[Objective] Land surface temperature(LST) variations significantly affect surface water resource distribution, particularly in heavily dependent on these resources. Egypt, located in a tropical desert climate, is highly dependent on its surface water resources, primarily supplied by the Nile river. Therefore, accurate surface temperature inversion is crucial for formulating long-term water resource response strategies in the country. This study aims to: (1) investigate the impact of LST on surface water distribution in Egypt, (2) enhance single-channel algorithm accuracy for LST inversion, and (3) establish an inversely proportional functional model linking LST to surface water distribution.
[Method] Due to limited temperature measurement data in Egypt, this study employed a combination of variable normalized difference vegetation index (NDVI) thresholds and high-precision atmospheric water vapor content data to calculate a high-precision surface temperature dataset for Egypt using the Google Earth Engine (GEE) platform. By creating buffer zones at varying distances from the Nile River, quantitative characteristics of LST in riverine and surrounding areas were analyzed. A mathematical model was developed by assigning weighted coefficients to water resource distribution patterns, enabling analysis of the relationship between LST and surface water dynamics in Egypt.
[Result] LST inversion results, validated against Landsat 8 temperature products, achieved root mean square errors (RMSE) below 1.0 for two study areas—exceeding World Meteorological Organization (WMO) accuracy standards—and below 1.5 for the third. High linear fit accuracy was observed between inverted LST and meteorological station measurements (R2 = 0.828), closely matching historical long-term correlation (R2 = 0.849). Analysis revealed: (1) In water-abundant regions, LST reduction effects diminished rapidly with distance from water bodies; (2) In sparse, braided water distribution areas, LST effects decreased gradually. The inversely weighted proportional function model demonstrated strong correlation (R2 = 0.96) in regions with significant surface water resources.
[Conclusion] This research validates the efficacy of variable NDVI thresholds and atmospheric water vapor data for LST inversion in tropical desert regions. The inversely weighted proportional model provides a quantitative framework for understanding LST-water resource interactions in Egypt, offering critical insights for water management in arid regions. The model is broadly applicable to mid-low-latitude deserts facing water scarcity. Future work will refine NDVI threshold optimization (e.g., via genetic algorithms), integrate higher-resolution LST data, advance surface water distribution modeling, and employ machine learning to enhance model precision.

[Objective] Traditional indoor measurement methods exhibit significant limitations in complex and confined spaces. In intricate indoor layouts, their precision often suffers, and data collection can be incomplete. For instance, in areas with complex furniture arrangements or narrow corridors, obtaining accurate and comprehensive data becomes challenging. Similarly, in tight spaces like ducts or between closely spaced shelves, the operation of traditional measuring tools is restricted, leading to potential data omissions. This research aims to develop an indoor UAV-based autonomous exploration and mapping method integrated with a software-hardware system. The goal is to achieve highly accurate indoor mapping without relying on GNSS signals, which is essential for applications such as indoor navigation, facility inspection, and emergency response in buildings. By doing so, it seeks to provide effective solutions for various indoor-related tasks and enhance the overall efficiency and accuracy of indoor spatial data acquisition.
[Method] This method is founded on the information gain maximization strategy and the Euclidean signed distance field (ESDF) map. Initially, exploration points are evenly distributed across the map to ensure comprehensive coverage. Data from the LiDAR and inertial measurement unit (IMU) are then fused. During this process, distortions in the LiDAR data are corrected to ensure accuracy. The IMU data is pre-integrated to predict the UAV's pose changes between LiDAR scans. Factor graph optimization is employed to incorporate LiDAR odometry constraints, IMU pre-integration results, and environmental constraints, effectively reducing drift in parameters like inertial navigation biases. The UAV's hardware includes a semi-solid-state LiDAR for high-resolution environmental perception and a flight controller for stable flight management. The ROS-based software features algorithms for precise positioning, real-time dynamic obstacle avoidance, and optimal path planning, enabling the UAV to navigate and explore the indoor environment autonomously.
[Result] Simulation results demonstrate notable improvements. Compared to the FUEL algorithm, the proposed method reduces exploration time by an average of 13.28%, indicating a more efficient exploration process. Additionally, the trajectory length is decreased by 14.89%, suggesting less redundant movement. In a basement corridor field experiment, point cloud measurements show a maximum deviation of 4.87 cm, a minimum of 0.37 cm, and a standard deviation of 2.66 cm. These results meet the high-precision requirements for indoor mapping. Furthermore, the UAV efficiently explores abandoned train tunnels, demonstrating its adaptability to complex and challenging environments.
[Conclusion] The developed UAV system has proven its capability to explore autonomously in indoor environments without GNSS signals. However, in highly complex and dynamic indoor scenarios, such as crowded areas with constantly moving objects, preset exploration points may face challenges. For example, new obstacles can render preset points ineffective, reducing exploration efficiency. Future research will focus on optimizing the algorithm, potentially integrating more advanced sensor fusion techniques and intelligent decision-making algorithms. This enhancement will improve the UAV's adaptability in complex environments and further advance the development of indoor mapping technology.

[Objective] Lake Victoria, the largest freshwater lake in Africa, plays a critical role in regional water resource management, flood preparedness, and ecological conservation across the Nile River Basin. However, conventional methods relying on optical remote sensing are severely limited by persistent cloud cover and heavy rainfall in the tropical climate, resulting in fragmented temporal observations long-term monitoring consistency. These atmospheric conditions often lead to discontinuities in temporal observations, limiting the ability to conduct consistent and reliable long-term monitoring. Therefore, there is an urgent need for a more stable, weather-independent, and temporally consistent method for large-scale inland water body mapping.
[Method] We advanced water body extraction techniques by exploiting the all-weather imaging capability of Sentinel-1 SAR datasets. A rigorous comparative analysis identified an optimal SAR-based water index, which was coupled with an improved Edge-Otsu algorithm to replace conventional methods that rely on manually set initial thresholds. This innovation enables adaptive, automated water body segmentation, minimizing subjectivity and enhancing methodological reproducibility. The framework was applied to generate a 10-meter resolution monthly water surface area dataset for lake Victoria from 2017 to 2023. This dataset facilitated a comprehensive analysis of the spatiotemporal dynamics of lake Victoria.
[Result] The proposed method demonstrated exceptional stability and automation across multi-temporal SAR imagery, achieving an overall water extraction accuracy of 98.9%. Compared to global products such as the JRC Global Surface Water (GSW) dataset (R = 0.1) and Dynamic World (R = 0.29), our dataset exhibited a significantly higher correlation of 0.76 with in situ water level records, reflecting superior temporal consistency and reliability. Notably, the algorithm captured dynamic water boundary fluctuations without manual intervention, outperforming traditional threshold-dependent approaches.
[Conclusion] The high-resolution dataset revealed distinct temporal and spatial patterns in lake Victoria's surface area. From 2017 to 2022, the lake exhibited a gradual expansion trend, followed by a minor contraction in 2023. More specifically, water surface area increased rapidly during the long rainy season (March to May), typically peaking in June, then gradually receding through the dry season (July to September). Spatial heterogeneity was most evident in the northeastern and southern basins, particularly in Kenya's Winam Gulf and Tanzania's Mwanza Gulf. This study provides comprehensive and long-term monitoring of the dynamic changes in lake Victoria's water surface area, offering a solid scientific basis for transboundary water resource management. It plays a vital role in supporting ecological conservation and promoting regional sustainable development. Furthermore, the SAR-based methodology pioneered here is transferable to other tropical regions, supporting hydrological modeling, climate adaptation strategies, and cross-border environmental management initiatives.

[Objective] Over the past three decades, the expansion of arable land and the increase in agricultural water consumption in the Xinjiang plain region have led to the over-exploitation of groundwater. This has consequently resulted in a range of issues, including but not limited to declining groundwater levels, reduced runoff, deteriorating water quality, land subsidence, and ecological degradation. The mechanism underlying changes in groundwater storage in the Xinjiang plain is complex. Both domestically and internationally, there have been relatively few studies on large-scale groundwater storage changes in this area. Therefore, it is essential to employ modern observational technology to objectively study the information on groundwater storage changes in this region.
[Method] Investigating the spatial and temporal evolution of groundwater reserves in the Xinjiang plain region is crucial for ensuring the security of regional water resources, food supply, and ecological health. By utilizing GRACE satellite and GLDAS model data, which are well-known for their high accuracy and reliability in hydrological research, we estimated the alteration in groundwater storage in the Xinjiang plain from 2003 to 2022. Subsequently, we analyzed the spatial and temporal evolution of groundwater storage using methods such as the Theil-Sen slope, Mann-Kendall trend test, and grading of groundwater storage changes. This comprehensive analysis provides an understanding of the groundwater dynamics in the region.
[Result] The results indicate that: (1) From 2003 to 2022, the overall groundwater reserves in the Xinjiang plain region exhibit a decreasing trend in four stages, with average monthly change trends of -0.58 mm/mon, -0.26 mm/mon, -0.11 mm/mon, and -1.69 mm/mon for the periods of 2003-2008, 2009-2013, 2014-2018, and 2019-2022, respectively. (2) There are irregular spatial changes; groundwater storage in the pre-mountain plain areas of the northern foothills of the Kunlun Mountain and parts of the southern foothills of the Altai Mountain remains stable or slightly increases (0-2 mm/mon). In contrast, the rest of the region experiences varying degrees of decline, with the Tianshan Mountains region's pre-mountain plain area showing the most significant decrease (-3 mm/mon to -1 mm/mon), and the area affected by this decline increasing annually. The decrease is most pronounced in the plain area in front of the Tianshan Mountains (-3 mm/mon to -1 mm/mon), with the affected area expanding each year. (3) The slowdown in the decline of groundwater reserves during certain periods is related to the implementation of the “Three Red Lines” policy and measures to control groundwater over-exploitation. However, the overall decrease in groundwater reserves is attributed to the combined effects of rising temperatures and increased groundwater extraction for irrigation.
[Conclusion] The research findings provide a scientific basis for monitoring, analyzing, and managing groundwater storage changes in large-scale regions.

