [Analysis] Driven by deep learning and sensing technologies such as LiDAR, depth cameras, and 3D laser scanners, point cloud semantic segmentation can automatically classify and accurately recognize large-scale point cloud data through precise feature extraction and efficient model training. However, compared with typical high-density, category-balanced point cloud datasets (e.g., those used in indoor scenes, autonomous driving, or robotics), airborne point clouds present significant challenges in areas such as registration and feature extraction. These challenges stem from their unique characteristics, including large-scale 3D terrain coverage, dynamic platform motion errors, considerable variations in ground-object spatial scales, and complex occlusions. Currently, deep-learning-based airborne point cloud semantic segmentation is still in its early stages. Due to heterogeneous data acquisition methods, varying resolutions, and diverse attribute information, there remains a gap between existing research and practical algorithm deployment. [Progress] This paper provides a comprehensive review of the field, covering adaptive algorithms, datasets, performance metrics, and emerging methods along with their advantages and limitations. It also offers quantitative comparisons with existing technologies, evaluating representative methods in terms of precision and applicability. [Prospect] A thorough analysis suggests that breakthroughs in airborne point cloud semantic segmentation necessitate systematic research innovations across multiple dimensions, including feature representation, multimodal fusion, few-shot learning, algorithm interpretability, and large-scale model benchmarking. These advancements are essential not only for overcoming current bottlenecks in real-world applications but also for establishing robust technical foundations for critical use cases such as digital twin cities and disaster emergency response.

[Objectives] Map compilation involves professional operations such as element selection, symbolization, and notation configuration. However, the process is often complex and inefficient. Leveraging Large Language Models (LLMs), text-to-map technology significantly simplifies the mapping process, lowers the barrier to entry for non-experts, and improves mapping efficiency. Nevertheless, challenges remain, including heavy reliance on manual debugging and fragmentation tool invocation. [Methods] This paper proposes a DeepSeek-based method for constructing text-to-map agents, which automates the entire process from user input to visualization output. This is achieved through the decomposition of natural language instructions and autonomous adaptation of tools. Centered on the DeepSeek model, the approach associates cartographic elements with specialized tools and usage descriptions, analyzes module structures and collaboration mechanisms, and organizes tools into five categories. By interpreting user instructions and reasoning through task-oriented chains of thought, the agent invokes appropriate visualization tools to achieve cross-modal mapping from natural language to maps, enabling autonomous task reasoning and automated map generation. [Results] To evaluate the agent's effectiveness, two types of mapping tasks—based on local map data and online map services—were conducted using DeepSeek-V3-0324 and R1 models as decision-making cores. The experiments demonstrated that the agent could autonomously complete mapping tasks from natural language using both local and tile-based data. Local map visualization experiments confirmed the agent's ability to reuse tools effectively in low-complexity scenarios. Tile-based map visualization experiments indicated the agent's capability in handling high-complexity scenarios involving multi-toolchain invocations. It accurately decomposed subtasks, assigned appropriate tools, and performed structured string-based input variable transmission or direct invocation without variables, all presented to users in a semi-transparent manner. Across forty repeated experiments, the V3 model outperformed the R1 model, achieving 6.56 times greater execution efficiency with an average processing speed of approximately 6.29 seconds per step, and demonstrated better modular adaptability with the LangChain agent framework. [Conclusions] The proposed construction method validates the feasibility of using DeepSeek-based agents for intelligent cartography. The V3 model exhibits strong potential in this field, with its performance (6.29 s/step) comparable to that of professional cartographers. The text-to-map intelligent agent significantly reduces the entry barrier for map creation, promotes the broader adoption of mapping tools in everyday use, and provides a valuable technical reference for integrating autonomous cartography with professional software platforms such as ArcGIS and QGIS.

[Objectives] The trajectory knowledge graph effectively captures the deep semantic relationships between trajectories and geospatial entities, offering significant advantages in revealing complex associated information. However, traditional methods for constructing knowledge graphs from domain-specific data sources rely heavily on expert knowledge, involve extensive data preprocessing and entity-relationship extraction, and require high levels of professional expertise. [Methods] To address these challenges, this paper proposes a trajectory knowledge graph construction method that supports natural language-driven task execution through prompt learning with large language models. First, a prompt strategy for the preprocessing task is designed to guide large language models in automatically generating data processing code for cleaning abnormal trajectories. Second, a two-level system prompt strategy is developed to enable tool invocation by matching and calling the trajectory knowledge extraction tool. This strategy allows non-expert users to complete the graph construction process using simple natural language instructions, significantly reducing reliance on programming skills and deep semantic understanding. [Results] To evaluate the feasibility and effectiveness of the proposed prompt strategies, a set of test sentences was created for trajectory preprocessing and entity-relation extraction tasks. Real-world ship and vehicle trajectory datasets were used to support knowledge graph construction. Experiments conducted on two representative large language models, Tongyi Qianwen and Baidu Qianfan, achieved average accuracy rates exceeding 75% and 80%, respectively, demonstrating strong generalization ability and practical value. [Conclusions] This study verifies the effectiveness of combining large language models with prompt learning in constructing trajectory knowledge graphs with low technical barriers, demonstrating the strong generalization and application value of the proposed prompt strategy.

