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

[Objectives] Visible information accessible within urban three-dimensional space forms the basis for comprehensively understanding the systematic nature and cultural significance of cityscapes. To overcome the computational limitations of GIS-based viewshed analysis and the challenges of digitally representing the semantic complexity of heritage landscapes, this study aims to develop an efficient computational method for identifying 3D visible locations. The objective is to facilitate the detection and strategic establishment of key viewing positions and visible corridors within urban vertical space. [Methods] Using semantic feature points of cultural heritage landscapes as conceptual anchors, this research integrates building height data into a Digital Elevation Model (DEM) to construct a ground-referenced 3D visible location calculation matrix. This matrix enables the generation of a dataset specifically designed for analyzing visible locations relative to cultural heritage, which is subsequently used to systematically examine spatial distribution patterns and inform urban planning strategies. [Results] An empirical study was conducted in Zhangjiakou City, focusing on its Great Wall landscape. A 3D visible-location dataset was successfully generated for the Great Wall based on a 100 m horizontal grid and 3m vertical intervals. Analysis of this dataset revealed four distinct spatial patterns of visible locations: (1) Comprehensive High-Visibility Zones, areas with significantly superior Great Wall visibility, primarily in the southwestern and southern sections with low building density, suitable for designation as priority protection areas; (2) Vertical High-Visibility North-South Aerial Corridors, centered along the east side of the Qingshui River to Dongxing Street and Linyuan Road in the central city, showing strong potential for establishing elevated visible corridors; （3) Vertically Restricted Visibility Zones under Dual Building-Terrain Influence, mainly located in the southeastern areas affected by both structural and topographical constraints, where height restrictions could be strategically relaxed to 'trade height for view'; and 4) Pervasive Low-Visibility Zones, where vertical position changes have minimal impact on visibility, located in the eastern region blocked by mountains, making them low-priority for landscape resource investment. The study also pinpointed 3 m, 9 m, and 12 m above ground level as key observation heights for perceiving the Great Wall in Zhangjiakou, providing a foundation for differentiated height control in urban planning and moving beyond one-size-fits-all approaches. While maintaining consistency with traditional analysis results, the proposed method effectively avoids the computational redundancy of conventional panoramic calculations, demonstrating its capability for rapid assessment of landscape visibility resources across large urban 3D spaces. [Conclusions] The proposed computational method for 3D visible locations ensures efficiency while capturing the semantic richness of heritage landscapes. The resulting dataset can effectively support 3D landscape design and spatial planning in urban contexts, offering a novel approach for leveraging the resource value of National Cultural Parks.

[Objectives] Visual quality serves as a crucial perceptual indicator for evaluating the built environment of cities and holds significant importance for urban renewal and the improvement of the human habitats. However, existing methods often overlook the influence of the built environment on the visual quality of indoor spaces, making it difficult to accurately capture the human perceptual experience within urban environments. [Methods] This study integrates multi-source laser point clouds to propose a visual quality evaluation method based on window-centered viewpoints. First, UAV and TLS point clouds were aligned, and feature categories were extracted to construct a viewshed image using the window center as the viewpoint, with both category and view distance assigned separately. Second, two novel three-dimensional evaluation factors, spatial visibility and spatial layer balance index, were introduced, and nine existing two-dimensional evaluation factors were integrated to establish a visual quality evaluation system across three dimensions: naturalness, sociability and complexity, based on the viewshed image. Finally, the Elo rating system was employed to quantify public subjective perceptions at the viewpoints, while a random forest model was used to analyze the relationships between evaluation factors and visual quality scores and to explore factor correlations. [Results] In the selected experimental area, the proposed method achieved high evaluation accuracy, with an R² value of 0.969 3. Among the evaluation factors, spatial visibility, sky view factor, and spatial layer balance index were identified as key indicators for improving the visual quality of urban environments. While two-dimensional metrics dominated visual quality assessment, three-dimensional metrics proved more predictive and influential when the number of factors was limited. Most evaluation factors exhibited positive correlations with visual quality scores. Areas with open viewsheds and higher feature diversity demonstrated superior visual quality ratings. [Conclusions] Based on window-centered viewpoint, this study innovatively introduces the concepts of spatial visibility and spatial layer balance index and incorporates the Elo rating system to enhance the objectivity of evaluation results, achieving a multidimensional and multifaceted assessment of visual quality. The proposed method quantitatively evaluates the influence of the outdoor built environment on indoor visual quality, supports fine-scale urban assessments and key-area analyses from a visual perception perspective, and provides quantitative data support for urban planning and landscape design.

