Traditional 3D geographic scene reconstruction methods rely on single data sources for geometric restoration, struggling to effectively integrate non-geometric information like text and semantics. This severely hinders reconstruction feasibility in scenarios involving sparse data or non-geometric descriptions. Advances in generative artificial intelligence (AI) for multi-modal data understanding and generation offer new solutions. However, applying generative AI to this task still faces two core challenges: the conflict between model output randomness and reconstruction accuracy, and between model generality and the generation of diverse, professional content within geographic scenes. To address these challenges, this study proposes a framework guided by geographic entity semantics and powered by multi-modal generative models. It first integrates multi-modal inputs for semantic enhancement, then generates 3D entities under geometric and domain-enhancement constraints, and finally completes scene integration using spatial pose constraints. A case study demonstrates the method’s versatility across various data combinations. It not only reconstructs scenes from text-only inputs, addressing the feasibility challenge, but also generates high-precision professional entities via rapid domain enhancement. Furthermore, adding geometric constraints yields significant improvements in reconstruction accuracy and visual realism, with user evaluations confirming its comprehensive superiority over traditional parametric methods.

Augmented reality (AR) scene modeling with virtual-real integration is an effective way to enhance users’ perception and understanding of geographic spaces. However, the existing modeling methods focus on precise virtual-real alignment in static scenes using single-frame images, leading to inefficiencies in dynamic scene modeling and low accuracy in virtual-real integration. This paper proposes a fast modeling method for AR dynamic scenes with spatio-temporal semantic constraints. By thoroughly analyzing spatio-temporal semantic constraint rules in AR dynamic scene modeling, a keyframe extraction algorithm based on a synchronized spatio-temporal semantic distance measurement model was designed. A rapid spatio-temporal interpolation model for AR dynamic view poses with spatio-temporal semantic association was established, and a real 3D scene-driven fast twin modeling method for AR dynamic scenes was proposed. Experimental results show that the proposed method reduces redundant image matching computations by 87.53% while maintaining virtual-real registration accuracy above 1°. This method enables accurate sampling of keyframes with spatio-temporal homogeneity, avoids redundant transmission of large volumes of frame image data, and improves AR dynamic scene virtual-real registration efficiency while maintaining accuracy. Furthermore, the spatial semantic information in real 3D scenes effectively guides fast AR dynamic scene modeling.

Accurate and rapid localization can improve geographic information system (GIS) tasks to support disaster rescue and resource exploration in field operations. However, the existing localization methods suffer from high costs, low efficiency, and poor accuracy of long-distance targets due to environmental factors like terrain and landforms. Therefore, we propose a multilevel localization framework based on mobile phones for different field scenarios. First, we investigated a rapid localization method constrained by scene contextual features in the field with salient features. Second, we designed a single-point localization method that combines DEM data and mobile phones when high-precision DEM data are available for the region. Third, we studied a map-matching corrected joint localization with mobile phone images in the field lacking salient features and high-precision DEM data. Finally, we developed a prototype system for field localization and selected a forest scene for experimental analysis. The results showed that the proposed mobile phone-based localization framework supports long-distance localization in the field. The localization accuracy in three different field scenarios is within 100 meters, and the localization efficiency reaches a minute level, which can effectively support the convenient, rapid, high-precision, and long-distance localization tasks in field scenes.

Attractive cycling routes can effectively promote active mobility, thus reducing the twin pressures of the population boom and the greenhouse effect. However, the existing approaches for cycling route recommendation primarily concentrate on identifying the most efficient routes while ignoring the urban spatial context, which is essential to meet the user’s particular preferences. This article proposes a novel method for informed sampling and recommending cycling routes leveraging crowd-sourced trajectories with weighted-latent Dirichlet allocation (WLDA). Precisely, spatial context mapping, incorporating a weighting mechanism into LDA, latent topics mining, and cycling route recommendation based on informed sampling are introduced. We collected 1,016 cycling trajectories around Cologne, Germany, for experimental analysis. The experimental results show that the three latent topics within the trajectories, leisure, city, and green tours, are clearly presented in the line density analysis. The insightful recommendation for unfamiliar cyclists could also be actively sampled upon the WLDA model. These findings suggest that our approach could shift the route recommendation paradigm from GIS analysis to a semantic mining perspective, yielding highly interpretable results and offering novel research avenues for applying machine learning in route planning.

