[Objectives] This study addresses the critical challenges in typhoon disaster knowledge services, which are often hindered by "massive data, scarce knowledge, and limited services." The core objective is to rapidly distill actionable knowledge from vast datasets to enhance disaster management efficacy and mitigate typhoon-related impacts. Large Language Models （LLMs）, renowned for their superior performance in natural language processing, are leveraged to deeply mine disaster-related information and provide robust support for advanced knowledge services. [Methods] This research establishes a typhoon disaster knowledge service framework encompassing three layers: data, knowledge, and service. [Results] For the data-to-knowledge layer, an LLM-driven （Qwen2.5-Max） automated method for constructing typhoon disaster Knowledge Graphs （KGs） is proposed. This method first introduces a multi-level typhoon disaster knowledge representation model that integrates spatiotemporal characteristics and disaster impact mechanisms. A specialized training dataset is curated, incorporating typhoon-related texts with explicit temporal and spatial attributes. By adopting a "pre-training + fine-tuning" paradigm, the framework efficiently transforms raw disaster data into structured knowledge. For the knowledge-to-service layer, an LLM-based intelligent question-answering system is developed. Utilizing the constructed typhoon disaster KG, this system employs Graph Retrieval-Augmented Generation （GraphRAG） to retrieve contextually relevant knowledge from the graph and generate user-specific disaster prevention and mitigation guidance. This approach ensures seamless conversion of structured knowledge into practical services, such as personalized evacuation plans and resource allocation strategies. [Conclusions] The study highlights the transformative potential of LLMs in typhoon disaster management and lays a foundation for integrating LLMs with geospatial technologies. This interdisciplinary synergy advances Geographic Artificial Intelligence (GeoAI) and paves the way for innovative applications in disaster service.

[Objectives] In response to the challenges of geometric priority, semantic weakening, and cross-software semantic loss in the practical application of Building Information Modeling (BIM) according to the IFC standard, this study leverages the knowledge graph and its inference algorithm (TransE) to establish a network semantic representation of BIM model information. By enhancing the geometric and semantic correlation of the model, it addresses the issue of semantic loss during cross-platform interactions. [Methods] Using the Revit software library's built-in three-layer building model as the experimental object, the TransE model is applied to extract semantic information from BIM. BIM semantic information is first categorized into three types: component semantics, association semantics, and coordinate semantics. IfcEntity dynamic labels are assigned to component nodes, while static relationship attribute labels are assigned to association nodes. A total of 2 453 BIM semantic nodes and 14 844 association relationships are extracted. [Results] The experiment results demonstrate that: (1) The knowledge graph effectively represents BIM model components and their complex relationships; (2) By comparing the TransE model performance index (MRR\Hits@n) under different parameter combinations, it was found that: The embedding dimension is directly proportional to model performance and the learning rate is inversely proportional to model performance; (3) The optimal model performance is achieved when the embedding dimension is set to 200 and the learning rate to 0.0005; (4) By querying the system for all component nodes and verifying the results, the success rate of extracting semantic information from BIM components was found to be 94.47%. [Conclusions] The method proposed in this study is effective for extracting semantic information from BIM and conducting deeper semantic analysis. The findings provide a novel approach for semantic transformation in the integration of BIM and GIS.

[Objectives] Discrete Global Grid System (DGGS) is a hierarchical structure with seamless global coverage. It functions similarly to an electronic table covering the Earth, supporting the processing and analysis of heterogeneous geospatial data. However, the grid is essentially a multi-scale raster structure, and integrating geographic vector data into it remains a challenge in both research and application. Vector data includes points, lines, and polygons, among which vector line gridding is a fundamental problem. Most existing solutions express vector lines using the center lines of grid cells on a planar grid. However, when extended to spherical surfaces, the accuracy of vector data modeling decreases, making it difficult to meet application requirements. [Methods] This paper proposes a high-precision modeling method for vector lines in DGGS. First, a hexagonal global discrete grid is constructed based on the rhombic triacontahedron, which offers a higher degree of conformity to the sphere. Three adjacent rhombic faces are combined to form a composite structure, and a three-axis integer coordinate system is established to describe the spatial positions of hexagonal elements. Based on the grid cells corresponding to the start and end points of the vector line, optimal direction codes are determined to reduce the search range. The great arc of the vector line passing through the cells is identified using a neighborhood encoding operation. The resulting model is constructed based on the connecting line between grid centers, and a method for processing cross-surface vector lines is proposed. Finally, grid vertices are introduced as structural elements to enable vector line modeling using multiple structural elements, further improving the accuracy of hexagonal grid-based vector line modeling. Experiments show that the proposed method successfully models the grid representation of major coastlines across different continents. The results ensure that the grid model intersects with the original vector line topology, avoiding topological errors where original vector lines do not intersect with any grid cells.[Results] Compared with planar grid modeling, the proposed method achieves significantly higher accuracy in vector line gridding across various coastal regions worldwide. The modeling results demonstrate strong stability and are nearly unaffected by the resolution of the original vector data. Moreover, the method maintains an efficiency advantage, even after complex geometric operations on the spherical surface. [Conclusions] To address the geometric accuracy loss and topological distortion issues in traditional vector data grid modeling, this paper proposes a high-precision spherical grid modeling method. The approach shows strong potential to support the conversion of vector data to grid-based isomorphic representations.