[Objectives] This paper tackles the challenges of small target detection, complex background handling, and dense target distribution in remote sensing imagery, proposing an advanced solution to enhance detection performance. The research aims to improve the accuracy and robustness of target detection in high-resolution remote sensing images by addressing limitations in existing methods, particularly in scenarios with intricate backgrounds and densely packed or small-sized targets. [Methods] Based on the YOLOv11 framework, this paper proposes an advanced remote sensing object detection method that effectively integrates multi-scale feature collaboration and scenario-aware mechanisms. To achieve superior performance, three novel modules are specifically designed: the Parallel Kernel Feature Fusion Module (PKFFM), which performs cross-scale feature integration through parallel convolution kernels to significantly enhance feature representation capability; the Cascaded Dual-Branch Attention Module (CDBAM), which sequentially emphasizes critical spatial and channel-wise information to refine feature extraction; and the Scenario-Aware Module (SAM), which enables the network to better capture and utilize global contextual information in complex remote sensing scenes. Furthermore, the RS-WIoU (Remote Sensing Wise Intersection over Union) loss function is introduced to address the challenges of high-resolution imagery and varying object scales, leading to more accurate bounding box regression and substantially improved overall detection performance. [Results] To comprehensively validate the effectiveness of the proposed method, extensive experiments are conducted on three widely recognized high-resolution remote sensing datasets: TGRS-HRRSD, NWPU VHR-10, and DOTA-v1.0. The experimental results demonstrate that the proposed approach achieves outstanding mean Precision (mP) of 97.3%, 87.3%, and 84.3% on the respective datasets, significantly surpassing the baseline YOLOv11 model with relative improvements of 2.1%, 3.8%, and 2.9%. In terms of the more comprehensive metric mAP_50-95, the proposed method further delivers gains of 3.0%, 1.2%, and 1.5% across the three datasets. Beyond superior accuracy, the model exhibits remarkable lightweight characteristics and strong robustness against complex backgrounds, varying scales, and dense object distributions, consistently outperforming other state-of-the-art remote sensing object detection algorithms. [Conclusions] The proposed method dramatically improves both precision and robustness in high-resolution remote sensing image object detection by synergistically combining the PKFFM, CDBAM, SAM, and RS-WIoU loss function, delivering a highly efficient and effective solution for real-world remote sensing applications. This collaborative framework enables better multi-scale feature fusion, enhanced channel and spatial attention, adaptive scenario understanding, and more accurate localization, leading to state-of-the-art results. Future work will focus on validating these modules on additional datasets and downstream tasks to further strengthen generalization performance and drive continued innovation in remote sensing technology.

[Significance] Urban digital twin models simulate comprehensive urban scenes by digitally mapping physical entities through real-time data integration. These models serve as visual, real-time representations of urban dynamics within smart cities, incorporating technologies such as the Internet of Things (IoT), spatial information systems, artificial intelligence, and others. Building on the Physical-Social-Information (PSI) three-dimensional framework, this paper reviews the current research progress of urban digital twin models and innovatively proposes a four-dimensional coupling framework: Physical-Social-Information-Time (PSIT). [Progress] The main research findings are as follows: (1) Since the introduction of digital twin technology into urban research in 2017, related literature has grown rapidly, with theoretical foundations and functional design frameworks gradually maturing. Urban digital twin models have initially been developed along three dimensions, PSI, including the digital mapping of geographic entities, spatial analysis of human activities, and the fusion and mining of geographic big data. (2) To more accurately reflect the real urban operations, current models require breakthroughs in data, technology, and algorithms. The PSI framework tends to overemphasize spatial features while oversimplifying the temporal dimension, lacking a representation of the spatiotemporal differentiation inherent in urban systems. (3) Recognizing the critical role of spatiotemporal coupling in urban modeling, this paper elevates time from a background variable to an independent dimension. This is based on the unidirectional nature of time, the temporal constraints on social behavior, the allometric time scales of urban element evolution, and the time-dependent mechanisms behind system phase transitions. Accordingly, the PSIT four-dimensional coupling framework is proposed to enhance the logic of urban system evolution and advance the theoretical paradigm of urban digital twin modeling. The CitySPS platform is presented as a case study for detailed illustration. [Prospect] The PSIT four-dimensional coupling framework offers the potential for more precise simulation and accurate prediction in digital urban spaces, representing a promising direction for future "intelligent" urban governance.

[Objectives] Change detection is a critical and challenging task in remote sensing image analysis, playing an increasingly important role in Earth observation. Although deep learning-based change detection techniques have achieved promising results, issues such as false detection and missed detection persist, especially in detailed and edge regions. [Methods] To address these challenges, this paper proposes a Multi-Scale Wavelet Transform Attention Network (WTANet) that integrates spatial-domain contextual information with frequency-domain high-frequency details. By leveraging complementary features from both spatial and frequency domains, and guiding the network through multi-scale feature differences, WTANet enhances the model’ s ability to perceive subtle changes from both global semantic and local detail perspectives. WTANet introduces the Detail Capture Wavelet Module (DCWM), which combines the frequency-domain properties of wavelet transforms with attention mechanisms to effectively extract coarse-to-fine information from remote sensing images. This helps recover high-frequency details typically lost due to convolution or pooling operations, thereby improving the network's capability to detect fine-grained changes. Additionally, the Feature Difference Enhancement Decoder (FDED) emphasizes differences between multi-scale features, enriching the feature representations and boosting the model’s performance in complex scenarios. [Results] Experimental results on three high-resolution remote sensing change detection datasets, CDD, LEVIR-CD, and S2Looking, demonstrate that WTANet achieves F1 scores of 97.52%, 91.24%, and 65.43%, respectively. Compared with representative change detection models such as SNUNet and BIT, WTANet exhibits superior performance in detail and edge detection. [Conclusions] The WTANet proposed in this study effectively improves the accuracy of remote sensing image change detection by integrating spatial and frequency domain information. This approach not only provides new insights for future research in remote sensing image analysis, but also offers valuable technical references for urban planning, environmental monitoring, and related fields.

