High-fidelity 3D tree mesh models have broad applications in virtual geographic environments, forest ecology, and digital entertainment. Although laser point clouds provide precise data, existing point cloud-based methods struggle with accuracy and completeness due to tree structural complexity, leaf occlusion, and data gaps. To address these challenges, we propose a novel framework that integrates data-driven and morphology-knowledge-driven techniques for generating high-fidelity tree mesh models with accurate trunk and overall crown morphology. The proposed framework consists of three main stages. First, in the trunk separation stage, non-scattering feature clustering is employed to extract the main trunk points. Second, in the robust trunk mesh generation and optimization stage, a coarse-to-fine skeleton extraction and refinement method is proposed, followed by weighted Levenberg–Marquardt cylindrical fitting to generate the trunk geometry. Finally, we propose a novel method that integrates improved space colonization with Alpha Shape constraints to overcome challenges of reconstructing fine branches within the crown, thereby generating complete tree mesh models with accurate trunk and crown morphology. The method was evaluated on laser-scanned, 3D Gaussian, and synthetic point clouds, and benchmarked against state-of-the-art techniques. Results demonstrate superior accuracy and robustness across diverse point cloud types, with strong resilience to noise and incomplete data.

In recent years, virtual geographic environments have played a crucial role in enhancing public risk perception through disaster simulations. Previous studies, such as Zhu et al.1, have proposed knowledge-driven visualization frameworks, offering valuable insights into public risk perception. Building on this foundation, this paper further focuses on the automated construction of knowledge organization and cross-platform adaptive presentation to better meet the public’s needs for understanding and participation in settings without professional guidance. This framework begins with a thorough analysis of the public’s specific needs for disaster visualization, using large language model (LLM) to extract triples and construct a detailed knowledge graph containing disaster geographic information. Under this knowledge framework, we realized precise modeling of virtual scenes and context-adaptive representation based on the theory of virtual geographic environments (VGEs). Then, we designed a cross-platform data organization and dynamic scheduling algorithm to enable content presentation across diverse devices. Finally, we conducted three groups of experiments using typical disaster cases, with user cognition assessed via eye-tracking. The experiment results indicate our method supports adaptive, smooth visualization on multiple platforms effectively. Compared to traditional approaches, it significantly improves debris flow disaster information dissemination efficiency by integrating scene context, user interest, demand response and spatial intelligence, offering advantages in standardized modeling, personalization and adaptive optimization, thereby enhancing public debris flow disaster information perception.

Coupling dynamic data and mechanistic models for fine-scale flood simulation in a 3D virtual geographic environment

ABSTRACT Data-driven landslide susceptibility assessment (LSA) remains unconvincing owing to the disconnection from the modelling to physical cognition of landslide causation. Most models are mere good fits to certain datasets and can produce unexpected bias in their prediction, misleading high-risk area zoning. To validate data-driven LSA, this study delved into the innate interactions between input landslide features and predictions by model explanation and compared feature permutation results with landslide statistical priors. Furthermore, multi-temporal interferometric synthetic aperture radar (MT-InSAR) derived ground deformation was applied in an alpha pixel fusion and growth method for LSA enhancement. This study took Hong Kong as the research area, employed extreme gradient boosting (XGBoost) for LSA, and utilized Shapley additive explanations (SHAP) method for model explanation. The mean Shapley values – indicating feature importance – for slope, stream power index (SPI) and land use are 1.31, 1.12, and 0.67, respectively. This aligns with the landslide feature permutation derived from prior statistics, verifying the model prediction reliability. The applied InSAR enhancement results in a 13% increase of ‘(very) high’ landslide susceptibility areas. Additionally, LSA results and ground deformation map were cross-validated in the virtual geographic environment. This study improves the reliability of data-driven LSA through model explanation and InSAR enhancement.

A cultural landscape can have a lasting impact on the surrounding region, significantly influencing its culture, customs, and development patterns. It is therefore important to understand the spatiotemporal influence range of a particular cultural landscape. The scope of influence is affected by subjective factors regarding the views, hobbies, communication, and cognitive abilities of all individuals involved in landscape interaction. These factors are difficult to account for using the learning process of traditional methods. To address this problem, we propose a spatiotemporal influence analysis model for cultural landscapes based on a virtual geographic environment (SC-VGE). A virtual geographic environment is constructed to interactively recognize the relationships between individuals and cultural landscapes, forming key parameters of the cultural landscape influence propagation process under various conditions. On this basis, we construct influence propagation agents representing different groups of people. Based on the movement process of the agents in the virtual space, we can determine the spatiotemporal influence range of a specific landscape or landscape object. In experiments, we analyzed two typical cultural landscape examples in the city of Changchun. We conducted a comparative analysis of our approach against several traditional methods, including the Spatial Straight-Line Distance, Spatial Accessible Distance, Cellular Automata, and Multiagent. The SC-VGE method surpassed these traditional methods in overall prediction accuracy, achieving 89.7 ± 1.3% and 87.8 ± 1.5%. This is more than 9% higher than the highest accuracy among the traditional methods. Higher accuracy enables SC-VGE to more precisely reflect the influence ranges of cultural landscapes, thereby providing better support for the management and analysis of cultural landscape value.