[Objectives] Knowledge graphs, as a cutting-edge technology for integrating multimodal data sources, have garnered significant attention in the GIS domain. These graphs are typically constructed using graph databases. However, mainstream graph databases still face challenges in effectively organizing and analyzing geospatial-temporal data. [Methods] To address this issue, this paper proposes an approach to modeling spatiotemporal semantics and query optimization that bridges graph and spatial data engine implemented within relational databases. In the graph database, geographic entities are stored as lightweight placeholder nodes (storing only mapping IDs) and linked to spatiotemporal index nodes (such as time trees and Geohash encodings) to enhance aggregation capabilities. Meanwhile, complete geospatial-temporal objects are stored in a relational database, while table partitioning strategies are employed to improve retrieval efficiency. This approach uses unified identifiers and JDBC for routing geographic entities across the databases. When users invoke pre-registered spatiotemporal functions in the graph database, a query rewriter transforms the graph queries into SQL statements based on entity identifiers, pushes them to the relational database for processing, and returns the results to the graph query pipeline. Additionally, a two-phase commit protocol ensures data consistency across the heterogeneous databases. [Results] We implemented a prototype system integrating Neo4j and PostGIS and conducted experiments on query and storage efficiency using a multisource spatiotemporal dataset from Shenzhen (including taxi trajectories, bike-sharing trajectories, road networks, POIs, and remote sensing imagery). Compared to mainstream graph database systems (e.g., Neo4j and GraphDB), our approach significantly improves performance for geospatial-temporal queries, reducing response times by 1~2 orders of magnitude in complex computational scenarios and enabling raster computations unsupported by native graph databases. By leveraging lightweight graph nodes and PostGIS data compression, storage space is reduced by approximately 3~5 times. Compared to virtual knowledge graph systems (e.g., Ontop), our method shows minimal differences in spatial query performance and storage overhead, while achieving notably faster response times for large-scale spatiotemporal queries. [Conclusions] Compared to existing methods, our approach leverages existing graph databases to construct materialized spatiotemporal knowledge graphs, enhancing modeling flexibility and query efficiency for geospatial-temporal data. It also supports user-defined extensions to the geospatial-temporal function library, offering a novel framework for efficiently managing and analyzing such data within knowledge graphs.

[Objectives] Accurate identification of turbidity distribution in urban rivers is crucial for understanding urban water quality, assessing pollution levels, and optimizing water resource management. Current remote sensing inversion methods for water quality typically rely on correlation coefficients to select sensitive spectral bands or combinations for modeling. However, the broad spectral range and limited number of bands in common satellite data introduce uncertainties in identifying optimal bands, thereby constraining model accuracy. [Methods] This study proposes a turbidity inversion method based on a Convolutional Neural Network (CNN) architecture comprising four convolutional layers, two pooling layers, and four fully connected layers, with the final layer outputting turbidity values. Using Planet satellite data, field-measured turbidity, and spectral data from the Dongfeng Canal and Xiong’er River in Zhengzhou City, China, the CNN model was implemented for urban river turbidity inversion. Performance comparisons were conducted against two regression analysis methods and three classical machine learning approaches to generate spatial turbidity distribution maps. [Results] The CNN model achieved a coefficient of determination (R²) of 0.908 and a Root Mean Square Error (RMSE) of 0.410 NTU in the study area, outperforming the best regression model and the best classical machine learning method by 39.6% and 6.5%, respectively. Validation through visual inspection, field sampling, and laboratory analysis confirmed consistency between CNN-derived turbidity maps and ground truth data. The study area exhibited a mean turbidity of 3.52 NTU, with a standard deviation of 1.003 NTU and a coefficient of variation of 0.28. [Conclusions] These findings indicate that the convolutional neural network effectively captures complex nonlinear and high-dimensional data relationships in remote sensing images, improving the accuracy of turbidity parameter inversion.

[Objectives] With the deepening of urbanization and intensified market competition, long working hours have become a pervasive social issue, posing challenges to both workers' physical and mental health and to urban sustainable development. Current studies on urban residents' work activities predominantly rely on questionnaire survey data, which suffer from limited sample sizes and a lack of in-depth exploration into long working hours in megacities. [Methods] This research utilized mobile signaling data from Beijing, collected between November and December 2019, to identify stay points using a threshold rule method. Residential and workplace locations were determined through a time-window approach, and users' working hours were extracted. The study then examined the spatial distribution patterns of long-working-hours employees (defined as those working over 40 hours per week) and investigated spatial characteristics across various gender and age groups. Finally, the study also explored the characteristics of long working hours in different employment clusters in Beijing. [Results] The findings reveal that 47.1% of Beijing's workforce engages in long working hours (weekly working hours ≥40 hours), with an average weekly working duration of 48.86 hours. Spatial analysis demonstrates a polycentric agglomeration pattern, concentrated in major employment hubs such as the CBD, Financial Street, Zhongguancun, and Yizhuang. Significant disparities exist across gender and age groups. Male employees work an average of 49.62 hours per week, 1.5 hours more than their female counterparts (48.12 hours). Among male age groups, those aged 20~29 have the longest average weekly working hours at 50.68 hours. In contrast, although women aged 30~39 constitute the largest proportion of the female workforce (22.13%), their average weekly working hours are the lowest, at 47.59 hours. The characteristics of overtime work in different employment clusters show a clear pattern: the CBD and Zhongguancun have a higher number of overtime workers, while Yizhuang stands out with the highest proportion at 58.0%. Wholesale and logistics hubs such as Xinfadi and Majuqiao exhibit the most intensive work schedules, with average weekly working hours exceeding 50 hours. [Conclusions] This study provides rich empirical evidence for understanding the phenomenon of long working hours in Beijing. The results offer data-driven support for optimizing labor time policies, contributing to urban sustainable development and social equity.