[Objectives] Traditional optical remote sensing Change Detection （CD） methods involve cumbersome procedures and exhibit low automation levels. On the contrary, deep learning-based CD approaches possess hierarchical feature representation capabilities, as well as automatic learning of change patterns which facilitates to end-to-end CD. This significantly enhances the accuracy and automation levels of CD algorithms, establishing them as the mainstream solutions in the era of Remote Sensing (RS) big data. However, high-resolution remote sensing images are characterized by high spatiotemporal complexity of ground objects. Meanwhile, existing deep learning CD methods typically employ Siamese encoder architectures to extract multi-temporal image features and calculate feature differences to identify changes. This conventional approach easily leads to insufficient utilization of differential information, limited modeling capacity, and susceptibility to interference from complex backgrounds, shadows, and illumination variations. [Methods] To address the abovementioned limitations, this paper proposes a Multi-Scale Differential Feature Enhancement Network (MSDFENet) based on a fully convolutional architecture. MSDFENet employs a Siamese encoder architecture to extract multi-scale features from bi-temporal remote sensing images. By introducing an Asymmetric Partial Double Convolution (APDC) module, it reduces the number of parameters and minimizes redundant information. Furthermore, differential operations are utilized to extract differential features that capture multi-scale details of change information. During the decoding phase, a Multi-scale Feature Attention （MSFA） module is designed to achieve collaborative optimization of deep semantic features and shallow geometric features through the incorporation of a spatial coordinate attention mechanism. Finally, progressive up-sampling is applied to gradually restore fine-grained details of changed regions, and a simple convolutional layer is used to generate change map. [Results] To validate the effectiveness of this method, extensive experiments and analysis are conducted against mainstream deep learning CD methods by using the LEVIR-CD, CDD, and WHU-CD datasets. Quantitative results indicate that MSDFENet achieves optimal accuracy metrics across all three datasets, with F1-scores reaching 90.68%, 94.65%, and 91.64%, and IoU values attaining 82.96%, 89.78%, and 84.56%, respectively. Visual results demonstrate that MSDFENet effectively suppresses complex background interference and enhances edge localization accuracy, yielding superior visual performance. Model complexity analysis confirms that MSDFENet achieves an optimal balance between CD accuracy and computational efficiency. [Conclusions] The proposed MSDFENet is capable of significantly enhancing differential feature representation, effectively suppressing complex background noise interference, and substantially improving multi-scale change capture capabilities, thereby advancing CD performance.

[Objectives] Fine-grained object recognition in remote sensing is a fundamental yet highly challenging task within both Earth observation and computer vision. It involves the accurate localization and detailed classification of objects in High-Spatial-Resolution (HSR) imagery, which often features highly complex backgrounds, inter-class similarities, and intra-class variations. In recent years, notable progress has been driven by algorithms that jointly exploit pixel-level, object-level, and neighborhood-level information. These approaches combine semantic features, texture characteristics, and spatial contextual relationships to form multi-source and multi-scale feature representations. Despite these advances, existing methods remain inadequate for directly utilizing higher-level fine-grained knowledge such as scene composition, entity semantics, attribute descriptions, and temporal dynamics. The core limitation lies in the absence of a formalized knowledge organization and representation paradigm capable of systematically bridging low-level visual perception and higher-order semantic reasoning. [Methods] To address these limitations, this study proposes a multi-level knowledge graph-based organization and representation framework specifically designed for fine-grained remote sensing object recognition. The framework adopts a four-layer hierarchical structure encompassing scene, entity, feature, and change dimensions, enabling dynamic and semantically rich descriptions of remote sensing targets. In this structure, scene nodes provide contextual constraints, entity nodes capture essential connotations of objects, feature nodes encode visual and semantic attributes, and change nodes represent temporal evolution. [Results] By incorporating spatiotemporal references, spatial morphology, and inter-object relationships, the proposed approach enables knowledge organization under multiple constraints, including scene, entity, feature, and temporal conditions. In doing so, it moves beyond purely data-driven perception and establishes a mechanism for knowledge-driven reasoning in remote sensing interpretation. Extensive experiments were conducted to validate the effectiveness of the proposed framework. When integrated into the baseline model STD, the knowledge graph yielded an improvement of approximately 3.82% in mean Average Precision (mAP) and 3.92% in recall, demonstrating its ability to enhance detection accuracy. Beyond this single case, the universality and robustness of the framework were confirmed by consistent performance improvements across several representative neural networks, including Oriented R-CNN, Oriented RepPoints, LSKNet, and STD. These results indicate that the proposed method not only improves recognition performance but also enhances interpretability and adaptability across heterogeneous architectures and datasets. [Conclusions] Overall, this study demonstrates that a multi-level knowledge graph provides an effective pathway for advancing fine-grained object recognition in remote sensing, transitioning from feature perception to knowledge reasoning. The method not only increases recognition accuracy but also enhances semantic interpretability and dynamic adaptability, offering a scalable solution for intelligent remote sensing analysis. Importantly, it provides new theoretical and practical insights for applications in geospatial information extraction, environmental and urban monitoring, disaster assessment, and military intelligence analysis. By systematically integrating structured knowledge with data-driven models, the proposed framework enriches the semantic depth of remote sensing interpretation and demonstrates strong potential for future developments in intelligent Earth observation systems.