[Objective] In today's rapidly evolving world, emerging information technologies such as big data, artificial intelligence, the Internet, blockchain, and remote sensing are driving significant changes in current work methodologies and shaping a new paradigm for earth science research. Big data, serving as both a foundational infrastructure and a novel component, offers fresh opportunities and technical support for modernizing geological surveys. Precision monitoring, accurate forecasting, and refined services have become essential requirements for achieving high-quality development in geological survey work. The United States Geological Survey and other advanced countries' geological survey institutions are vigorously pursuing the informatization, digitalization, and intelligent transformation of their operations. These efforts have yielded substantial progress, with many having essentially achieved geological survey modernization.
[Method] Information technology stands as a pivotal force behind the transformation and evolution of geological survey endeavors. The integration of contemporary information technologies is revolutionizing traditional geological survey models, markedly enhancing efficiency, capacity, and the overall level of geological survey work. This paper commences by defining the fundamental essence of geological survey modernization, encompassing the establishment of a three-dimensional survey monitoring and observation system, an analysis, prediction, and evaluation framework, an information service system, business informatization support systems, a geological science and technology innovation ecosystem, and a comprehensive geological survey management structure. It further delineates the trajectory and orientation of geological survey modernization, aiming at digitization of surveys, automation of monitoring, quantification of predictions, intelligence in evaluations, and wisdom in services.
[Result] Drawing upon the meteorology sector as a representative example, which heavily relies on earth observation technologies, the paper outlines strategies for advancing meteorology modernization through satellite remote sensing and digital analysis simulation techniques. These include automating data collection, enhancing the sophistication of numerical forecasting, centralizing information resource management, refining social services, and standardizing operational procedures. Lastly, grounded in the practical context of geological surveys, the paper proposes strategies and recommendations for China's geological survey modernization. These encompass building infrastructural foundations for geological surveying and monitoring, developing business informatization capabilities, expanding geological information products, establishing standards for geological informatization, and conducting research into basic theories and technological equipment within the realm of informatization.
[Conclusion] Looking ahead, automation, informatization, and intelligence will characterize the new era of geological survey work. Consequently, accelerating the construction of geological survey modernization is not only imperative but also urgently needed.

[Objective] Mainstream semantic segmentation methods, primarily designed for small natural images, face significant challenges when applied to large-scale remote sensing imagery, e.g., 5000×5000 pixels. These challenges include spatial feature loss due to fragmented processing, block stitching artifacts from patch-based strategies, and prohibitive computational resource demands. To overcome these limitations, this study proposes large scale segment anything model (LS-SAM), an enhanced fine-tuning framework based on the segment anything model (SAM), specifically optimized for accurate and efficient building extraction from ultra-high-resolution remote sensing images. The primary objectives are to: Enable end-to-end processing of full-scale images while preserving spatial and contextual integrity. Balance computational efficiency with high segmentation accuracy for practical deployment. Address the limitations of existing methods in handling large-scale geospatial data.
[Method] The proposed LS-SAM framework addresses challenges in large-scale remote sensing image processing through four innovations: (1) A dynamic positional encoding generator (PEG) replaces SAM's fixed positional encoding, using depthwise convolutions (kernel size=3) to adaptively partition input images, e.g., H×W, into patches and project spatial coordinates into learnable embeddings. This enables arbitrary-sized input processing, e.g., 5000×5000 pixels, while preserving positional relationships. (2) A hybrid encoder integrates a CNN backbone with Transformer, where the CNN extracts hierarchical local features (edges, textures) and fuses them with SAM's global attention outputs via skip connections. (3) A SMS-AdaptFormer employs parallel convolutional branches with varying kernel sizes 1×1, 3×3, 5×5 and dilation rates, r=8, 14, 20, small kernels refine local details, while dilated convolutions expand receptive fields. Features are aggregated via weighted summation for precise segmentation of diverse buildings. (4) A dynamic training strategy is used: during training, the model takes full-resolution images and applies random crops, e.g., 512×512 pixels, while PEG generates adaptive positional encodings. At inference, PEG handles any input size, and the combined CNN-Transformer encoder processes large images, e.g., 5000×5000 pixels, end-to-end—no chunking or stitching required.
[Result] Experiments on four public datasets, IAILD, MBD, WBDS, WAID, demonstrate LS-SAM's superiority: Achieves 86.7% mIoU on IAILD, outperforming DeepLabV3+ 81.25% and SAM 76.98%. On WBDS and WAID datasets, LS-SAM attains 96.11% and 94.14% mIoU, respectively, demonstrating robust generalization. Reduces GPU memory usage to 12GB, vs. 24GB for vanilla SAM, during training. Attains 10.1 FPS inference speed on 5000×5000 pixels images, NVIDIA RTX 3090Ti. Visual results on Inria and WBDS datasets show LS-SAM effectively mitigates boundary ambiguities and block stitching errors, particularly in dense urban areas and complex terrains. Additionally, ablation experiments reveal that removing PEG reduces mIoU by 2.06%, while disabling SMS-AdaptFormer reduces accuracy by 1.02%, confirming the contribution of each component.
[Conclusion] LS-SAM provides an effective solution for large-scale geospatial analysis by harmonizing global context modeling with local detail preservation. The framework significantly mitigates block stitching errors and computational bottlenecks, achieving state-of-the-art performance in building extraction tasks. This work establishes a foundation for advancing large-scale remote sensing interpretation, with potential applications in urban planning, disaster response, and environmental monitoring. Future work will focus on scaling the architecture for ultra-large imagery, 10000×10000 pixels, and enhancing cross-modal adaptability for multi-sensor data fusion.

[Objective] Raster geological maps constitute vital data resources in geological research and mineral exploration. However, these maps are often stored in non-standardized, unstructured raster formats, posing significant challenges for large-scale data retrieval, integration, and intelligent utilization. Traditional geological data management systems frequently struggle with fragmented storage, inefficient querying, weak inter-data relationships, and inadequate support for semantic-level searches. These limitations hinder the comprehensive exploitation of geological information. To address these challenges, this study explores large-scale textual element extraction from raster geological maps and proposes a novel map-text retrieval framework to enhance semantic accessibility and intelligent processing of unstructured geological data.
[Method] This study proposes a method for text extraction and map-text retrieval from raster geological maps. A distributed architecture leveraging HBase and the Hadoop distributed file system (HDFS) is constructed to efficiently manage large-scale unstructured raster geological maps and associated documents. For text extraction from geological maps, a deep learning-based optical character reader (OCR) pipeline is implemented, combining a differentiable binarization network (DBNet) for text region detection with a convolutional recurrent neural network (CRNN) for sequence-based text recognition. This approach substantially improves text detection and recognition accuracy under complex map backgrounds. In processing geological reports, the term frequency-inverse document frequency (TF-IDF) algorithm is employed for semantic similarity analysis, establishing meaningful associations between map elements and document content. Building on this, a sequence labeling model integrating bidirectional encoder representations from transformers (BERT) and conditional random field (CRF) is utilized to automatically extract geological entities and domain-specific keywords. Additionally, a full-text retrieval module based on the Apache Solr search engine is incorporated, enabling high-efficiency, semantic-aware retrieval of geological documents.
[Result] The experimental results indicate that the proposed method substantially enhances the usability of textual information and the efficiency of keyword extraction in real-world applications. The proportion of usable text increased from 51.7% to 82.3%, reflecting a marked improvement in the accuracy and completeness of text extraction. Furthermore, the efficiency of keyword extraction achieved a 426% improvement compared to the TextRank algorithm. The proposed framework demonstrates strong scalability and adaptability, enabling efficient processing of large-scale geological datasets, supporting real-time storage and rapid retrieval across datasets of various sizes, and significantly advancing the utilization efficiency of unstructured geological data.
[Conclusion] This study proposes a method for text extraction and map-text retrieval tailored to unstructured geological data, integrating distributed big data infrastructure, deep learning-based OCR technologies, and advanced semantic extraction models. The proposed method significantly strengthens the connection between raster geological maps and textual geological knowledge, thereby improving data utilization efficiency, research productivity, and geological decision-making. Looking ahead, future work will focus on incorporating cutting-edge Transformer-based architectures, constructing domain-specific geological knowledge graphs, and enhancing user interaction via intuitive interfaces. This integrated approach introduces a novel technological paradigm for geological data processing and knowledge discovery, with potential applications in scientific research, natural resource management, and digital geoscience services.

[Objective] Population movement reflects the complex interplay between human activities and geographical dynamics, facilitating the spatial diffusion and concentration of resources, capital, and technology. To deepen the understanding of its characteristics, patterns, and development trends, this study analyzes the structural features and evolutionary dynamics of population flow networks.
[Method] This research utilizes Amap migration big data to examine the spatiotemporal patterns and structural evolution of the population flow network within the Chengdu-Chongqing economic circle over the past five years. By integrating complex network analysis and GIS methods, the study provides a comprehensive investigation.
[Result] The results show that population inflows and outflows in most cities within the Chengdu-Chongqing Economic Circle have generally increased over the past five years, though fluctuations with periodic patterns persist. Chengdu, Chongqing, and Leshan exhibit a net daily population inflow pattern, contrasting with the other thirteen cities. A clear weekly rhythm characterizes population flows: in Chengdu and Chongqing, inflow peaks occur on Sundays and outflow peaks on Saturdays, while in the remaining fourteen cities, inflow peaks fall on Saturdays and outflow peaks on Sundays. Rather than forming a typical “Chengdu-Chongqing” dual-center structure, the population flow evolves into a single-hub network centered on Chengdu. The flow network demonstrates distinct spatial proximity and hierarchy, with larger flows primarily occurring between Chengdu-Chongqing and their satellite cities. Over five years, inter-tier-one-city flows (Chengdu-Chongqing) exhibit an increasingly polarized trend, accounting for 48%, 45%, 49%, 51%, and 56% of the total annually. Smaller flows dominate interactions among non-core cities. Three cohesive subgroups emerge: a western cluster around Chengdu, an eastern cluster around Chongqing, and a southern Sichuan cluster, collectively representing around 70% of intercity mobility. Additionally, population flow networks derived from Amap data versus railway data reveal significant spatial discrepancies, with Amap-based networks highlighting stronger hub-and-spoke dynamics in core cities.
[Conclusion] Through analyzing five-year Amap migration datasets, this study systematically elucidated the spatiotemporal dynamics, distribution, network structure, and evolutionary trends of population flows in the Chengdu-Chongqing economic circle. The findings enhance regional population flow theory, offering insights for regional planning and governance. Additionally, the comparison of Amap and railway data challenges single-source research paradigms, providing a methodological reference for future studies.