ABSTRACTVirtual geographic scenes, as a key component of virtual geographic environments (VGEs), play an essential role in conveying complex geographic information. However, the existing geographic visualization methods have problems such as separation from user needs and poor multi‐terminal adaptation, resulting in low user cognitive efficiency. To address these limitations, an adaptive geographic scene representation framework grounded in user requirements is proposed. A systematic mapping relationship between user requirements and scene representations was first established through rigorous analysis of multi‐platform user interaction patterns. Subsequently, data‐driven strategies for dynamic scene organization and scheduling were developed based on comprehensive terminal capability profiling. Furthermore, an integrated closed‐loop framework was developed, incorporating requirement analysis, task definition, scene organization, and interaction refinement to support adaptive scene description, analysis, and exploration. The efficacy of the proposed method was systematically assessed through eye‐tracking cognitive experiments and participant‐based questionnaires. Experimental results demonstrated that the proposed methodology facilitated smooth scene interaction rendering on contemporary terminal devices. In comparison with the original scenes, user cognitive efficiency in the designated areas of interest exhibited a 90.19% improvement, thereby establishing a novel approach for enhanced geographic information transmission.

Virtual geographic environments long simulated real-world scenarios in urban planning, monument preservation, city infrastructure management, education, and entertainment. Their web-based visualisation and distribution made these environments widely accessible. However, many systems remain static, lacking real-time data integration and multi-user collaboration, while virtual worlds designed for the Metaverse emphasise dynamic interaction yet often omit essential geospatial context. Bridging this gap is critical for advancing virtual geographic environments into the next generation. In this paper, we present a modular system architecture for applications demonstrating geospatial virtual worlds over the web. Our goal is to provide a generic, well-structured framework that exposes the essential classes and interfaces needed for building 3D virtual worlds with geospatial data at their core. Our work focuses on defining specific geospatial components, methods, classes, and interfaces that form the foundation of a modern geospatial virtual environment in the Metaverse era. The proposed architecture is organised into three layers: access, world, and integration, which together enable accurate mapping and integration of real-time sensor data, digital twin synchronisation, and support for location-based services. Our analysis reveals that while most current solutions excel in either multi-user interaction or geospatial data management, they rarely combine both. In contrast, our model delivers enhanced geospatial focus, real-time collaboration, and interoperability between physical and digital realms. Overall, this work lays a solid foundation for future innovations in creating immersive, interactive, and geospatially grounded virtual experiences over the web, marking an important step in the evolution of virtual geographic environments for the Metaverse era.

ABSTRACT In the virtual geographic environment, conducting status analysis on urban structures and similar objects is crucial for enhancing their detailed management level. However, it is challenging to directly convert the same object across various software systems with different modalities (such as spatial analysis, BIM design, numerical simulation etc.). Therefore, the effective conversion of multi-modal models becomes pivotal. Due to the characteristics of inconsistent spatial description and complex association relationship among multimodal models, resulting in low knowledge reuse rate, poor accuracy of unit mapping, and low efficiency of state sharing in the process of model conversion. Aiming at these problems, this article delves into the knowledge-driven semantic conversion techniques for multi-modal models from a geospatial viewpoint. The mapping relationships between multimodal models in terms of spatial, geometric, and semantic information were first clarified. Subsequently, a structural matching template based on knowledge reuse was established, and a knowledge-guided algorithm for multimodal model transformation was designed. Finally, using a suspension bridge as a case study, a prototype system was developed and experimental analysis was conducted. The experimental results show that the method proposed in this article can accurately convert between BIM models, numerical analysis models, and GIS scene models, with spatial coordinate accuracy controlled within 1 mm and a conversion efficiency increase of more than 10 times. This can effectively enhance the integrated performance of models in applications such as digital geospatial twin scenarios.