[Objectives] In the context of rapid urbanization and increasing population mobility in China, spatial variations in crime location choices among offenders with different household registrations (hukou) have drawn growing attention. However, most existing studies have focused on the differences between local and non-local offenders, without further differentiating among local offenders. This study aims to refine the classification of local offenders into two subgroups, those with matching hukou and residence and those with unmatched hukou and residence within the same city, to reveal differences in offending rates across different hukou types. It also examines the spatial distribution characteristics of their crime location choices and the underlying influencing factors. [Methods] Using ZG City in China as a case study, this research integrates offender data, census data, POI data, mobile phone signaling data, and remote sensing imagery to classify street theft offenders into three categories: local offenders with matching hukou and residence, local offenders with unmatched hukou and residence within the city, and non-local offenders. Kernel density estimation and a discrete choice model are used to analyze differences in crime location choices among these offenders types. [Results] The findings are as follows: (1) Offending rates are highest among non-locals, followed by local offenders with unmatched hukou and residence, and lowest among local offenders with matching hukou and residence. (2) Regarding journey-to-crime distance, non-locals travel the shortest distance, followed by unmatched locals, while matched local offenders travel the furthest. (3) All three offender types commit crimes in hotspots within the ring road, yet spatial patterns differ: matched local offenders tend to commit crimes in the old urban areas, unmatched local offenders are active in both old urban areas and Central Business Districts (CBDs), and non-local offenders mainly operate in CBDs. (4) Crime location preferences vary significantly by hukou types. A key finding is that the proportion of the migrant population has a positive effect on non-local offenders, no significant effect on unmatched locals, and a negative effect on matched local offenders. Matched local offenders tend to avoid neighborhoods with high rates of undergraduates and are more likely to commit crimes in urban villages, hospitals, and near bus stops. For unmatched local offenders, the elderly population proportion and proximity to factories have significant positive effects. For non-local offenders, elderly population proportion, urban villages, and factories all have significant positive effects. [Conclusions] By subdividing local offenders, this study reveals distinct spatial distribution patterns and influencing factors for street theft offenders with different hukou statuses. The findings provide valuable insights for optimizing urban security planning, enhancing law enforcement efficiency, and developing targeted crime prevention strategies.

[Significance] Permafrost monitoring represents one of the most critical application domains of Synthetic Aperture Radar Interferometry (InSAR) technology. In recent years, significant progress has been made in InSAR-based permafrost monitoring, driven by advancements in SAR satellite systems, the evolution of InSAR algorithms, and the integration of multi-source remote sensing technologies. These developments have established InSAR as a high-precision, large-scale technical approach for monitoring and assessing permafrost degradation under climate warming. [Progress] This paper systematically reviews recent innovations in InSAR-based permafrost monitoring and explores its interdisciplinary potential. First, we introduce commonly used Synthetic Aperture Radar (SAR) satellite systems, explain the fundamental principles and recent advancements in InSAR methodologies, and summarize both the geographical distribution of current permafrost study areas and quantitative trends in InSAR-related publications. Next, we comprehensively evaluate state-of-the-art applications of InSAR, including permafrost surface deformation monitoring, physically driven model construction, deformation prediction, and active layer thickness retrieval. Special emphasis is placed on addressing long-standing challenges such as interferometric decorrelation and imperfect permafrost parameter modeling through the integration of thermal-optical remote sensing data and hydrological models. [Prospect] In alignment with the sustainable development needs of permafrost regions, we analyze emerging research trends in InSAR permafrost studies, particularly in deep learning, multi-source data fusion, and sustainability assessment. This review not only provides a methodological framework and roadmap for addressing key challenges in InSAR-based permafrost research but also lays a technical foundation for tackling critical issues such as Arctic engineering safety and ecosystem stability evaluation.