Efficient geovisualization is beneficial for understanding geospatial phenomena, an important research direction for GISers and Cartographers. However, the current research on geovisualization overemphasizes the visual effects while neglecting the prominent representation of crucial information and failing to consider the user’s cognitive workload of information processing. Following the laws of visual perception of the human eyes, this article proposes a visual attention-guided augmented representation approach of geographic scenes that involves area of interest computation, background simplification, and compound graphic variables. Finally, we select bridge stress visualization as a case study for experimental analysis. The experimental results of eye-tracking show that augmented representation could draw the participants’ attention to areas of interest in a short time, increasing their duration of fixations and the accuracy of completing given tasks. These findings suggest that our approach can enhance geographic scenes’ cognitive efficiency, offers a new idea for the theoretical studies of geovisualization, and holds promising potential for broader application in various geographical phenomena visualization.

An important step in implementing geospatial digital twins is to enhance the expressiveness of virtual geographical scenes for the physical world. However, the existing virtual geographical scenes cannot quickly express the dynamically changing geographic environment for remote users due to the inefficient handling of modeling processes, user perception, and remote sharing. The research analysed the concept and characteristics of geospatial digital twins, and constructed the virtual geographical scene ontology, based on which we developed geographical spatiotemporal semantic rules and designed a dynamic annotation algorithm to enhance the representation of virtual geographical scenes. Finally, we investigated a real-time transmission method of panoramic video based on 5 G and used immersive virtual reality (IVR) to realize the user experience of remote immersion in geographical scenes. We selected a specific geographic environment containing multiple typical geographic entities to develop three prototype systems for experimental analyses. The results showed that the proposed method enabled users to view the virtual geographical scene on a VR device. The average latency for this process was 14.72 seconds. Compared with the virtual geographical scenes constructed by traditional methods, the experiments showed the proposed method advantageous in comprehensiveness, timeliness, and photorealism and abilities to enhance the user’s geographical scene perception.

Public’s rational flood mitigation behaviors depend on accurate perception of flood risks. The use of natural language for flood risk perception is an effective approach, and it is critical to ensure the accuracy and comprehensibility of the flood information provided by the system in natural language dialogues. This study presents a framework for large language model (LLM) that is constrained by flood knowledge and can interact with geographic information system (GIS), aimed at enhancing the public’s perception of flood risks. We tested the performance of LLM within this framework and the results demonstrate that LLM can generate accurate information about floods under the constraints of entities and relationships in the knowledge graph, and interact with GIS to produce personalized knowledge through real-time coding. Furthermore, we conducted flood risk perception experiments on users with different cognitive levels. The results indicate that using natural language dialogue can narrow the differences brought about by cognitive levels, allowing the public to equally access knowledge related to flood events.

The detail and representation of a spatial layout varies with scale. This affects an individual’s learning effectiveness and understanding, in turn directly influencing their behavior in a fire evacuation. However, the impact of layout learning methods with different spatial scales on fire evacuation behavior, and the relationship between spatial cognition and evacuation effects, remains unclear. We conducted spatial layout learning across three scales with 81 participants and simulated a fire evacuation scenario in a mobile virtual reality for groups. We collected evacuation decision-making and user experience questionnaires as supplementary data. The results demonstrate that small-scale learning objects are the easiest for participants to understand in terms of spatial layout and relationships, but their performance in fire evacuation is poor. Large-scale learning objects significantly improve participants’ evacuation efficiency. Spatial layout learning plays a crucial role in fire evacuation outcomes, but traditional spatial knowledge acquisition measurement methods cannot predict fire evacuation performance. This study sheds light on how spatial cognition influences fire evacuation behavior and provides a more reliable fire evacuation simulation method based on mobile virtual reality (MVR).