[Objective] The rapid development of navigation positioning and IoT technology has generated a large amount of trajectory data, which plays an important role in the field of spatiotemporal data mining. In many application scenarios, it is usually necessary to efficiently query the k-nearest trajectories from large-scale trajectory data under given spatiotemporal constraints, known as trajectory spatiotemporal k-nearest neighbor query. The spatiotemporal features of trajectory data pose challenges to data query design, and existing work still has some shortcomings in handling spatiotemporal k-nearest neighbor queries that coexist with spatiotemporal constraints. Therefore, this paper studies large-scale spatiotemporal k-nearest neighbor distributed queries for trajectory data using the distributed column family database HBase. The aim is to improve query efficiency through advanced indexing strategies and optimized query mechanisms, and provide technical support for practical applications.
[Method] This paper first provides a formal definition of trajectories and their spatiotemporal k-nearest neighbor query, including point query mode and trajectory query mode. It also provides methods for calculating the distance between points and trajectories and the distance between trajectories. By combining XZ2 spatial encoding and XZT temporal encoding, two new spatiotemporal indexing strategies were designed: XZ2T+and TXZ2+. These two indexing strategies solve the problem of rough time partitioning in previous spatiotemporal indexing strategies. Based on this, this paper designs and implements a multi round distributed spatiotemporal k-nearest neighbor query. In each round, the query scope is encoded by a spatiotemporal indexing strategy and divided into different subqueries. Due to the combination of indexing and distributed structure proposed in this paper, a data shard counting system is introduced. After encoding the query range in time and space, we can optimize the effective number of query ranges based on data fragmentation. This solution addresses the issue of expanding query ranges in previous research and improves data scanning efficiency. Additionally, to reduce data processing, this paper also uses HBase's coprocessor mechanism on the storage side to implement pruning strategies that consider the spatiotemporal characteristics of trajectory data.
[Result] We conducted a comparative experiment with the existing index strategy XZ2+T, and the experimental results showed that the two indexing strategies proposed in this paper, XZ2T+ and TXZ2+, did not take longer to construct the dataset than XZ2+T. Most importantly, both of these indexing strategies exhibit better query performance compared to XZ2+T, and can effectively support trajectory spatiotemporal k-nearest neighbor queries. Without optimizing the query window, in the experiment when the parallelism is 24, the efficiency of XZ2T+ is increased by 25.7%, TXZ2+ by 18.9% in point mode, XZ2T+ by 36.4% and TXZ2+ by 40.8% in trajectory mode. This paper effectively accelerates the query process through distributed parallel queries, and optimizes query efficiency by adjusting the number of query scopes based on data sharding and setting spatiotemporal pruning strategies. Further experiments have shown that our scheme exhibits stable and good performance at different k values. When the time window is small, XZ2T+ queries are faster because they do not scan duplicate data. When the time window is large, the advantage of TXZ2+ gradually becomes apparent because it maintains aggregation in the time dimension.
[Conclusion] Overall, this paper has successfully implements distributed trajectory spatiotemporal k-nearest neighbor queries, addressing some of the shortcomings of previous research. It has been validated on large-scale datasets, providing strong technical support for processing large-scale trajectory data queries and laying a solid foundation for future research and application of trajectory data management systems.

[Objective] Epipolar imagery is crucial in the 3D reconstruction process within photogrammetry. Traditional image analysis requires identifying feature points across an entire graphic range, whereas epipolar imagery simplifies this by focusing on corresponding points along the epipolar line of another image after a feature point is detected. This advantage extends to dense matching and 3D scene construction in computer vision. Unlike simple calibration used in computer vision, close-up photogrammetry offers precise absolute 3D coordinates and higher calibration accuracy. However, binocular orientation elements obtained by technicians typically rely on photogrammetric methods. Despite shared theories, differing coordinate system definitions between the two fields complicate direct integration.
[Method] Addressing the incompatibility of traditional epipolar image generation methods from photogrammetry with computer vision, this paper introduces a new method tailored for the Fusiello calibration model in computer vision, using a binocular camera setup for mobile robot navigation and hazard avoidance. The study begins by comparing geometric constraints and parameter expressions of the poles between the Fusiello and close-up photogrammetric models, deriving an adaptation formula based on rotational and translational parameters. Next, it adapts and processes parameters from both models for epipolar image generation. Finally, SIFT feature matching and RANSAC mismatch rejection evaluate calibration accuracy and the effectiveness of the epipolar correction adaptation through reprojection errors of matched feature points.
[Result] Experiments demonstrate that the proposed adaptation method achieves an average matching error below 0.9 pixels, a maximum error under 2 pixels, and a root-mean-square error around 1 pixel. It successfully verifies the accuracy of binocular epipolar correction and relative orientation calibration for the navigation and hazard avoidance camera, outperforming the epipolar rearranging method in speed and accuracy, and marginally surpassing the Bouguet calibration method.
[Conclusion] This methodology paves the way for subsequent stereo mapping tasks involving the navigation and hazard avoidance binocular camera. Future work will explore extended baseline scenarios and significant viewpoint discrepancies to further validate the approach presented herein, providing a robust foundation and reference for ongoing research in this domain.

[Objective] Mathematical foundations are integral to maps, enabling users to precisely interpret spatial relationships, feature positions, and geometric configurations. Restoring these foundations is critical for maps lacking coordinate information, particularly historical or digitized scans. Map registration serves as the primary means to align map images with standard geographic coordinates, facilitating their integration into geospatial analyses. However, existing registration methods prioritize image feature extraction over leveraging inherently associated geographic coordinates, compromising accuracy and robustness. This limitation is pronounced for maps with complex projections, variable scales, divergent symbology, or significant imaging distortions. Consequently, there is a pressing need for an adaptable, automated approach that harnesses semantic feature points with embedded geographic coordinates to restore cartographic mathematical frameworks.
[Method] This study introduces a map registration framework combining deep learning-based semantic keypoint detection with a cubic transformation model. A YOLOv8-pose architecture is trained on annotated data to efficiently identify visually discernible semantic keypoints while preserving their geographic coordinates. These paired image-geographic coordinates are then input into a weighted least squares algorithm to derive cubic transformation parameters, effectively modeling the spatial-to-geographic transformation. This process automates the recovery of mathematical foundations for unreferenced maps, minimizing manual intervention and enhancing resilience across diverse cartographic conditions.
[Result]Experiments validated the method's performance on six maps with varying projections, scales, symbology, and imaging artifacts (rotation, perspective distortion, overexposure, texture interference). The approach achieved over 90% precision and recall in semantic keypoint matching, demonstrating strong adaptability to challenging scenarios. By reconciling recovered mathematical frameworks with standard geographic data, the method successfully integrated unreferenced maps into hybrid geospatial datasets, vector, and raster formats.
[Conclusion] By integrating semantic features with geographic coordinates within a deep learning paradigm, this study achieves efficient, accurate, and robust restoration of map mathematical foundations. The proposed method addresses limitations of traditional approaches, such as rigid transformation models, heavy manual reliance, and poor generalizability. This work advances applications in historical cartography, thematic mapping, geospatial data fusion, and semantic geographic space analysis.

[Objective] 3D realistic geospatial landscape model (3DRGLM) serves as a crucial infrastructure and foundational element in advancing the digital transformation of urban governance. It encounters challenges such as high modeling expenses, data integration difficulties, and inadequate spatial analysis capabilities. As the demand for nuanced urban management grows, there is a corresponding need to refine the granularity of management entities supporting this governance. The adoption of three-dimensional information models facilitates an elevation from two-dimensional to three-dimensional perspectives, and from static to dynamic representations, thereby imposing new demands on element coding and the integration of business information. Currently, the platform's spatial analysis and decision-making capacities are insufficient and necessitate further enhancement.
[Methods] This study addresses these challenges by exploring lightweight hierarchical household model reconstruction techniques to swiftly model detailed management units at various levels; establishing encoding rules grounded in planar mesh subdivision to optimize data encoding and correlation efficiency; investigating business information fusion methodologies that combine the nine-intersection model with nearest neighbor matching, thereby creating multi-source data spatial constraints associations; and developing a flexible analytical framework rooted in spatiotemporal knowledge graphs to offer adaptable computational support for grassroots services. A practical application in Xuhui district, Shanghai, involved constructing a live 3D data system encompassing both interior and exterior spaces, facilitating entity mapping and associating business management information pertinent to urban governance. Efficient visualization was attained through server cloud rendering and WebRTC technology, leading to the creation of diverse demonstration scenarios including grassroots community governance, panoramic digital business operations, and transparent firefighting simulations.
[Results] Employing lightweight hierarchical household model automation has enabled rapid construction of refined management units tailored to urban governance needs. The 3D encoding technique, predicated on planar mesh segmentation and prioritizing machine recognition while maintaining human readability, effectively controls encoding length to enhance data handling efficiency. Integrating business information with consideration for spatial constraints has successfully mapped entities and associated business management data within urban governance, accurately depicting residential and commercial structures. Computation powered by spatiotemporal knowledge graphs offers versatile and customizable capabilities for grassroots services, alleviating workload pressures.
[Conclusion] The development of a 3DRGLM platform for urban governance propels the progressive evolution towards visualization, intelligence, precision, and interactivity in city management. These research findings not only bolster urban governance capabilities but also furnish valuable insights and direction for the digital metamorphosis and advancement of urban governance underpinned by real-world 3D scenarios.

[Objective] An increasing number of the public tend to share information via social media posts during disaster events. This is significant for supporting disaster risk reduction decision-making by capturing timely public response information. This study proposes a method for generating public disaster response maps through mining social media text data, demonstrating its application by constructing such maps for major earthquakes, typhoons, and cold waves in China using Weibo data.
[Method] Weibo text data related to earthquakes, typhoons, and cold waves (2018 - 2022) were collected via the Sina Weibo API and subjected to preprocessing steps, including deduplication, filtering, and word segmentation. Based on administrative division data, a comprehensive gazetteer containing national township-level (street) administrative names was developed. By integrating Python's requests and pandas libraries with jieba word segmentation technology, geographic entities in Weibo texts were precisely identified and extracted. For different disaster types, keyword frequency statistics were conducted, followed by spatial analysis.
[Result] The results reveal that: (1) Earthquake responses clustered in Sichuan province, reflecting its status as a seismically active region;(2) Typhoon-affected areas predominantly included coastal provinces (Guangdong, Zhejiang, Fujian, Shandong), with Guangdong exhibiting the highest impact proportion, consistent with typhoon track distributions; (3) Cold waves howed higher prevalence in central/southern China (Jan-Mar) and primarily affected northern regions (Inner Mongolia, Beijing) during Nov-Dec. These findings align well with statistical validation, confirming methodological reliability. The generated maps, visualize spatiotemporal distribution patterns and public response hotspots for different disaster types.
[Conclusion] This approach effectively reveals spatial-temporal distribution patterns of public responses to various disasters across large regions. It provides decision-making support for precise disaster prevention and mitigation, addressing spatial information gaps in traditional surveys. Despite challenges from data bias and quality inconsistencies in social media, the methodology demonstrates significant potential for disaster monitoring. Future work should focus on: (1) Refining geolocation extraction algorithms; (2) Enhancing emotional information analysis; (3) Integrating multi-source data to build a comprehensive disaster monitoring system.