[Significance] The 3D real-scene models are evolving from merely "representing reality" in the past to "connecting reality, cognizing reality, and even foreseeing reality" in the present. With the construction of the national 3D real scene of China, the integrated application paradigm of "3D Real-Scene +" has applied in numerous industries. As a critical part of national new infrastructure, 3D real-scene models hold significant value for scientific research in areas such as geospatial cognition, virtual geographic environments, and spatiotemporal computing, as well as for national strategies and societal needs such as the "Digital China" and the "Digital economy". [Analysis] This paper provides a systematic review of the conceptual framework, core methodology, application scenarios, and challenges of 3D real-scene models. First, this paper systematically reviews and synthesizes the concepts, connotations, and main types of 3D real-scene models, proposing the concepts of primary 3D real-scene models and secondary 3D real-scene models to facilitate their in-depth application. The research scope of 3D real-scene models is summarized across five levels: the data layer, model layer, platform layer, application layer, and theory-standard layer. Second, this paper comprehensively outlines the core methodology currently from the perspective of primary 3D real-scene modeling, secondary 3D real-scene modeling, and model optimization and assessment. The state-of-the-art techniques in 3D real-scene and their development directions were systematically summarized as well. Next, this paper systematically reviews the application scenarios of 3D real-scene models. The application scenarios were summarized into four dimensions of spatio-temporal base, information extraction, connecting reality, and industry empowerment. Industrial empowerment is the core purpose of 3D real scene models. The industrial empowerment can be achieved through four paths: business data visualization and fusion in 3D real scene models, business decision-making enhancement by 3D analysis, process business reengineering by online 3D platform, and AI agent decision-making. Finally, the main issues and challenges currently facing the 3D real-scene models are summarized from the perspectives of the paradigm transformation of geographic information cognitive, spatiotemporal dynamic modeling of complex scene, intelligent transformation, and in-depth application in industries. Additionally, the future development directions of 3D real-scene models are prospected. [Purpose] This paper aims to provide a comprehensive academic perspective and a systematic overview of technological developments for related research, assisting researchers and technical professionals in quickly grasping the up-to-date trends in the field of 3D real-scene models. It serves as a reference for technology R&D and for the integrated application of 3D real-scene models in industries, while also identifying potential breakthrough directions such as AI-enabled real-scene 3D construction, thereby inspiring innovative research and practical ideas.

[Objectives] Unsupervised change detection is a research hotspot in Synthetic Aperture Radar (SAR) image information extraction. However, existing studies often rely on single-method pseudo-label generation, leading to limited reliability. Moreover, most current methods mainly utilize spatial-domain features of multi-temporal images, with relatively few explorations into the fusion and utilization of spatial-frequency dual-domain features. To address these challenges, this study proposes a Mamba-based spatial-frequency feature fusion U-Net model for unsupervised SAR change detection. [Methods] The proposed approach first uses a difference segmentation-clustering fusion approach to generate high-quality pseudo-label samples, reducing dependence on manually labeled sample data. Next, a spatial-frequency dual-domain feature fusion U-Net model, integrating Mamba and wavelet convolution, is constructed to extract change information. Mamba is employed to efficiently capture global features, which are then fused with local spatial features extracted by convolutional networks. Simultaneously, wavelet convolution is used to enhance frequency-domain feature extraction. The fusion of dual-domain features is performed during the upsampling stage of the U-Net architecture. [Results] To validate the effectiveness of the proposed method, experiments were conducted on two SAR image datasets. Both qualitative and quantitative comparisons were made with traditional and deep learning-based methods. Compared to the best-performing baseline, the proposed method improved the average F1_Score by 2.35% and the Kappa coefficient by 2.65% across the two datasets, significantly enhancing the reliability of change detection results. [Conclusions] The proposed method effectively improves the automation and reliability of SAR image change detection, offering strong technical support for applications such as environmental monitoring, urban expansion analysis, and disaster assessment.

[Objectives] This study aims to investigate the spatiotemporal evolution characteristics and driving mechanisms of landslide evolution in the Three Gorges Reservoir Area (TGRA) under compound extreme weather events in 2022, characterized by severe drought in the Yangtze River Basin and localized heavy rainfall within the reservoir area. It also seeks to address the knowledge gap in understanding landslide evolution under extreme climatic conditions. [Methods] Focusing on the Zigui-Fengjie section, this study utilized Sentinel-1 SAR data and the Small Baseline Subset (SBAS) InSAR technique to monitor surface deformation and identify active landslides. A dynamic evaluation of landslide susceptibility was conducted combining the information value model and SBAS-InSAR results. Based on reservoir water level fluctuations, the study period was divided into two intervals: normal weather (July 2020 to July 2022) and extreme weather (July 2022 to September 2023), to comparatively analyze landslide evolution patterns and driving mechanisms. [Results] The results are as follows: (1) A total of 136 active landslides were identified. The most favorable geomorphic conditions for landslide development included slopes of 10°-30°, southeasterly to northwesterly orientations, elevations of 100~400 m, distances to rivers less than 200 m, and distributions mainly in clastic and mixed clastic-carbonate rock areas. Many landslides were located in rainfed farmland within 100 m of roads. (2) The combination of InSAR technology with traditional landslide susceptibility assessment models enabled dynamic assessment. The method can be updated synchronously with InSAR deformation data to reflect the current state of landslide evolution in a timely manner. (3) Under extreme weather conditions, landslide risks in the study area increased significantly, while the relatively low water level of the Three Gorges Reservoir had no significant negative impact on reservoir bank stability. (4) Precipitation was identified as the primary driver of dynamic landslide susceptibility evolution, with the susceptibility-precipitation response varying considerably across regions with different lithologies. [Conclusions] By integrating SBAS-InSAR time-series deformation monitoring with the information value model, this study reveals the spatiotemporal variability of landslide risk under extreme weather conditions. It addresses critical gaps in understanding landslide evolution mechanisms in the TGRA and provides a scientific foundation for landslide monitoring, early warning, and risk management.