Historical streets are an important component of urban form and embody urban culture. A detailed quantitative analysis of visitors’ spatial perception in historical streets is crucial for enhancing the spatial quality of streets. Focusing on Gulangyu Island, Xiamen, this study proposed a physical–physiological–psychological research framework for spatial perception by constructing a 3D virtual geographic environment. Based on environmental behavior theories and five dimensions of street design quality, it assessed the spatial features of historical streets, visitors’ eye-tracking and physiological sensing data, and psychological perception, summarized the mechanisms of how historical streetscapes influence visitors’ perception, and finally proposed corresponding streetscape optimization strategies. The main findings are as follows. 1) Spatial features of historical streets, including architectural style, layout of commercial spaces, spatial scale, and interface transparency, directly affect visitors’ visual experience and preferences. 2) Visual attention is significantly positively correlated with the historical streets’ imagery, openness, transparency, and complexity, and significantly negatively correlated with enclosure; among these factors, street openness, vitality, and lighting are key factors influencing visitors’ physiological responses. 3) The physical–physiological–psychological interaction mechanisms show that the visual attractiveness and emotional stimulation of the streetscapes can significantly influence individual perception and behavioral decisions, which confirmed the “physical environment–eye-tracking fixation–emotional arousal” mechanism of visitors’ visual preferences. Finally, spatial perception optimization strategies for different types of streets are proposed to inform decision-making for historical street renewals.

The Virtual Geographic Environment (VGEs) is a framework in which real-world information is conveyed and converted into the digital world. It contributes to a better understanding of geographic knowledge by human beings and helps decision-makers by giving them a profound view of geographic concerns like three-dimensional (3D) models. The production of 3D models is essential for the characterization, monitoring, and management of water areas. This study aims to expose the 3D model of the pond in the presence of Unmanned Aerial Vehicle (UAV) and Acoustic Doppler Profile (ACDP) bathymetry to precisely determine the actual amount of water in the pond for possible water problems in the future. Moreover, determine how the sediments carried by the mainstream and side tributaries feeding the pond affect the pond’s water volume and in which parts of the pond their accumulation is concentrated. This study will contribute to the literature by using an ACDP device for the bathymetry measurement process which is rare in the literature. In this approach, the pond's underwater topography of the pond contributed to the sustainable management of the pond by producing Digital Elevation Models (DEM), which is a sensitive indicator of climate change and man-made effects.

A 3D virtual geographic environment for flood representation towards risk communication

Despite a history of year-by-year reduction in road-crossing harm and fatality in the United States, the trend reversed course in 2009 and road-crossing has grown more hazardous since. Within this tendency, there has been a marked uptick in risk to urban crossers who are neither children nor elderly. The age group in between these extremes represents a bulk of urban crossers, for whom theoretical explanations for crossing behavior that are focused on youth and senior crossing factors often do not apply. New insight is likely required to explain why the rate of crossing harm is growing for the 20–44 age group, but declining among the young and elderly. However, it is difficult to experiment with crossing scenarios in a real-world context, where significant dangers are present and for which the uniqueness of crossers and crossing sites is abundant. In this paper, we introduce an end-to-end system for examining crossing behavior using a unique combination of real human crossing behavior, made safe through the combination of agent-based models, motion capture, virtual geographic environments, and immersive technologies from virtual reality. We demonstrate that this combination of methods can be deployed to examine very high resolution and very high specificities of crossing scenarios and behaviors, with reach to individual crossers and their judgment over tiny windows of space and time. We demonstrate that the system can reproduce known effects from the theoretical literature and from existing case studies, while also generating huge swaths of empirical and diagnostically useful data on crossing actions, interactions, and reactions relative to fleeting events and phenomena of urban geography, traffic dynamics, and ambient pedestrian crowds. To prove the concept, we deploy the system to investigate crossing judgment behavior among the 20–44 age group.

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.

Discussion on flood control application technology of digital twin basin based on virtual geographic environment

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A preliminary study of semantic virtual geographic environment in the context of wetland monitoring

Branching the limits: Robust 3D tree reconstruction from incomplete laser point clouds

Abstract In the era of big data information digitization, digital terrain editing technology is an important research content in the virtual geographic environment, 3D landscape simulation system, which has significant value in digital planning and design practice of cities and villages, regional road networks, and landscape gardens. This paper focuses on deep learning-based digital terrain interactive editing technology for landscape planning and design. Firstly, it introduces the concept and principle of digital terrain interactive editing technology, constructs the model and terrain interactive editing algorithm, and then analyzes three systems’ digital geography interactive data from two aspects of accuracy and convergence. The results show that A system planning digital landscape garden landscape accuracy, planning accuracy, and convergence are small, and the planning accuracy reaches a minimum of 88% and a maximum of 95%. The intersection reaches a minimum of 75% and a maximum of 87%. Compared with the other two systems are high, so it can be seen that the digital interactive terrain editing for landscape planning of this design has high planning accuracy and convergence. Digital interactive editing technology is developing rapidly in China’s landscape architecture discipline research and professional practice applications. It has shown excellent development potential and application value in the landscape planning and design industry. This study improves the accuracy and efficiency of digital interactive editing technology and has great historical significance for the development and progress of the Chinese architectural design industry.