[Objective] Surface water resources in arid African regions are scarce and unevenly distributed, presenting significant challenges for water access and management. Egypt, emblematic of such regions, endures a hot, dry climate with minimal rainfall, rendering its water resources heavily reliant on the Nile River and an extensive network of artificial canals. These canals are crucial for agriculture, population sustainability, and driving economic activities. However, their complex and variable spatial configurations, compounded by the presence of adjacent landforms like deserts and agricultural areas, render the precise delineation of surface water boundaries and small water bodies exceedingly difficult for conventional remote sensing methodologies. These methods often fall short due to incomplete extractions, ambiguous boundaries, and misclassifications, particularly within narrow canals. Addressing these issues is crucial for achieving accurate monitoring of surface water and facilitating the sustainable management of Egypt's water resources.
[Method] To address these challenges, this study proposes an improved U-Net deep learning model, namely GLF-MFUNet, designed to enhance the precision and robustness of surface water extraction from remote sensing imagery. The model features a dual-path encoder that seamlessly integrates Vision Transformer (ViT) and Manhattan self-attention (MVT), effectively capturing global contextual cues to ensure comprehensive extraction of artificial canals and enhance water body classification in intricate environments. Moreover, a multi-scale depthwise convolution mechanism is embedded within the spatial attention module, empowering the model to proficiently merge water features across diverse scales, thereby refining the definition of fine details and water boundaries. The implementation of channel attention mechanisms further serves to suppress noise and minimize misclassifications. The model's training utilized Sentinel-2 multispectral imagery of Egypt and was rigorously validated against ZY-3 satellite data, ensuring its robustness across varying environmental conditions.
[Result] Experimental outcomes underscore that the proposed GLF-MFUNet markedly elevates water body extraction accuracy in comparison to existing models. It outshines prevalent semantic segmentation models, including ViT, SwinTransformer, and DeepLabV3+, demonstrating superior performance across a majority of evaluation metrics. Specifically, relative to the pre-improvement baseline U-Net model, GLF-MFUNet achieves an impressive increase of 4.97% in IoU, 3.02% in F1-score, and a substantial 10% enhancement in precision. The synergistic fusion of global and local feature extraction through the MVT and SPCAI modules endows the model with heightened spatial continuity and a reduced incidence of false detections.
[Conclusion] The GLF-MFUNet model adeptly confronts the pivotal challenges associated with surface water extraction in arid African locales such as Egypt, yielding substantial improvements in detection accuracy, spatial coherence, and classification consistency. Through the integration of global-local feature synthesis, attentive mechanisms, and multi-scale information processing, it emerges as a fitting solution for surveillance of artificial water networks in arid environments. Evidently, GLF-MFUNet exhibits unparalleled performance in large-scale, automated surface water mapping, furnishing dependable data to inform water resource governance, irrigation scheming, and ecological preservation initiatives. Its successful application in Egypt highlights its potential for wider adaptation in arid and semi-arid areas, providing valuable support for zones globally, thereby contributing valuably to the sustainable water resource management in Africa.

[Objective] The rational planning and allocation of medical facilities are crucial for enhancing urban public services and promoting the integrated development of urban and rural areas. This study proposes a comprehensive analysis framework and model parameter adaptation strategy at the prefecture-level city level to guide the hierarchical and accurate allocation of medical facilities.
[Method] This study focuses on the central area of Ganzhou City, using residential areas as assessment units. From the perspective of hierarchical diagnosis and treatment, it employs an improved potential model, service coverage and overlap rate, spatial autocorrelation analysis, minimum facility point model, and GIS spatial analysis. By analyzing the adaptability of relevant model parameters, the study obtains the optimal parameter combination to systematically analyze and evaluate accessibility, equality, and spatial optimization. Firstly, the distribution characteristics of medical facilities in the study area are examined based on location entropy. The improved potential model is then used to measure accessibility for hospitals of various levels. Subsequently, hierarchical evaluations from non-spatial and spatial equality perspectives are conducted based on accessibility results. Finally, using the minimum facility point model, the study performs spatial layout optimization analysis and proposes corresponding suggestions.
[Result] The research findings indicate that: (1) Localized calibration of limit travel time and friction coefficient in the improved potential model significantly enhances regional model adaptability within the “accessibility-equality-spatial optimization” analysis system. (2) The spatial allocation and distribution of medical facilities in the study area are imbalanced, with accessibility decreasing in a circular layered pattern. Tertiary, secondary, and primary hospitals show gradually decreasing accessibility levels, with notable differences. High accessibility communities are located in areas with dense medical facilities and convenient transportation. (3) Medical facilities generally exhibit inequality. The coverage and overlap of the 15-minute service range of primary hospitals and the 30-minute service range of secondary hospitals are relatively high, with significant spatial agglomeration among hospitals at all levels. (4) It is recommended to add new or renovate primary and secondary hospitals in under-resourced areas such as Shuidong Town and plan tertiary hospitals in peripheral areas like Meilin Town to achieve balanced medical facility distribution.
[Conclusion] This research enriches the systematic framework for studying medical resource allocation. The proposed analysis framework and model parameter adaptation methods support hierarchical and accurate allocation of medical facilities and provide methodological references for similar urban studies. The empirical results offer auxiliary decision-making support for optimizing medical facility layout in Ganzhou, enhancing overall efficiency and rationality, and ensuring balanced and coordinated development of regional medical resources.

[Objective] The rapid proliferation of information technologies has engendered unprecedented volumes of heterogeneous data across disparate systems and sensors. A primary challenge in multi-source data integration lies in reconciling divergent data models and organizational frameworks. This issue is particularly acute in geological survey domains, where massive, spatiotemporally correlated datasets are managed across fragmented systems with heterogeneous models and standards. Such structural and semantic discrepancies hinder data management efficiency, exacerbating redundancy, inconsistency, and dispersion.
[Method] We propose a metadata-driven, semantic modeling approach to construct a unified data model for geological survey business management. The methodology comprises five sequential stages: (1) requirements analysis and data organization, (2) metadata extraction,(3) domain semantic analysis, (4) hierarchical model construction, and (5) iterative evaluation and refinement.
[Result] To address challenges associated with multi-source data, inconsistent standards, semantic ambiguity, and insufficient correlation in geological survey business management systems, the proposed methodology systematically organizes data sources, extracts and analyzes metadata from relevant systems, and performs semantic integration. This process yields a standardized data framework comprising unified entities, attributes, and relational structures. By leveraging geospatial coordinates and master reference data, a hierarchical organizational model is developed to harmonize heterogeneous datasets, enabling consistent data description and domain-specific abstraction. Bidirectional mapping protocols and synchronization mechanisms are established between the unified model and disparate sources. The resulting conceptual, logical, and physical data models have been validated through implementation in a geological survey business management data center.
[Conclusion] The proposed approach successfully constructs a unified data model for geological survey business management, integrating data from 20 heterogeneous sources. This model enables centralized data description and holistic, one-stop management services, resolving issues of standard inconsistency, semantic ambiguity, and multi-source data-induced redundancy. By facilitating interoperability, shared analytics, and advanced applications, the framework supports optimized workflows and evidence-based decision-making. This work provides a scalable technical solution to reconcile structural and semantic discrepancies in multi-source heterogeneous data, including spatiotemporal datasets.

[Objective] Extracting unlabeled urban roads is crucial for autonomous driving, urban planning, and emergency response. Traditional remote sensing-based extraction methods struggle with accuracy and efficiency, especially in areas with scarce labels, where deep learning models face challenges due to their reliance on large-scale labeled datasets. To address this limitation, we introduce a teacher-student framework using cross-domain transfer learning, leveraging D-LinkNet model distillation to extract urban roads from unlabeled remote sensing images. This framework enables adaptation from a labeled source domain to an unlabeled target domain, reducing the dependency on human-annotated data. A feedback mechanism further enhances pseudo-label quality, ensuring progressive improvement in segmentation accuracy.
[Method] The proposed approach employs a teacher-student learning strategy with knowledge distillation and cyclic refinement to adaptively improve road extraction performance across domains. Initially, a D-LinkNet-based teacher model is trained using labeled data from a source domain. The trained model generates pseudo-labels for the unlabeled target domain, serving as the primary supervisory signals for student model training. The student model iteratively learns from these pseudo-labels, refining its segmentation capability through a feedback mechanism that progressively enhances pseudo-label accuracy. To further reduce domain gaps, cyclic distillation is introduced, continuously updating both the teacher and student models. The method is evaluated using remote sensing datasets to validate its effectiveness in urban road extraction without requiring manual annotations.
[Result] Experimental evaluations on the Massachusetts and CHN6-CUG datasets demonstrate substantial improvements in remote sensing-based road extraction. Compared to the baseline D-LinkNet model, the proposed method achieves notable performance gains, with recall, F1 score, and IoU increasing by 16.291%, 10.191%, and 11.669% on the Massachusetts dataset, respectively. Similarly, on the CHN6-CUG dataset, recall, F1 score, and IoU improve by 26.305%, 23.453%, and 20.099%. These results confirm that the integration of the teacher-student framework and D-LinkNet model distillation significantly enhances segmentation accuracy in unlabeled target domains. Furthermore, the incorporation of cyclic distillation effectively refines pseudo-label quality, reducing false predictions and improving spatial continuity, ultimately enabling more accurate and reliable urban road extraction from remote sensing imagery.
[Conclusion] The proposed method effectively addresses the challenges of unlabeled urban road extraction in remote sensing imagery, providing a scalable solution for large-scale applications. By incorporating a teacher-student framework with cross-domain transfer learning, pseudo-label refinement, and knowledge distillation, the approach significantly enhances segmentation performance in label-scarce target domains. The ability to generalize across different environments without human-annotated labels makes this method highly suitable for urban road extraction in diverse geographic regions. Its successful application to the Massachusetts and CHN6-CUG datasets highlights its potential for broader deployment in remote sensing-based urban planning, intelligent transportation systems, and infrastructure monitoring.