[Objectives] As one of the key data products and organization methods in China's new fundamental surveying, mapping, and 3D realistic geospatial scenes, geo-entity data and its model have expanded and improved existing geographic information products in terms of temporal accuracy, dimensionality, and granularity. Research on administrative division modeling, particularly under big data conditions for multidimensional dynamic data, is of significant importance. However, traditional modeling methods fail to comprehensively describe the lifecycle, sematic composition relationships, and other critical information of administrative divisions from an entity-based perspective. Furthermore, there has been limited exploration of entity-based fusion and transformation methodologies for multi-source heterogeneous data. [Methods] To address these challenges, this study proposes an objectified modeling method for administrative divisions that considers composition relationships. Based on the multi-granularity spatio-temporal object data model description framework, the spatio-temporal entity description of administrative divisions is first abstracted, encompassing identification coding, spatio-temporal reference, spatial characteristics, attribute characteristics, and composition relationships. Formal descriptions are then provided at two levels: the spatio-temporal characteristics of the entity itself and the composition relationships among the entities. Subsequently, considering the multidimensional dynamics and complex relationships of administrative division entities, a logical data model—referred to as "4 tuples & 1 operation"—is proposed, consisting of the inherent tuple, spatial tuple, attribute tuple, relationship tuple, and version operation. Using standard administrative division boundaries, codes, and statistical data from official websites as examples, a multi-source data fusion and transformation process for geo-entities is constructed. [Results] Experimental results from objectified modeling, implemented using open-source software and development platforms, demonstrate that the proposed method effectively supports the construction, storage, management, editing, querying, and visualization of administrative division entity data, incorporating lifecycle management and complex composition relationships. It successfully addresses the challenges of describing temporal composition relationships and multi-source data fusion in administrative division modeling. [Conclusions] This study establishes a coupled expression mechanism that integrates temporal evolution at the entity level and nested hierarchical relationships at the relational level for administrative divisions. The findings provide a foundational model for entity-based spatial cognition and multimodal applications.

[Background] With the comprehensive advancement of the Real-Scene 3D China initiative, rapidly establishing high-precision Digital Elevation Models (DEMs) based on existing real-scene 3D models has emerged as a novel approach to terrain modeling. In complex urban areas, terrain modeling faces additional challenges due to diverse land parcel types and the coexistence of both gradual and abrupt elevation changes. [Methods] To address these challenges, this study proposes a high-precision partitioning-and-blocking DEM modeling method based on real-scene 3D models. The implementation process consists of three key stages. First, elevation points are extracted from pre-established real-scene 3D models. This step provides the fundamental data foundation for subsequent DEM construction, ensuring that the source data accurately reflect the topographic characteristics of the study area. Second, the CoANet network is employed to extract road skeletons, identifying the linear features of urban roads and providing spatial references for regional division. Concurrently, the DeepLabV3 model is utilized to segment four major functional zones: buildings, woodlands, grasslands, and water bodies. Based on the distinct topographic characteristics of these functional zones, a differentiated modeling strategy is developed: For bare land areas, elevation data are extracted, and height-reduction processing is applied based on the average statistical height of street trees. This ensures the flatness of adjacent road sections and prevents scattered tree elevations from interfering with road terrain modeling. For woodland areas, an improved local-maximum algorithm is integrated with a neighborhood mean height-reduction algorithm to restore the understory terrain. This integration compensates for the limitations of using only a local-maximum algorithm and more accurately reproduces the topographic features beneath forest canopies. For building areas, extreme-value detection technology is adopted to reconstruct the transition zones between buildings and surrounding terrain, effectively solving the problem of elevation discontinuity at building edges. For water areas, DEM construction is performed based on water boundaries to ensure that modeled water surface elevations conform to actual hydrological conditions. Finally, the DEMs generated from the partitioned modeling of each functional zone are fused through a spatial data integration algorithm. This process eliminates data redundancy and inconsistencies between adjacent regions, ultimately generating a complete and unified DEM of the study area. [Results] Experimental results demonstrate the superior performance of the proposed differentiated modeling method. Specifically, in road areas, the modeling accuracy improved by 56.5% after average height-reduction processing, significantly improving the flatness and accuracy of road terrain representation. In grassland areas, accuracy improved by 17.8% compared with the traditional Cloth Simulation Filter (CSF) algorithm, due to better adaptability to subtle elevation fluctuations. In woodland areas, accuracy improved by 53.1% compared with the single improved local-maximum method, verifying the effectiveness of the algorithm-fusion strategy in restoring understory terrain. Overall, the DEM accuracy for the entire study area increased by 35% compared with conventional modeling methods. [Conclusions] The method proposed in this study provides an automated and high-fidelity DEM modeling solution for urban surfaces characterized by interlaced natural and artificial terrains, as well as the coexistence of abrupt and gradual elevation changes. It effectively overcomes key technical bottlenecks of traditional DEM modeling in complex urban environments and holds significant potential for advancing refined and intelligent urban terrain mapping with the Real-Scene 3D China initiative.