As an important application of virtual geographic environments (VGEs), virtual disaster scenes are essential in enhancing the public’s risk awareness. However, existing virtual disaster scene visualization methods lack expert guidance and fail to meet the public’s requirements, resulting in an ineffective public understanding. Therefore, this paper proposes a knowledge-guided disaster scene 3D visualization framework. First, the public’s demand for disaster scene visualization is analyzed, and a geographic knowledge graph of disaster scenes is constructed. Second, through the guidance of the knowledge graph, the virtual disaster scenes are fusion modeled and suitability represented. Third, a diverse organization and adaptive scheduling method of disaster scene data for multi-computing devices is established. Finally, we developed a prototype system for disaster scene visualization, selected a typical disaster, and conducted cognitive experiments with eye-tracking technology. The results show that the proposed method can effectively support the adaptive visualization of virtual disaster scenes for four computing devices and maintain an efficient frame rate. In addition, compared with other disaster scene visualization methods, our framework incorporates semantic knowledge of scene, user, demand, and space. It can effectively convey disaster information and help the public cognize disaster risks and has significant advantages in modeling standardization, personalization, and adaptability.

Virtual scenes can present rich and clear disaster information, which can significantly improve the level of public disaster perception. However, existing methods for constructing scenes of debris flow disasters have some deficiencies. First, the construction process does not consider public knowledge, which makes it difficult for the constructed scenes to meet the requirements of the public. Second, the scene representation emphasizes visual effects but lacks augmented visualization, leading to scarcity of semantic information and inefficient public perception. In this paper, the optimal selection of scene objects, semantic augmentation through the combination of various visual variables and dynamic augmented representation are discussed in detail. Finally, a debris flow that occurred Shuimo town is selected for experiment analysis. The experimental results show that most people are unaware of the risks posed by debris flow disasters. The public is more concerned about the consequences of a disaster than its spatiotemporal process, especially when the consequences are related to their own interests. Furthermore, an augmented representation can increase the amount of semantic information of scene objects, which is essential for enhancing public understanding of the causes, processes and effects of debris flows and thereby changing people’s attitudes and enhancing their risk perception.

Simulation and subsequent visualization in a network environment are important to glean insights into spatiotemporal processes. As computing systems become increasingly diverse in hardware architectures, operating systems, screen sizes, human–computer interactions and network capabilities, effective simulation and visualization must become adaptive to a wide range of diverse devices. This paper focuses on the optimization of simulation and visualization analysis of the dam-failure flood spatiotemporal process for diverse computing systems. First, an adaptive browser/server architecture of the dam-failure simulation application was designed to fill the hardware performance and visualization context gap that exists within diverse computing systems. Second, a data flow and an optimization method for multilevel time-series flood data were given to provide more support to network simulation, visualization and analysis on diversified terminals. Finally, a user interaction friendly and plugin-free prototype system was developed. The experiment results demonstrate that the methods addressed in this paper can cope with the challenge in simulation, visualization and interaction of a dam-failure simulation application on diversified terminals.

A novel method called knowledge-guided spatio-temporal consistent correlation analysis (KSTCCA) was developed to discover reliable deformation features induced by multiple factors based on multimode landslide monitoring data. Compared to conventional approaches, KSTCCA integrates both temporal and spatial correlation analysis to improve the consistency of deformation patterns and capture the spatio-temporal heterogeneities in multimode monitoring data. KSTCCA considers both the landslide deformation mechanisms and the relationships between different influential factors as knowledge. Moreover, the method extracts the morphological structures of monitoring curves based on a seven-point approach and identifies knowledge rules using the k-means clustering method. Under the guidance of prior knowledge, a spatial correlation analysis is conducted based on support vector regression, and a temporal correlation analysis of the time lag is carried out based on the morphological structure features. Finally, three kinds of typical monitoring data, including deformation, rainfall, and reservoir water level data collected in the Baishuihe landslide area, China, are used for experimental analysis to verify the validity of the proposed method.