[Objective] Conducting geological surveys in challenging and hazardous terrains, particularly in remote plateau regions, poses significant difficulties when integrating “remote sensing + geology” multi-modal data. Given these complexities, this article focuses on innovating the storage, representation, intelligent extraction of geological remote sensing information, and automated services for geological survey data, leveraging advanced technologies like cloud computing, big data, and artificial intelligence. These innovations collectively establish a collaborative cloud service technical framework for multimodal geological survey data, catering to the demand for intelligent high-resolution data services and integrated multi-source data in difficult and dangerous geological survey areas.
[Method] (1) To address the challenge of unified expression and storage for multimodal geological survey data, we propose a novel, multi-dimensional, and multidisciplinary “remote sensing + geology” data storage model. This model constructs a centralized database that harmonizes data across various levels and scales throughout its lifecycle, allowing dynamic updates. It facilitates comprehensive, area-wide, and element-wise data support tailored to the arduous conditions of geological surveys. (2) In response to inefficiencies in image data utilization and service distribution, we introduce an automatic extraction technique for linear features from high-resolution remote sensing images, combining deep learning with wavelet transform technology. Additionally, we present a service distribution strategy for geological survey image base maps optimized for multi-platform operation. This dual approach automates linear feature extraction from intricate images and enhances the distribution efficiency of large-scale, multi-type, high-precision image base maps.(3) Addressing shortcomings in the existing data storage model, our proposal entails developing a unified database encompassing multiple hierarchical levels, scales, lifecycle stages, with continuous updates. This ensures coherent, full-coverage data management crucial for geological surveys in demanding environments. (4) To overcome low automation levels and the inability to promptly meet field survey demands, we have designed a framework centered around field geological survey location sensing, knowledge discovery, and proactive knowledge services. This framework propels the dissemination of geological survey information in challenging territories, resolving key issues related to timeliness, precision, and comprehensiveness of such services. Collectively, these technological advancements constitute a collaborative cloud service ecosystem for multimodal geological survey data, forming an “Intelligent Spatial Platform for Geological Surveys in Difficult and Dangerous Areas” underpinned by a “cloud + terminal” service paradigm.
[Result] The research findings have been successfully implemented and validated across over 400 regional geological and mineral surveys, including Quaternary geology and ophiolite belt surveys in formidable regions like Qinghai-Tibet.
[Conclusion] The outcomes of this research effectively fulfill the operational needs of geological surveys in difficult terrains, bolstering the enhancement of geological surveying capabilities.

[Objective] 3D realistic geospatial landscape model (3DRGLM) China, as an emerging spatiotemporal infrastructure, furnishes a unified three-dimensional spatiotemporal foundation crucial for the advancement of Digital China. Nonetheless, amidst its comprehensive development, substantial obstacles persist concerning the establishment of an accomplishment framework, key technological breakthroughs, and the expansion of application domains. Ningxia pioneered the initiation and completion of province-wide 3DRGLM construction in 2020. This study adopts Ningxia as a case study to methodically consolidate its practical experiences, thereby offering valuable perspectives for the national deployment and widespread adoption of real scene 3D technology.
[Methods] Grounded in the analysis of Ningxia's provincial-wide construction practices, this research unfolds through three dimensions:(1) Categorizing and characterizing the construction outcomes, aligning them with practical necessities to formulate a “three-category, four-tier” achievement system. This encompasses three model types, namely 3DRGLM terrain, rural 3D frameworks, and urban 3DRGLM models, alongside four tiers of model representation precision. It also encapsulates the multi-source nature, usability, timeliness, and sharing attributes of these achievements. (2) Tackling the hurdles and challenges encountered during construction by investigating automated modeling methodologies rooted in digital line graphics and point cloud data. This involves devising seamless integration strategies between terrain scenes and individual structures, and refining modeling techniques for intricate, irregular edifices, thereby augmenting the automation quotient of 3D modeling. (3) Assessing the efficacy of applications and services in vital areas such as major project site selection, urban planning and design, cultural tourism, and natural resource administration, guided by operational requirements and the inherent value of the achievements.
[Results] The results show that Ningxia's 3DRGLM initiative has established multi-tiered accomplishments spanning terrain, rural, and urban contexts, enabling the creation of 3D models with diverse levels of detail through the utilization of multifaceted data sources including aerial and satellite imagery. Key technological explorations have successfully addressed automatic modeling challenges in rural areas, the harmonious integration of terrain scenes with standalone models, and the nuanced modeling of complex architectural forms. Practical implementations validate that these accomplishments facilitate 3D visualization services, enhance operational efficiency, and expand service reach.
[Conclusion] Ningxia has cultivated a distinctive 3DRGLM construction paradigm through early experimentation and pilot projects, charting a pragmatic course of action in achievements systems, core technologies, and application services. This approach has not only been empirically validated but also extensively implemented. The insights garnered from this experience present a viable blueprint for the nationwide proliferation and enhancement of 3DRGLM construction endeavors.

The low-altitude economy aims to make the best use of low-altitude airspace and to shape three-dimensional transportation, and demonstrates distinctive characteristics of “air-ground” cooperation. It is therefore becoming obligatory to digitize the three-dimensional low-altitude airspace, to perform 3D analysis, and to conduct digital-intelligent controlling in the 3D space. Recently, China has promoted its national 3D mapping program and the resulting data product, the 3D realistic geospatial landscape model(3DRGLM), provides reliable fundamental 3D framework data for the low-altitude economy. This paper analyzed the requirements and challenges of the unitization of 3DRGLM in supporting the low-altitude economy. Several fundamental problems were examined and development strategies were proposed, including the digitalization of low-altitude elements, the establishment of low-altitude data spaces, and 3D spatial analysis for supporting the low-altitude economy. Furthermore, the near-future key tasks were identified and examined, such as the planning of the low-altitude skyway, the development of 3D navigation maps and systems, the construction of digital infrastructure for the low-altitude economy, as well as the specialized territorial spatial planning for low-altitude applications. Future efforts should be devoted to emphasizing coordinated planning, enhancing technological innovation, developing typical application scenarios, accelerating pilot demonstrations, and promoting cross-domain integration and cooperation.

3D realistic geospatial landscape model (3DRGLM) stereoscopic reconstruction technology plays a pivotal role in China’s digital transformation by leveraging the spatiotemporal complementarity and multi-view synergy of multi-source remote sensing data to achieve high-precision, multi-dimensional virtual space modeling. This article systematically reviews the technical framework of multi-source remote sensing data-driven 3D realistic geospatial landscape model reconstruction, covering data sources, geographic scene and entity modeling methods, technical challenges, and emerging trends.
Key data sources include optical imagery (satellite, aerial, and close-range), LiDAR point clouds (airborne, terrestrial, and mobile systems), and SAR data. Satellite optical imagery facilitates large-scale terrain monitoring, while aerial and close-range imagery improve urban and component-level modeling. LiDAR provides high-precision 3D spatial information, with mobile systems enhancing efficiency through colored point cloud acquisition. SAR data, when combined with InSAR-derived deformation point clouds, strengthens the reconstruction of complex terrain. Additionally, IoT-generated real-time data and historical geospatial data contribute to the dynamic maintenance of 3D models.
Geographic scene modeling primarily relies on mesh generation using multi-view stereo (MVS) and 3D Gaussian splatting (3DGS). Traditional MVS methods encounter difficulties in feature matching and environmental adaptability, while deep learning frameworks optimize pixel-level geometry. Transformer-based models enable joint camera calibration and 3D reconstruction from unconstrained images. 3DGS excels in visual fidelity and real-time rendering but faces challenges in maintaining multi-view geometric consistency. Large-scale reconstruction approaches balance detail preservation and computational efficiency through dynamic partitioning and distributed training, although cross-region fusion remains challenging.
Geographic entity modeling integrates model-driven (template-based) and data-driven (primitive segmentation) approaches. Model-driven methods excel in structured but low-detail reconstructions, while data-driven techniques provide flexibility at the cost of higher computational demands. Deep learning methods, such as Transformers and graph neural networks (GNN), facilitate large-scale urban reconstruction but require extensive training data. As a result, semi-automated workflows remain dominant, underscoring the need for a balance between efficiency and quality.
Critical challenges persist in advancing 3D realistic geospatial landscape model reconstruction: (1) Generative AI-based methods enable cross-modal 3D generation but encounter challenges related to data dependency and maintaining the plausibility of urban scenes. (2) Dynamic scene reconstruction faces difficulties in addressing long-interval changes (e.g., building demolition) and integrating rigid and non-rigid structures, with limited adaptability observed in methods such as Street Gaussians. (3) Multi-source data synergy remains constrained by spatiotemporal misalignment and complex preprocessing, necessitating the development of integrated platforms to enhance interoperability. (4) Application-driven product derivation demands standardized yet flexible models (e.g., photovoltaic assessment) to broaden the application of 3D models in smart cities and natural resource management.
Future developments focus on four promising areas: (1) Integrating generative AI with differentiable rendering to achieve lightweight and dynamic modeling. (2) Developing temporal reconstruction techniques that combine physical simulation with historical data-driven prediction to enhance long-term scene modeling. (3) Advancing intelligent multi-source registration and distributed computing to improve efficiency and scalability in large-scale reconstruction tasks. (4) Designing application-oriented model systems to enhance domain-specific services, such as digital twin platforms, by tailoring models to the needs of various industries. By addressing these challenges, 3D realistic geospatial landscape model reconstruction will strengthen its role as a spatiotemporal backbone supporting China’s digital economy and ecological civilization initiatives.