[Significance] Dynamic simulation of surface processes is crucial for understanding the evolution of geographical processes and their impacts on the ecological environment. It has long been both a focal point and a challenge in the field of geographic information science. [Background] Existing research has mainly focused on 2D surface simulations, while dynamic 3D process simulations still require further development. The integration of Discrete Global Grid Systems (DGGs) and Cellular Automata (CA) offers a powerful tool and platform for simulating geographical processes dynamically. [Methods] A water body simulation platform for Eling Lake was built using a Spherical Geodesic Octree Grid (SGOG). Based on Fick's first law and implemented in C++ with the Open Scene Graph (OSG) 3D engine and an integrated development platform, a visualization model for water pollution diffusion was constructed. Analytical and design procedures for both 2D and 3D cellular neighborhood and transformation rules were established using triangular cells. Boundary conditions at the lake embankment were defined by extending virtual cells. Static pollution diffusion simulations were then conducted under 2D, 3D, and terrain-constrained conditions at the shoreline to visually display pollutant diffusion during accidental water pollution events, and the simulation results were analyzed. [Results] The experiments showed that: (1) When the pollution limits and total instantaneous pollutant input were the same, but the actual water volume represented by each cell differed between 2D and 3D simulations, the duration of high-concentration pollution cells in the 3D simulation was significantly longer than in the 2D simulation—by about 55 minutes. Notable differences were also observed in the total number of affected cells and in the corresponding water body volumes. For example, after 55 minutes of diffusion, 661 polluted cells were identified in the 2D simulation, compared to 3,546 in the 3D simulation. At that point, the water body volume represented in the 3D simulation was approximately 89.4% of that in the 2D simulation. (2) In both 2D and 3D simulations, the temporal trends in the proportion of cells with different pollution levels were quite similar. For instance, the proportion of heavily polluted areas in both cases fell below 10% after 33 minutes of diffusion, while the proportion of lightly polluted areas exceeded 70% at the same time. However, the vertical spatial distribution of polluted cells should not be overlooked, as the 3D simulation results more accurately reflect real-world pollution diffusion patterns. [Conclusions] This study provides a new technical framework for research on water environment pollution and offers valuable decision-making support for managing sudden water pollution incidents.