[Objectives] To address the limitation that existing trajectory anomaly detection methods often fail to fully consider road network constraints, this study proposes a trajectory anomaly detection algorithm designed to effectively identify potential fraudulent behavior by taxi drivers during passenger pickup. [Methods] The algorithm first performs map matching, aligning trajectory data with the actual road network to obtain a sequence of path segments. Then, a two-stage clustering approach is applied: the matched trajectory paths are initially clustered to extract and expand core road segments, forming multiple core paths. Next, the algorithm calculates the similarity between different core paths and assigns highly similar paths to the same cluster, thereby generating multiple path clusters. Finally, a CostThreshold is computed based on each path cluster. The travel cost of each trajectory, calculated by combining travel time and distance costs, is compared against the corresponding CostThreshold to determine whether the trajectory is anomalous. [Results] Compared with traditional anomaly detection methods on real-world trajectory datasets, the proposed approach demonstrates superior performance in detecting anomalous trajectories. It achieves significantly lower runtime and improves detection accuracy by up to 9.03% compared to the STADCS method. The F₁ score also improves considerably compared to the Two-Phase and ATDC methods, with maximum gains of 6.67% and 9.45%, respectively. [Conclusions] This paper presents a detection method that integrates road network constraints with two-stage clustering and travel cost evaluation. The method enhances detection accuracy and efficiency while reducing the false positive rate. It is well-suited for complex urban road networks, offering valuable support for vehicle trajectory data mining and traffic management decision-making, with significant practical value in fraud detection and related fields.

[Objectives] Soil salinity is one of the major and widespread challenges in the recent era, hindering global food security and environmental sustainability. Accurate evaluation and analysis of soil salinization are of great significance for the improvement and management of soil salinization. [Methods] To address the challenge of mapping three-dimensional spatial distribution of soil salinity, this study selected 819 effective field soil samples within a saline soil region of the Yellow River Delta. These samples, which have vertical stratifications from 0 to 100cm, were used for comprehensive analysis. The soil sample points were arranged in a grid of 5 km×5 km horizontally, and the sampling soil layer was set up every 10cm vertically. Following the principle of covering different land cover types and human accessibility, soil samples were collected from the depth range of 0~100 cm in the study area. The three-dimensional spatial differentiation of soil salinity in the coastal saline soil area was revealed from different perspectives using traditional geostatistical methods and 3D Empirical Bayesian Kriging interpolation. The effects of various factors on the spatial differentiation of soil salinity were analyzed using the Geodetector method. [Results] The results showed that the spatial distribution of soil salinity in the whole soil range and different vertical layers were highly variable. There were differences in the scale of spatial autocorrelation of soil salt content at different depths. In this study, the 3D Empirical Bayesian Kriging interpolation method was established to spatialize the soil salinity of soil samples, which effectively revealed the vertical fine-scale three-dimensional spatial characteristics of soil salinity. Soil salinity exhibited significant three-dimensional spatial differentiation, with diverse profile distribution types. The main types were homogeneous and surface aggregated, with some local areas showing bottom aggregated and fluctuating types. All influencing factors significantly affected the three-dimensional spatial differentiation of soil salinity, but the degree of influence varied for each factor. The order of explanatory power of each influencing factor is as follows: land use/land cover > distance to coastline > groundwater depth > groundwater conductivity > elevation > land surface temperature > soil bulk density > soil clay content. Compared with single factors, the pairwise interaction of any factor had a greater effect on the spatial differentiation of soil salinity, but the interaction strength of different factors varied. In the whole 0~100 cm soil depth range, GWD ∩ LULC had the largest impact (0.443), followed by LST ∩ LULC (0.326). [Conclusions] Although the q values of land surface temperature and soil bulk density were not high, their explanatory power on soil salinity was greatly improved after their interaction with land use/cover, better explaining the changes of soil salinity in the study area. Factors such as land use/cover, groundwater depth, surface temperature, and soil bulk density are closely related to the spatial distribution of soil salinity in the study area. The research results provide a theoretical basis and technical support for the formulation of comprehensive improvement measures and management systems for fine-scale saline-alkali land in the region. These findings have positive implications for promoting the achievement of the Sustainable Development Goal of Land Degradation Neutrality in coastal areas.

[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] Road object detection is a significant application of LiDAR (Light Detection and Ranging) point clouds. As a rapidly developing mapping system, LiDAR can quickly collect point cloud data of objects' surface in road environments. Currently, many detection methods based on LiDAR point clouds perform well for large objects, close-range objects, or objects in simple scenes, accurately detecting objects in such conditions. However, due to sensor limitations during the acquisition process, LiDAR point clouds are often sparse and lack the texture information present in RGB images. This limitation leads to high false detection and omission rates when dealing with small objects, long-distance objects, or objects in complex road environments. The rich texture information in RGB images can compensate for these deficiencies in point clouds, making it necessary to explore road object detection technologies based on the fusion of RGB images and LiDAR point clouds. [Methods] To improve road object detection in complex scenes, this paper uses PointRCNN as the baseline network and proposes a two-branch multi-stage fusion detection network, named EPG2LFusion, based on RGB images and LiDAR point clouds. The network introduces two key innovations: Firstly, to address the limitation of existing convolutional neural networks in extracting image features due to receptive field constraints, a convolutional module named WaveDSConv is designed in the image branch. This module combines wavelet transform convolution and depth separable convolution to enhance global image feature extraction, thereby improving the performance of fused object detection. Secondly, to overcome challenges in fusing the two different modalities of point clouds and images, a fusion module G2L-Fusion is proposed. This module establishes accurate point-pixel correspondence between point clouds and images using a projection matrix and effectively employs a channel attention mechanism to fuse global information and local information across multiple stages. [Results] The proposed method was evaluated on the KITTI benchmark dataset for road object detection across multiple categories (cars, pedestrians, cyclists). Results indicate that the proposed method achieves an average detection accuracy of 65.21% for all categories on the KITTI test set, which is 4.88% higher than the baseline network. For the challenging pedestrian category under medium difficulty, an average detection accuracy of 45.86% was achieved, demonstrating competitive performance compared to existing advanced algorithms. [Conclusions] The results confirm that the proposed algorithm leverages the rich texture features of RGB images to address the sparsity of LiDAR point clouds, significantly improving the detection accuracy of common road objects.