Current methods for identifying areas of interest (AOIs) in tourism research often overlook less popular cities and primarily rely on tourist location data. These methods do not adequately account for the influence of geographical factors on tourist activities, resulting in limited geographical explanatory power. This study integrates the relationship between tourism activities and their influencing factors into the AOI identification process. It examines variations in tourist distribution density from a microscopic perspective, and establishes a reasonable threshold to clearly demarcate AOIs from non-AOIs.
Utilizing machine learning techniques, this study establishes a data mapping relationship between tourist distribution and its influencing factors to perform AOI identification. The aim is to precisely identify hotspots of inbound tourist activities at a 30-meter grid scale. The study area is divided into basic grids, and the presence or absence of tourists in these grids is treated as two types of samples. A decision tree is constructed based on the relationship between grid characteristics and these samples, with prediction outcomes determined through the results of the decision tree. The question of tourist presence is thus transformed into a probability issue, using the likelihood of tourist presence to represent regional differences in tourist activity popularity. Data from geotagged photos and their attributes from the Flickr platform are used in conjunction with spatiotemporal data that quantify levels of tourist facilities, services, and resources. The random forest (RF) algorithm is then to identify inbound AOIs. Results are compared with those from a density-based spatial clustering of applications with noise for geotagged photos (P-DBSCAN) to investigate the characteristics and causes of inbound AOIs in Changsha.
The findings indicate that the RF algorithm effectively identifies overall regional heat, offering richer information, broader coverage, and some AOI boundaries with clear geographical significance. In Changsha, AOIs are concentrated in three primary areas. The historical and cultural AOI serves as the core area visited by inbound tourists. Fewer AOIs are found outside this core area, mostly coinciding with local leisure and tourism zones, suitable for local recreation, travel, and shopping. The overall distribution pattern of inbound tourists remains relatively stable, with the periphery of the core area is significantly influenced by distance, indicating suboptimal tourism development potential. The critical point affecting inbound tourist distribution lies 400 meters outside attractions. Infrastructure and natural conditions exert minimal constraints on the distribution of inbound tourists. Urban tourism managers should focus on enhancing the attractiveness of popular sites and improving detailed tourist experiences.
Applying the RF algorithm to inbound tourist studies compensates for the limitations of clustering algorithms, distinguishes regional popularity variations and their causes in detail, and thereby provides targeted insights. The model’s reliance is not rely solely on tourist data, avoiding certain issues related to data representativeness. The predictive outcomes can offer theoretical foundations and guidance for urban tourism planners, enriching the research content on urban inbound tourism. However, the study has some limitations; for instance, the spatial behaviors of inbound tourists result from a combination of multiple factors. This analysis only considered objective factors, lacking an in-depth exploration of subjective elements, which requires further investigation in future studies.

现有的城市首位度仅反映中心城市与区域内特定几个非中心城市之间的关系，忽略了城市间跨行政区边界的交流。本文采用树状结构动态而清晰地展现2010～2019年长江中游城市群城市体系演变，聚焦省会城市与非省会城市之间的时空联系变化，从而丰富省会首位特征的刻画；并进一步通过Kanbur-Zhang指数和Capello空间溢出指数动态评估长江中游城市群省会城市经济发展给非省会城市带来的影响。结果表明：(1)2010～2019年长江中游城市群省会城市要素聚集程度，以及与所在城市圈内其他城市的交流联系紧密度随时间均有提升。(2)三个中心城市经济发展对周边城市产生效应不同；首位特征最强的武汉市对周边城市由虹吸效应转为显著的溢出效应，环长株潭城市圈中长沙市在后期初现溢出效应，首位特征最弱的南昌市对所在城市圈中其他城市仍以虹吸效应为主。城市组团发展受政策因素影响较为明显，本研究能够为强省会战略的实施提供技术支持。

To comprehensively, promptly, and objectively monitor the progress of an architectural construction project and enhance the government’s supervisory information capabilities, a monitoring technique utilizing unmanned aerial vehicles (UAV) is introduced. The conventional method of overseeing construction schedules, which relies on budget execution rates and textual descriptions or financial reports to gauge process advancement, often lacks objectivity, clarity, and completeness. With advancements in UAV technology, obtaining high-resolution 3D models of construction projects in their entirety and in a timely manner has become more feasible. This paper proposes an effective methodapproach to evaluate construction schedules from two perspectives: status monitoring and process monitoring, using objective UAV data.
Following an analysis of the actual construction process and the interpretative capacity of the UAV 3D model, a multi-tiered classification system is devised to ascertain the current state of the project. This classification encompasses initiation status, construction phase, and overall construction status, ranging from the macro view to individual construction units. Based on this classification and predefined interpretation symbols within the 3D model, vector data representing the current status is extracted through visual interpretation. Utilizing the classified data on initiation status and construction phase, the analytic hierarchy process (AHP) is employed to establish coding rules and assign weights that denote different statuses. Changes in the types, quantities, and areas of the construction phase and status over time can reflect the progression of the construction project. Consequently, a combination of the proposed dynamic coding analysis for changes in the construction phase and a Sankey diagram visual analysis for alterations in construction status are used to assess construction advancement. The dynamic coding of the construction phase qualitatively represents state transformations, while the Sankey diagram quantitatively expresses the rate of these changes.
The application of this method on three real-world construction projects at varying stages demonstrates its efficacy. Firstly, by applying the coding rules to construction phase data, the status of the construction can be accurately defined. Secondly, integrating dynamic degree codes with Sankey diagrams, derived from calculating transitions in the areas of the construction phase and status, allows for a reasonable description of the construction process compared to actual conditions.
Leveraging the rapid data acquisition and high resolution of 3D UAV technology, a method for monitoring and evaluating construction schedule progress is proposed. Through application in three case studies, the effectiveness of this method is confirmed. Using 3D data and established models of construction projects, each construction unit’s scope and corresponding current state classification can be objectively obtained, thereby achieving comprehensive, objective, and quantitative monitoring of both the current construction status and state changes between two monitoring periods. This data and methodology offer an effective and objective evaluation of construction status and progress. The adoption of UAV technology introduces a novel approach to monitoring construction schedules, significantly enhancing the traditional method of financial reporting.

The 3D realistic geospatial landscape model (3DRGLM) China initiative constitutes a vital component of the nation’s emerging infrastructure, with its standard system serving as the cornerstone to guarantee the successful execution of 3DRGLM development. In response to the challenge posed by an incomplete standard framework, this paper undertakes a comprehensive review of pertinent 3DRGLM standards from both domestic and international contexts, meticulously identifying shortcomings within existing norms.
Guided by overarching principles of standard formulation—systematic approach, scientific rigor, progressiveness, scalability, and operability—this study is anchored in the 3DRGLM product ecosystem and aspires to propel advancements in technology and production organization methodologies. It proceeds to delineate the design philosophy and comprehensive structure of the 3DRGLM standard system, encompassing thirty core elements distributed across five key dimensions: holistic design, acquisition and processing, database administration, application services, and quality assurance.
Leveraging the full potential of established surveying and cartographic geographic information standards, the paper introduces eighteen novel standards along with their principal tenets, ensuring coverage throughout the entire lifecycle of 3DRGLM implementation. This work furnishes a technical compass for the research and development endeavors surrounding 3DRGLM standards, pivotal to realizing a national “one map” that is seamlessly interconnected horizontally and vertically integrated. Such an accomplishment holds profound implications for the realm of 3DRGLM construction.

[Objective] Landslides represent a significant geological hazard in mountainous areas of western China. Particularly, the Qinzhou district of Tianshui city, Gansu province—characterized by steep terrain, complex geological structures, and uneven precipitation distribution—experiences frequent landslides that pose severe risks to ecological stability and infrastructure safety. However, limited long-term, high-precision landslide monitoring data and insufficient analysis of multifactorial triggers hinder effective risk management. This study aims to identify the spatiotemporal evolution characteristics of large-scale landslides in this region and elucidate the dominant environmental drivers and their interactions.
[Method] A total of 50 Sentinel-1A descending orbit SAR images, acquired between June 2021 and June 2024, were processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to derive surface deformation time series. Seventeen representative large-scale landslides were identified based on deformation features. Subsequently, the Geographical Detector model was employed to quantify the influence of eight environmental variables—elevation, slope, relief amplitude, aspect, precipitation, humidity, seismic activity, and anthropogenic disturbance—on landslide distribution. The spatial explanatory power (q-values) of each factor and their pairwise interactions were systematically analyzed.
[Result] Surface deformation rates in Qinzhou district exhibited significant spatial heterogeneity, ranging from -5.08 mm/a to 13.7 mm/a. Landslides were predominantly concentrated in zones with moderate elevation (1300-1750 m), moderate slope gradients (10°-15°), and annual precipitation between 535-550 mm. Landslides were further classified into three types: high-speed active, moderate-to-low-speed, and stable. Elevation (q = 0.374), precipitation (q = 0.252), and soil moisture (q = 0.216) emerged as the most influential single factors. Notably, multifactor interactions demonstrated strong nonlinear enhancement effects, such as precipitation interacting with topographic relief (q = 1) and elevation interacting with human activity (q = 0.91), substantially improving explanatory power for landslide distribution.
[Conclusion] This study reveals that landslide occurrence in Qinzhou district is governed by the interplay of multiple environmental factors, exhibiting distinct spatial clustering, threshold responses, and nonlinear coupling effects. The integrated approach combining SBAS-InSAR monitoring and the Geographical Detector framework provides a robust methodology for capturing spatiotemporal dynamics and driving mechanisms of landslides. These findings offer scientific guidance for early warning systems, spatial planning, and ecological risk mitigation in mountainous regions with similar geological and climatic conditions.

实景三维作为国家新型基础测绘和时空大数据平台的重要数据支撑，实现了对真实世界的高精度、高真实度、高智能度的数字化重现，具有广泛的应用价值和社会意义。实景三维数据提供了对真实物理环境准确和逼真的描述，有助于在各个领域更好地进行决策、分析和模拟。本文以第24届国际摄影测量与遥感大会（XXIV ISPRS Congress）的研究成果为依据，从实景三维的立体化、真实化、实体化三个方面，概述了国际上的科研进展和发展趋势，并针对中国实景三维建设面临的难点和挑战，提出了相应的对策和建议。针对不同层级的地理空间实体（如地形、城市、部件等）的建模需求，立体化重构技术利用不同数据获取平台特点，借助多模态遥感数据融合实现产品的多尺度多细节层级表达。纹理定向映射和感知信息融合等技术进一步提升了重构产品的真实化描述，将三维模型真实客观地模拟于数字化空间。具有海量性和实时性的物联感知数据包含了物联网技术采集的各种传感器数据，将其与实景三维模型进行融合和分析，既可用于实景三维产品更新，也可借助三维平台为众多时空地理信息应用赋能，如提供监测环境变化、预测灾害风险等智能服务。同时，实体化建模技术通过对精细化分类三维产品的各个部件结构进行编码存储，并利用在线系统与支撑环境的建设提供高效便捷的管理和计算平台，从而力争达到众多地理应用模块的综合协同。中国当前在实景三维技术领域同样成果显著，但在未来还需要突破关键技术。为实现实景三维产品的高效建模，需进一步融合数据源特点，完善产品结构和属性细粒度，并在此基础上最大限度压缩计算和存储成本。挖掘海量物联感知数据在实景三维产品真实化描述方面的潜力，同时关注传输安全和协同管理。另外，提高三维产品部件结构分类的自动化及通用化程度有助于建立高效便捷的数据处理体系。实景三维建设还需要完善数据管理标准和计算资源分配机制，以保证数据质量和安全性，以及计算效率和可靠性，从而更好地为社会发展提供科技支撑。