[Objectives] Differences in parameters such as DEM accuracy and grid size can affect the results of surface runoff simulations. However, no studies have yet examined the impact of DEM grid center coordinate offsets on surface runoff simulation results. [Methods] In this study, a 0.2 m resolution DEM constructed from UAV tilt photography was used as the reference dataset. Various offset directions and distances were applied to generate DEM sequences with different raster center coordinates. Based on these, surface catchment simulations were conducted using DEMs with different resolutions and offset parameters to extract hydrological features such as catchment areas and drainage lines. These results were then compared with those obtained from the original DEM. [Results] The results are as follows: (1) Grid center offsets affect the extraction of watershed lines and catchment areas, and the degree of response varies significantly with DEM resolution. Under a 5 m resolution DEM, hydrological parameters remained generally stable, with only slight changes observed under certain offset conditions (e.g., +2 m in the x-direction or -2 m in both x and y directions). However, the 1 m resolution DEM was more sensitive to offsets: when the x-direction offset was -0.3 m and -0.4 m, line density increased to 8.461 3 km/km² and the number of catchment areas decreased from 221 to 205. The maximum changes in line density for the 5 m and 1 m resolutions were 0.189 km/km² and 0.849 3 km/km², respectively, indicating that high-resolution DEMs are more susceptible to offset effects. (2) The impact of offset direction on hydrological information extraction also varies. Multi-directional offsets have a greater influence than single-directional ones. For example, in the 1 m resolution DEM, when the offset was -0.2 m, the x-direction line density is 8.363 9 km/km² and the number of catchment areas was 226. However, with combined x and y offsets, line density decreased to 7.503 7 km/km² and the number of catchment areas to 197. These results show that high-resolution DEMs exhibit greater variability in hydrological outputs under the same offset conditions. (3) The results also indicate that terrain complexity significantly affects the sensitivity to grid center offsets. In areas with high terrain variability, offset effects caused substantial changes in drainage lines, whereas in flatter areas, the impact of DEM center coordinate offsets on drainage simulation results was relatively minor. [Conclusions] This study investigates the impact of DEM grid center coordinate offsets on surface catchment simulations, offering insights into how such offsets contribute to uncertainty in hydrological analyses. The findings highlight the need to account for grid alignment when using high-resolution DEMs in terrain-sensitive applications.

[Objectives] To improve the accuracy of vehicle trajectory prediction in highway scenarios, a vehicle trajectory prediction method based on frequency-domain decomposition and feature fusion is proposed to address the problems of multi-scale feature entanglement, sensitivity to noise disturbances, and insufficient interpretability of spatial contexts under dense traffic flow. In complex highway environments, vehicle trajectories are characterized by strong non-stationarity, multi-scale temporal variations, and complex spatial interactions among neighboring vehicles, which increase the difficulty of accurate prediction. Therefore, it is necessary to jointly model motion regularity, interaction dependence, and temporal evolution in a unified framework. [Methods] First, under a local road coordinate system and lane-based spatial reference, the historical trajectories of vehicles are represented in a structured form. An improved multivariate variational mode decomposition method optimized by the improved whale optimization algorithm, namely IWOA-MVMD, is employed to decompose non-stationary trajectory signals into Intrinsic Mode Functions （IMFs） at different frequency scales. This process helps separate long-term motion trends, local fluctuation patterns, and high-frequency disturbance components, thereby enhancing multi-scale feature extraction and suppressing noise interference. Second, a convolutional neural network and a directional attention mechanism are used to extract local trajectory features and enhance the representation of spatial interaction features among vehicles. Third, a bidirectional cross-attention mechanism is designed to establish bidirectional information exchange between frequency-domain features and interaction features, enabling deep fusion of heterogeneous features. Finally, a Bi-Mamba network is used to model temporal dependencies, and a mixture density network is introduced to output the probability density distribution of future trajectories. [Results] Experiments were conducted on the NGSIM dataset over prediction horizons from 2 s to 5 s. The results show that the proposed model achieves an average RMSE of 1.04 m, which is 25.2% lower than that of the competitive baseline GCNTA. At the 5 s prediction horizon, the prediction error is reduced by 40.6%, indicating that the proposed method shows better robustness in longer-term prediction. In addition, the ADE and FDE of the proposed model are 0.95 m and 1.66 m, respectively, which are 12.0% and 34.9% lower than those of the best comparative model STDAN. These results demonstrate that the proposed framework performs well in both overall trajectory fitting and final-position prediction. [Conclusions] The proposed method effectively improves the accuracy and stability of vehicle trajectory prediction in complex highway scenarios. By integrating frequency-domain decomposition, spatial interaction modeling, cross-feature fusion, and temporal dependency learning, it provides methodological support for trajectory prediction in intelligent transportation and autonomous driving applications.