[Significance] As a critical pathway to achieving Sustainable Development Goal (SDG) 2, “Zero Hunger,” and ensuring long-term ecological sustainability, the concept and practice of sustainable agriculture are undergoing a paradigm shift toward data-driven and system-oriented approaches. In recent years, Big Earth Data—comprising remote sensing, geospatial, meteorological, and agricultural Internet of Things (IoT) data—has emerged as a foundational driver for agricultural monitoring, decision support, and technological innovation in sustainable development. [Analysis] Given the interdisciplinary, multi-stakeholder, cross-regional, and evolving-goal nature of sustainable agriculture, this study begins by systematically reviewing the conceptual evolution of the term. It highlights the multidimensional implications and diverse practical pathways of sustainable agriculture, noting its growing role as a core component of global development strategies. On this basis, the paper proposes a new, data-oriented and operational interpretation of sustainable agriculture. The study then establishes an analytical framework—“data-Technology”—to clarify the pivotal role of Big Earth Data in supporting sustainable agriculture. It examines the evolution of core datasets and key technical methods across three periods: before 2015, 2015—2019, and 2020 to the present. The applications reviewed include agricultural resource monitoring, multi-scale crop condition assessment, and evaluations of agriculture's environmental impacts. The findings suggest that sustainable agriculture, enabled by Big Earth Data, is rapidly shifting from a paradigm of "observational analysis" to one of "intelligent decision-making." Furthermore, the study conducts a comparative assessment of China, the United States, and the European Union across four critical dimensions: data infrastructure; technological advancement and application; scientific research capacity; and policy support. While China has made significant progress in all four areas—with strengths in remote sensing capabilities, rapid technological rollout and demonstration, substantial research output, and clearly defined policy directives—it continues to face challenges in data ecosystem development, original algorithm innovation, commercialization of scientific outputs, and the alignment of standards and incentive mechanisms. Finally, in light of the current needs for sustainable agricultural development, this study systematically analyzes the major challenges facing Big Earth Data from four aspects: data acquisition capacity, intelligent processing methods, application promotion and services, and data governance and ethical security. In response, it proposes multi-level strategies covering standardization, model optimization, improvements to service systems, and protection of data rights, with the aim of providing a reference pathway for the efficient utilization and sustainable development of agricultural big data in the future. [Prospect] The article aims to analyze the data-driven transformation pathway of sustainable agriculture and provide a systematic reference for its green, inclusive, and intelligent development.

[Objectives] Multiple Unmanned Aerial Vehicles (UAVs) path planning is a pivotal technology enabling efficient and cooperative operation of UAV swarms in complex environments. Fundamentally, it constitutes a constrained multi-objective optimization problem characterized by a sparse feasible region. Due to their robust global search capabilities, evolutionary algorithms have been widely adopted to address this class of problems. However, existing methodologies frequently neglect the spatial structural attributes of paths during the initialization phase and primarily rely on generic stochastic search operators for path regeneration. These limitations restrict their ability to effectively exploit the fitness function and the spatial relationships between UAVs and their operational environment, thereby hindering the generation of high-quality, globally feasible solutions under constrained computational resources. [Methods] To address these challenges, this paper introduces a Multi-Source Heuristic Evolutionary Algorithm (MSHEA) for multiple UAV path planning. MSHEA systematically integrates multi-source heuristic information related to spatial path structure, fitness data, and environmental context to enhance both the path initialization and regeneration processes. Specifically, we propose a Sequential Directed Expansion-based Initialization (SDEI) strategy to generate high-quality initial paths that exhibit spatial rationality and structural diversity. Furthermore, we develop a path regeneration mechanism integrating fitness and Flight Environment Information (FFEI), which substantially improves the repair efficiency of infeasible paths and the local optimization of feasible ones. [Results] Empirical validation was conducted using eight publicly available benchmark datasets for multiple UAV path planning, covering a range of complexity levels. The experimental results demonstrate MSHEA's superior performance and elevated stability across diverse flight scenarios: (1) Compared to the suboptimal benchmark algorithm, MSHEA achieved a 1%~6% improvement in hypervolume and a 6%~81% reduction in inverted generational distance. (2) Both the SDEI and FFEI components were confirmed to have significant positive impacts on the algorithm's overall performance. (3) MSHEA shows low sensitivity to its newly introduced hyperparameters, indicating strong adaptability and generalization capability. [Conclusions] In conclusion, MSHEA improves the effectiveness of solving multiple UAV path planning problems by incorporating multi-source heuristic information. This leads to robust and reliable performance for the challenges inherent in collaborative UAV missions.