Addressing the limitations of grid map services, such as large volume, complex style updates, lengthy service update cycles, and intricate chart structures and visualizations, this paper explores a method system for the automated production, processing, and updating of chart services based on vector slicing technology.
This article designs a comprehensive automation framework encompassing data conversion, data recognition and update, data visualization preprocessing, data grading preprocessing, data slicing configuration, map configuration, and service update publishing, and updating. Leveraging vector slicing services, we have introduced electronic and paper chart mapping styles for nautical map services. We constructed a data integration governance rule system, which includes the automatic conversion of S-57 format using GDAL , automatic stratification of electronic map levels for multi-scale discrete data, automatic processing of adaptive chart expression styles, and the construction and processing of chart data symbol encoding systems. We studied the chart mapping and slicing process based on vector slicing technology, proposed a technical method for converting vector tiles to grid tiles, and enhanced the efficiency of data slicing publication. Furthermore, we investigated an incremental change detection algorithm for nautical chart data, enabling rapid identification of data alteration areas, followed by updating data and map services for dissemination.
The research content has been implemented in the “Civil Navigational Chart Maritime Service Platform”,offering services to the public and receiving unanimous acclaim from users. To validate the method's reliability, experimental comparisons were conducted under identical conditions for different service types, production, and updates. The findings indicate that vector slicing technology facilitates the automatic publishing and swift updating of nautical chart services, supporting features like data querying and hierarchical control. Compared to grid map services, the production efficiency of chart vector slicing services is increased by 1.75 times, with storage requirements reduced by 6.5 times. Notably, the service update offers approximately a 68% boost in production efficiency, proving the feasibility of the update approach.
The ocean represents a crucial strategic resource, with chart data serving as the digital manifestation of marine information. To elevate the accessibility, applicability, and timeliness of chart data, this article employs vector slicing technology and geographic information systems to examine the entire production system and update techniques for chart services, encompassing data governance, map configuration, service deployment, and data renewal. This significantly enhances the production efficiency of chart services, shortens the update and slicing duration for nautical chart services, ameliorates the current state of nautical chart services, augments the application value of nautical chart data, establishes a robust service foundation for maritime and transportation capabilities, and supports diverse maritime endeavors.

在人类社会的发展过程中，突发事件常常引发人们生活和行为的急剧变化，并可能对其产生持续性的影响。目前相关研究多为居民出行活动的总体趋势和整体特征，而在细分层面分析出行活动在时空维度上差异性的研究较少，且存在时空维度分离、缺乏整体性的问题。本文以美国旧金山新型冠状病毒感染流行为例，采用共享单车出行数据、兴趣点数据等进行长时间跨度的研究，利用k均值聚类和潜在狄利克雷分配模型，挖掘突发事件前后居民出行时空模式的特征及变化。结果表明：(1)事件暴发后，居民不同目的的出行活动时空模式发生了显著变化，通勤及娱乐出行的比例大幅下降，居民尝试用聚集程度较小的户外休闲娱乐代替聚集性娱乐，生活必需品购买及处理个人事务的出行比例上升，医疗需求大幅增加且该类型出行的早高峰开始时间提前。(2)随着时间的推移，突发事件的影响逐渐降低，人们的出行活动时空模式逐渐恢复至事件前的状态。研究成果可深化对风险和不确定性的认知，建立更全面的时空知识服务体系，为城市管理部门制定合理的应急管理策略提供参考。

The evaluation of tourism suitability is an essential prerequisite for the sustainable development of the tourism sector. As a unique destination with its diverse terrain and landscapes, Jiaozuo requires a comprehensive evaluation model that takes into account the influence of the terrain on tourism suitability. This study aims to provide a detailed analysis of tourism suitability in Jiaozuo, taking into consideration both human comfort and air quality indices.
The human comfort index, which reflects the level of comfort experienced by individuals under different meteorological conditions, plays a significant factor in determining tourism suitability. In Jiaozuo, the complex terrain results in variations in meteorological parameters such as temperature, humidity, and wind speed across different regions. To accommodate these variations, adjustments were made to the human comfort index based on terrain characteristics. Data on meteorological conditions were collected from various locations within the city spanning a decade. By combining this data with digital elevation models, we improved the accuracy of the human comfort index using the temperature vertical decrement rate. Subsequent spatial analysis was performed through inverse distance weighting interpolation to map the distribution of the improved human comfort index throughout the city.
Another crucial factor considered in this study was the air quality index, which measures the concentration of pollutants in the air. Air quality data, collected over six years from various monitoring stations in Jiaozuo,were analyzed to assess overall air quality and its impact on tourism suitability. The results revealed that while most parts of the city maintained satisfactory air quality, certain areas experienced pollution levels beyond acceptable limits, particularly during winter months.
Based on the comprehensive evaluation of tourism suitability in Jiaozuo, it was found that the city offers excellent conditions for touristic activities, particularly during spring, summer, and autumn. However, the tourism suitability in some areas may diminish due to lower temperatures and poorer air quality in winter. To enhance winter tourism suitability, tourists are advised to adopt appropriate protective measures, and local authorities should prioritize the improvement of air quality by implementing effective pollution control measures.
Overall, this study provides valuable insights into the tourism suitability of Jiaozuo, considering both human comfort and air quality indices. The findings of this study can inform tourism planning and management decisions, contributing to the sustainable development of the tourism industry in Jiaozuo and similar destinations.

The integration of Web-based 3D rendering technology with geographic information systems (GIS) has emerged as a significant trend in the evolution of 3D GIS. Notably, Web 3D scene visualization and editing technology have attracted considerable attention, given that editable large-scale 3D WebGIS applications are contingent on specific usage scenarios.
This paper delves into the methodology for constructing editable large-scale 3D scenes, focusing on dynamic spatial indexing and multi level of detail (LoD) techniques. It introduces the dynamic loose octree and half-edge folding LoD algorithm to the 3D Tiles construction process. In terms of model rendering precision, the spatial index built upon the loose octree offers superior rendering speed compared to the conventional octree. During dynamic model editing, the time investment required for generating quasi real-time LoD using the half-edge folding algorithm remains relatively low, while still delivering commendable frame rate performance and ensuring satisfactory model simplification outcomes. In conclusion, the paper presents the development and refresh of 3D Tiles that support modifiable scenes, showcasing experimental results that amalgamate three-dimensional artificial models, architectural data, mountain models, and the Stanford dragon model.
Findings indicate that: (1) the loose octree index adeptly meets dynamic update demands in terms of computational burden, exhibiting impressive precision in frustum culling;(2) the creation of quasi-real-time LoD via the half-edge folding technique adequately addresses the computational needs of LoD requirements in dynamic environments; (3) when integrated with established frameworks like Three.js and employing technologies such as Web Workers, the resultant 3D Tiles scene facilitates efficient task scheduling and rendering of 3D Tiles.
The Web-based 3D scene editing approach introduced in this paper lowers user barriers, amplifies interactivity and flexibility in 3D GIS applications, and fosters broader adoption of 3D GIS across diverse sectors. This advances real-time data synchronization and scene updates in urban digital twin systems, furnishing novel instruments and methodologies for urban planning, management, and simulation endeavors.

在全面推行“放管服”改革的政策背景下，使用信息化技术提高宅基地建房管理效率成为保障乡村振兴战略的重要因素。由于传统的业务工作方式存在着信息不对称、流程烦琐等问题，住房审批、安全监督等管理工作效率低下，难以保障农民居住用地需求。因此，本文以湖南省宅基地信息化建设工作为研究对象，提出了面向“放管服”改革的省级宅基地建房管理平台的设计与实现方案。结果表明，在湖南省规范宅基地管理工作、保障农村住房用地政策的约束下，已有1800多个乡镇使用平台来开展业务工作，应用覆盖率超过86%，取得了良好的应用效果。期望本研究能够为其他省份的宅基地建房管理工作信息化转型提供借鉴作用，从而全面强化国土空间规划管控，加快推进政务服务的全面信息化。

[Objective] Ground substrate surveying, as an emerging field in the survey and monitoring of natural resources, has seen extensive exploratory research and demonstration efforts by numerous domestic experts and scholars in recent years. These endeavors have focused on the classification and surveying of ground substrates. Consequently, a preliminary technical system for ground substrate survey methods has begun to form. Simultaneously, advancements in information technology related to ground substrate surveys have progressed in tandem. Research and exploration by multiple teams have paved the way for the informatization of ground substrate surveys. However, there is currently a lack of an integrated information system to support the digitization and standardization of the entire investigation process. There is an urgent need to establish a digital software system to achieve the digitization of the entire business process and the full lifecycle management of business data. This would support the creation of a unified foundation and a single set of data for surface substrate investigations.
[Method] To address this issue, this paper designs and develops a digital surface substrate investigation system, which includes the following aspects: (1) Based on the analysis of data content and characteristics during the ground substrate survey process, and considering the features of current mature databases, an integrated hybrid database storage model was developed. This model combines “cloud storage + PostgreSQL relational database + MinIO object storage + MongoDB database.” (2) Centered on the concept of “one cloud, one database, one platform, three application terminals, two integrations, and two safeguards,” a five-layer architecture was adopted to construct the overall technical framework of the ground substrate survey system. (3) Drawing from the technical route of “indoor research, field survey, database modeling, and platform services” for ground substrate layer surveys, the digital workflow and system functional composition for ground substrate surveys were designed. (4) Key technologies were developed, such as online services for public base map data and big data storage based on hybrid databases. (5) The digital ground substrate survey system was developed. This system includes: A mobile subsystem for digital ground substrate survey field data collection; A desktop subsystem for digital ground substrate survey data editing and mapping; and A web subsystem for digital ground substrate survey data management and services.
[Result] The digital ground substrate survey system effectively supports the main process informatization of ground substrate surveying, including project management, data collection, data editing, data aggregation, mapping, database construction, and services. It enhances the efficiency of base map data services, field data collection, and data editing and mapping, achieving unified storage and management of data throughout the entire workflow.
[Conclusion] As compliance software for ground substrate survey standards, it lays the foundation for the standardization of data and the unified construction of a national ground substrate survey database.