It is difficult to obtain accurate simulation results without observation data. So using real-time dynamic observation data in the simulation process has become an academic frontier of international research. This paper is a probing research on the data-driven adaptive modeling and automatic refactoring methods of flood routing simulation. A cellular automata (CA) data-driven flooding model was developed using the Hunhe River in Shenyang City as a case study. The proposed model can increase the accuracy of simulations by calculating differences in the water stages using high temporal resolution observational data. Meanwhile, corresponding parameter analysis was carried out based on the proposed CA model and the best lagging time between simulation and observation was discussed.

A procedural modelling method based on model combination and spatial semantic constraint is proposed to realize the automatic modelling of high-speed railway scenes for management decisions and scientific experiments at different stages. The construction and description of basic-element models were first discussed in detail according to the fixed characteristics of the geometric appearances of and the spatial relationships between the components of high-speed railways. Then, spatial semantic constraint rules and scene mapping and instantiation methods were designed to accurately and rapidly integrate various basic-element models for automatically generating high-fidelity three-dimensional high-speed railway scenes. Finally, a prototype modelling system was developed to implement preliminary experiments. The experimental results show that the method proposed in this article is suitable for the automatic generation of virtual high-speed railway scenes by combining sophisticated basic-element models in a seamless manner. Modelling operations and professional knowledge are decoupled to reduce the complexity and difficulty of multidisciplinary collaborative modelling, which improves the modelling efficiency of virtual high-speed railway scenes.

Chapinghe Barrier Lake was the largest among the barrier lakes formed in the aftermath of the magnitude 5.12 Wenchuan Earthquake. A rapid quantitative method for the evaluation of potential risk to lives and properties downstream was of the utmost importance for disaster management. The proposed method is based on spatiotemporal simulation using different dam-break scenarios and downstream hazard distribution analyses. This article adopts a cellular automata (CA) model to synthetically integrate multiple sets of geographic layers, including those containing the models needed for routine computation of flood hazards and those needed for vulnerability analysis of the people living downstream. A CA-based simulation and analysis method integrating hydrologic/hydraulic mechanisms is herein introduced, and relevant techniques are investigated. Our prototype experiment demonstrates that the proposed CA-based flood-hazard model can be conveniently integrated into a digital earth system and can further provide real-time simulation analyses of dam-break flood risks.

To improve the efficiency of planning and designing silt dam systems, this article employs theories and technologies of collaboration and distributed virtual geographic environments (VGEs) to construct a collaborative virtual geographic environment (CVGE) system. The CVGE system provides geographically distributed users with a shared virtual space and a collaborative platform to implement collaborative planning. Many difficulties have been found in integrating data resources and model procedures for the planning of silt dam systems because of their diversity in heterogeneous environments. Unlike most of the current distributed system applications, the proposed CVGE system not only supports multi-platform and multi-program-language interoperability in the dynamically changing network environment, but also shares programs, data and software in the collaborative environment. Based on creating a shared 3D space by virtual reality technology, agent and grid technologies were tightly coupled to develop the CVGE system. A grid-based multi-agent system service framework was designed to implement this new paradigm for the CVGE system, which efficiently integrates and shares geographically distributed resources as well as having the ability to build modelling procedures on different platforms. At the same time, mobile agent computing services were implemented to reduce the network load, process parallel tasks, enhance communication efficiency and adapt dynamically to the changing network environment. Using Java, JMF (Java Media Framework API), Globus Toolkits (GT) core, Voyager, C++, and the OpenGL development package, a prototype system was developed to support silt dam systems planning in the case study area, the Jiu-Yuan-Gou watershed of the Loess Plateau, China. Compared with the traditional workflow, the CVGE system can reduce the workload by between one third and a half.