[Objectives] Mineral resources are a vital material foundation for human survival and economic development. Conducting mine monitoring and establishing monitoring models are essential for the efficient utilization of mineral resources and environmental protection in mining areas. Given the complex backgrounds, diverse target scales, and dense distribution of small targets in open-pit mining areas, this study aims to develop a lightweight model that balances monitoring accuracy and efficiency, thereby improving the recognition of target objects in such environments. [Methods] Existing remote sensing datasets often suffer from limitations such as low sample diversity and regional constraints. To address this, we construct the OMTSFD (Open-pit Mine Typical Surface Features Dataset) based on 0.9 m TianDiTu and 1.8 m Google imagery. The dataset covers various climate backgrounds, large areas, and a wide range of surface features. For model training and validation, we propose an improved YOLO11-DAE algorithm. First, the C3K2-DBB module is integrated into both the backbone and feature pyramid networks to enhance multi-scale feature extraction. Second, the ADown module replaces traditional downsampling convolution layers, improving the representation of diverse features and reducing detail loss in low-contrast scenes. Finally, the E_Detect efficient detection head is introduced to reduce model complexity and the number of parameters, contributing to overall model lightweighting. [Results] Experimental results show that YOLO11-DAE achieves an FPS of 528.100, indicating high inference speed. The model achieves a precision (P) of 0.932, recall (R) of 0.894, F1-score of 0.913, and mean average precision (mAP) of 0.950, significantly outperforming YOLOv5n, YOLOv8n, and YOLOv10n algorithms. Compared to YOLOv11n, the proposed method improves performance by 7.600%, 10.000%, 8.800%, and 8.000% in the respective metrics. [Conclusions] The YOLO11-DAE algorithm meets real-time monitoring requirements in mining areas and is well-suited for complex scenarios involving multi-scale and multi-background targets. It achieves high-precision detection with a low miss rate and strikes an effective balance between model applicability and real-time performance.

[Objectives] Addressing challenges in data acquisition and the limited quantitative dimensions in evaluating the spatial quality of historic district streets, this study proposes a quantifiable assessment method for street spatial quality, using Yuhou Street in Chenzhou City as a case study. [Methods] A 17-indicator evaluation model is constructed, incorporating cultural aspects across three dimensions: pedestrian accessibility, safety and convenience, and visual comfort. To overcome data collection difficulties in narrow streets and incomplete coverage from mapping platforms, the study employs self-collected street view images—manually simulating tourist walking paths to capture street scenes—ensuring applicability to narrow sample streets. Semantic segmentation using a Fully Convolutional Neural network (FCN) and spatial visualization in ArcGIS are applied to achieve a quantifiable assessment of street spatial quality. [Results] ① Self-collected street view images effectively cover narrow alleys and provide a pedestrian-perspective view, meeting experimental data requirements. ② The combination of street view images, semantic segmentation, and data visualization enables precise localization of street indicators and spatial quality grading. The analysis reveals significant gradient distribution and spatial heterogeneity in street quality across the case study area. ③ This method establishes a complete technical framework from street view data collection to spatial quality evaluation, enabling multidimensional quantitative analysis and visual representation of street environments. It provides data support and decision-making references for the refined renewal of historic districts. [Conclusions] ① This study innovatively integrates manual streetscape collection into a "data collection—semantic segmentation—spatial quantification" technical pathway for historical districts, effectively addressing data collection challenges in narrow streets and providing a scalable technical solution for small-scale built environment research. ② The evaluation system constructed for historical and cultural districts incorporates soft indicators such as "cultural visual recognition" and "proportion of traditional businesses" into a quantitative framework, offering methodological reference and renewal strategies to enhance the quality of similar districts.

[Objectives] With the rapid advancement of information technology, the volume of data on the internet has grown exponentially, making it increasingly difficult for traditional information retrieval methods to effectively access key information and uncover the underlying associations among data. In this context, knowledge graphs have emerged as a powerful technology for organizing and managing complex information. By providing structured representation, semantic association, and logical reasoning capabilities, knowledge graphs effectively address the problems of information fragmentation and structural disorganization, thereby offering a solid foundation for knowledge discovery, intelligent inference, and decision support. However, as a crucial component of knowledge graph construction, information extraction continues to face numerous challenges in practical applications. These challenges are particularly pronounced in specialized domains such as gold mining, where overlapping entities, nested structures, and complex relational patterns are common. Moreover, the lack of efficient and automated construction pipelines further exacerbates the complexity and inefficiency of building domain-specific knowledge graphs. [Methods] To address these issues, this study proposes an information extraction approach based on the PFNA (Partition Filter Network with Attention mechanism) model, specifically designed for entity and relation extraction tasks in the gold mining domain. The proposed model incorporates an attention mechanism to dynamically weight input features, thereby enhancing its ability to capture complex entities and semantic relationships. Additionally, by integrating a domain-specific word embedding enhancement strategy, the model improves its capability to identify and represent specialized terminology and intricate patterns within the domain. [Results] Experimental results on a gold mining dataset demonstrate the superiority of the proposed method. The model achieves an F1 score improvement of 6.50% to 50.42% for complex entity recognition and 13.15% to 58.54% for complex relation extraction, significantly outperforming several mainstream baseline models. These results strongly validate the effectiveness and advancement of the proposed method in the context of knowledge extraction in the gold mining field. [Conclusions] Finally, by leveraging the Neo4j graph database for entity and relation storage and integrating a visualization interface, the constructed knowledge graph system is successfully deployed. This system provides structured and systematic knowledge support for intelligent decision-making, resource management, and domain-specific knowledge services in the gold mining industry.