As climate change intensifies and extreme heat events become more frequent, forest fires are escalating globally. Yet, most existing systems are limited to singular simulations of forest fire propagation and few offer comprehensive representations of the scenario evolution during forest fire emergency responses.
This study introduces a multi-element, multi-event scenario evolution timeline model. Utilizing this model, a three-dimensional Geographic Information System (3D GIS) for simulating forest fire scenario evolution was developed using the open-source Web 3D visualization engine, Cesium. Symbols representing four key elements of forest fires—disaster factors, disaster bearing bodies, disaster resistant bodies, and disaster bearing environments—were designed. For each element, large-scale and small-scale visual symbols were created to accommodate varying observation scales and perspectives in the three-dimensional scene. Large-scale symbols include highly realistic three-dimensional models such as humans, aircraft, and houses. Small-scale symbols are simple, intuitive two-dimensional dot, line, or icon markers. Cesium’s particle system was employed to simulate fire, smoke, and various weather conditions like rain and snow. An enhanced 3D cellular automata model, integrating digital elevation model (DEM) elevation data, meteorological factors, and vegetation attributes, was used to mimic forest fire spread. The system’s front end was built using the Vue3 framework, while the back end leveraged the Spring Boot 3 framework. The GDAL library was utilized for raster and vector spatial data computations and processing, with PostgreSQL managing the database infrastructure.
The devised system adeptly replicates processes such as flame dispersion, firefighting team interventions, firebreak establishment, and aerial fire suppression. Its practicality and efficacy were substantiated through the real-world case of the “3.30” forest fire incident in Lushan, Sichuan. While the model generally accurately simulates forest fire spread, certain areas exhibited discrepancies, which could be manually rectified in near real-time. Testing indicated that the manual correction update rate for the fire spread range was under 5 seconds per iteration, satisfying the demands of real-time fire rescue command operations.
This system will provide decision support for forest fire combating and mitigating, help to improve forest fire emergency response capabilities, and reduce environmental and economic losses due to fire.

This thesis focuses on industry applications and proposes a framework for semantic modeling of geo-entity relationships 3D realistic geospatial landscape model, compatible with multiple domains.
[Objective] Semanticizing of geo-entities in 3D realistic geospatial landscape model is crucial for constructing a unified 3D spatiotemporal substrate for Digital China. This process facilitates efficient information circulation and sharing, promoting high-quality industry development. However, current semantic modeling of relationships faces challenges such as insufficient standardization, poor scalability, and difficulty in cross-domain application. Establishing and enhancing this framework along with optimizing the relational semantic construction method, are essential for advancing the entire chain from high-quality data production to multi-domain applications. Therefore, we need to create a robust and practical semantic modeling framework.
[Method] By analyzing the multi-dimensional characteristics of geo-entities in 3D realistic geospatial landscape model domains, this paper delves into the content of the geo-entity semantic model system, and underscores the necessity of developing a semantic modeling framework. We proposes a framework that considers multiple domains formed by defining relationship types and description rules. Standardized and generalized methods ensure the accurate and consistent expression and storage of relationship semantics among geo-entities. Building on this basic framework, we add a new relationship domain index or extend the relationship triggering feature conditions to accommodate different business applications.
[Result] Using the emergency disposal scenario of community gas pipeline leakage as an example, this paper validates and elaborates on the application of the semantic modeling framework in detail. The researcher integrates proprietary relationship types and corresponding description rules from extended domains like real estate rights, and emergency security, to construct a multi-domain compatible semantic modeling framework. Practical results demonstrate that the framework effectively supports cross-domain decision-making and application requirements.
[Conclusion] The proposed framework for semantic modeling of relationships achieves compatibility and extensibility across multiple industries, fostering interconnectivity and interoperability of geo-entity semantics information. It holds significant value and prospects for supporting the informatization construction and services of related domains, offering strong support for their advancement.

E-WID(Euler-number-based whole-object intersection and difference)三维拓扑关系模型能够表达三维空间简单、复杂目标间的拓扑关系，具有区分能力强、无理论缺陷等优势，但目前尚缺乏E-WID三维模型自动计算的相关研究。因此，利用Nef多面体三维数据模型能够表达带通道或空穴等复杂目标的特点，本文研究了一种基于Nef多面体的E-WID三维基本拓扑关系计算方法，其中包括三维基本空间数据间布尔运算结果的组件构建和欧拉数计算方法；并与已有方法进行对比。结果表明，本文方法实现了三维空间数据间的E-WID三维基本拓扑关系自动计算，并能够准确区分。

[Objective] Enhancing the efficiency and effectiveness of the land use approval process through digitalization and intelligent technologies is crucial for consolidating efforts in natural resource management, specifically achieving “dual unification”. This study tackles challenges posed by fragmented data management and the complexity of policy retrieval within the land use approval workflow. By leveraging the ontology of land use approval processes, we have developed a collaborative framework that integrates the construction of a knowledge graph with intelligent question-and-answer (Q&A) capabilities. This framework is designed to support and streamline land use approval activities, providing a robust decision-support tool that addresses issues related to weak business associations and difficult policy access.
[Method] The methodological approach involves systematically extracting and integrating information from various data sources relevant to land use policies and approval procedures. Utilizing advanced information extraction techniques and graph construction algorithms, we built a dynamic knowledge graph encapsulating the complex dependencies and regulations governing land use. Additionally, a knowledge retrieval-augmented generation model was developed to facilitate sophisticated Q&A interactions, allowing users to engage with the system via natural language queries and receive accurate, context-aware responses. This integrated framework was implemented within an intelligent service platform tailored for land use approval, and its effectiveness was assessed through a qualitative comparative analysis against traditional search engines, such as Baidu.
[Result] The implementation of the proposed framework led to the successful development of an intelligent service platform that significantly enhances the land use approval process. The constructed knowledge graph introduces a novel organizational structure for land use-related information, enabling seamless integration and retrieval of policy data. The intelligent Q&A system outperforms conventional search engines in delivering precise and relevant answers, demonstrating its ability to comprehend and process complex queries within the land use domain. The comparative analysis indicates that the platform substantially improves the accessibility and usability of policy information, thereby facilitating more informed and timely decision-making by approval personnel. Furthermore, the framework’s capability to systematically organize and leverage domain-specific knowledge highlights its potential to transform traditional land management practices into more streamlined and intelligent operations.
[Conclusion] In conclusion, this study presents an innovative approach to overcoming the inherent challenges in the land use approval process through the collaborative construction of a knowledge graph and the implementation of an intelligent Q&A system. The developed framework not only offers a new paradigm for knowledge organization within the land use sector but also provides a practical tool that enhances decision-making capabilities. Despite the significant advancements demonstrated, limitations remain in the automation of knowledge graph construction and the sophistication of Q&A interactions. Future research will focus on increasing the automation levels in knowledge graph development and expanding the applicability of the Q&A system to encompass a broader range of business scenarios. By further integrating these technologies with existing natural resource and land use planning systems, the framework aims to strengthen digital and intelligent governance capacities, ultimately contributing to more efficient and effective land management practices.

Drought is a significant factor that affects vegetation phenology and growth. The occurrence of drought events in our country exhibits a widespread and frequent trend, which has resulted in severe negative impacts on the ecological environment, agricultural production, and socio-economic development of our country. Particularly, due to the imbalance of surface water balance and frequent occurrence of drought events, it severely affects the sustainable development of the ecological environment. Drought can affect vegetation through various pathways, leading to the deterioration of the regional ecological environment. Drought can impact vegetation growth by altering vegetation respiration and inhibiting photosynthesis, and the occurrence of extreme drought events poses a serious threat to vegetation growth. The unique geographical environment of Qinghai-Tibet Plateau has created extremely sensitive and volatile climate changes, as well as extremely fragile ecosystems.
Studying the growth behavior of surface vegetation is crucial for understanding the complex response of alpine ecosystems to climate change. Vegetation is an important indicator for measuring the state of the ecological environment. Once the ecological environment changes, vegetation will quickly respond to the outside world through its own physiological reactions. Traditional research has mainly focused on the interaction between drought and phenology and their impact mechanisms, the impact of drought on ecosystems and biodiversity, the impact of drought on climate systems and water balance, etc. However, there is little research on whether the phenological period affected by drought in SPEI (standardized precipitation evapotranspiration index) is advanced or extended.
The method used in this article utilizes multi-year AVHRR 15-days products and the maximum value synthesis method MODIS 15-day same time resolution products to generate a VIP (vegetation index phenology) dataset. The annual time series dual-band enhanced vegetation index EVI2 (2-band enhanced vegetation index) is fitted using a segmented logistic fitting model algorithm. The curvature change rate method is used to extract phenological indicators from the logistic fitting, in order to invert key phenological parameters during the green-up and withering periods of alpine grasslands in Qinghai Lake Basin. The phenological trend is analyzed by combining the multi-scale standardized precipitation evapotranspiration index (SPEI).
The results show that the long-term changes in phenology of alpine grasslands in Qinghai Lake Basin are diverse. Meadow green-up and steppe dormancy are significantly advanced, while the phenological changes during meadow dormancy and steppe green-up are not significant. The impact of SPEI06 in April on meadow and steppe green-up is most significant, contributing 11.56% and 19.36% of interannual changes respectively. The impact of SPEI12 in August and SPEI06 in April on meadow and steppe dormancy is most significant, contributing 10.89% and 25% of interannual variation respectively.

在城市规划、公共管理、防灾减灾和导航等领域，高精度的城市建筑物三维信息发挥着重要作用。传统的建筑物高度信息获取方法具有成本较高、精度较低、时效性差的缺点。随着亚米级的高空间分辨率遥感卫星的发展和普及，基于遥感卫星立体像对数据反演建筑物高度信息的方法受到广泛关注。基于Geo Eye-1卫星数据，本文提出了一种自动化程度较高的建筑物高度信息提取方法。首先，利用有理函数模型对Geo Eye-1立体像对数据进行几何校正，生成数字表面模型；然后，基于多窗口滤波方法，在无须辅助数据的情况下，利用数字表面模型生成数字高程模型，通过叠置分析提取建筑物的高度信息；最后，根据建筑物轮廓数据绘制研究区建筑的高度分布图。研究结果表明，该方法在多种地表类型上提取出建筑物的高度信息具有较高的精度，具有操作简单、处理速度快等特点。

不一致性探测对提高多尺度点–线目标拓扑关系一致性处理的科学性和效率具有十分重要的意义。道路和居民地作为基础地理空间数据中两类最重要的地图要素，两类要素之间拓扑关系在不同尺度地理空间数据中的不一致性问题十分突出。本文以同名点状居民地与线状道路为例，研究了多尺度地理空间数据的拓扑关系不一致性问题，提出了基于拓扑距离的多尺度点–线目标拓扑关系不一致性探测方法，并进行了实验验证。研究表明，本文方法识别出的拓扑关系不一致性结果，可以作为不同尺度数据中点–线目标拓扑关系一致性处理的依据。