# 3D City Online Visualization and Cluster Architecture for Digital City

Authors: Lin Fu; Yaqing Ding
Journal: Journal of Sensors
 (2021)

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## Abstract

As an important carrier of human production, life, and social development, the emergence of cities symbolizes the maturity and civilization of mankind. For more than 40 years of reform and opening up, our country’s economic development has become increasingly prosperous, and urbanization is booming. At present, our country is in a decisive period for building a well-off society in an all-round way, with rapid progress in socio-economic growth and urbanization. Based on this, this article is oriented towards urban visualization modeling work and proposes a cluster modeling method that is compatible with the combination of urban geological structure and three-dimensional urban space, so that urban space modeling work not only pays attention to the rationality of above-ground planning and construction but also fully considers underground geology the stability and safety of the structure. This paper uses the 3D city online visualization modeling technology to efficiently and reasonably complete the 3D urban geological modeling under the fusion of multiple geological data and combines the organic combination of multisource heterogeneous model data to convert the geological model data into a 3D geographic information model; the universal standard format analyzes the rapid construction of large-scale complex geological structure models and the integrated expression of multisource heterogeneous model data. Experiments have proved that from the cluster capacity of 5,000 to 100,000, no matter how much the modeling time is different, whether it is to search the entire territory or part of the scope, the search time of the 3D city visualization model is less than 20 ms, and the 3D city visualization model map of the city can be well established. This shows that the three-dimensional city visualization model highlights the impact of the urban geological environment on urban construction and development and visually and vividly displays region geological structure and other information in a three-dimensional way, providing corresponding information for urban geological stability assessment and geological disaster rescue reference and help.


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## Body

## 1. Introduction

We are now entering the information age, and access to information is the key to making scientific decisions. In the process of information acquisition, the comprehensive management and visual expression of information is a key to the speed of information acquisition. The current geographic information model is such a collection, input, management, retrieval, analysis, and output of information. The integrated computer software and hardware model is also a spatial information model related to the distribution of spatial geographic coordinates, which extracts abstract information, which is demonstrated through medical viewpoints and conforms to human physiological functions. Starting from this point in order to truly express the real world, this has led to the development of visualization technology. 3D imaging of urban landscapes is one of the most important digital imaging technologies. The implementation of questionnaires and analysis in the three-dimensional urban landscape enables the three-dimensional landscape not only to provide users with a visual experience but also to allow users to better understand urban construction, make decisions, and allow designers and users to understand the status quo and plan urban construction. The design process is more meticulous, objective, and easy to understand, which broadens the vision of urban planning, design, and management personnel, makes urban planning and infrastructure design more scientific, and is very important for sustainable urban development training.Due to the rapid development of graphics visualization technology, virtual reality technology, and 3D reconstruction technology, the development, implementation, and integration of 3D digital cities have become key areas of current digital research, and 3D city spatial modeling and visualization research have been extremely popular big attention [1]. As a true representation of the urban space scene, the three-dimensional city model has been widely used in urban planning, construction, and development. With the increasing number of urban buildings, the integrity and stability of urban underground geological structures play an important role in urban planning, design, construction, and the development and utilization of underground resources.In recent years, in this innovative era at home and abroad, as digital technology is becoming more and more popular today, the overall social environment cannot be separated from digital technology [2]. The progress of digital technology is based on the development of social economy, and the development of the digital age has promoted that social and cultural changes are also changing people’s living environment to a certain extent. Al-Shareefi et al. proposed a multisource 3D city model visualization framework, from data storage and management, data analysis, and 3D model simplification to multisource fusion visualization. First, store the data in HBase for management, and design a new row key method to quickly retrieve and build indexes. Secondly, a model simplification algorithm is proposed. We have implemented fast concurrent processing of 3D models in the Hadoop environment, which not only reduces the complexity of the model but also retains the visual effects and improves the network transmission speed and model rendering efficiency. Finally, with the help of the Cesium platform, the fusion visualization of multisource 3D city models was realized. However, his research did not clearly explain the effectiveness of the 3D city model visualization framework [3]. Billger et al. proposed a novel framework based on CityGML and X3D to support the visualization of 3D city models via the Internet. In the proposed framework, the CityGML file is first parsed to obtain city model information. Citygml4j is used for this analysis. Then, an X3D representation is generated based on the city model through the proposed algorithm, which can dynamically create different 3D city models according to the corresponding level of detail. Finally, use a Java applet or other X3D viewers to display the 3D city scene in X3D format via the Internet. Preliminary experiments show that the framework can display 3D city models correctly and effectively through the Internet. However, its overall research lacks data support, and more data is needed to support its conclusions [4]. In order to improve the visualization of large-scale 3D landscapes and city models in the network environment, Chiang et al. use two different types of hierarchical level of detail models to construct terrain and building groups. He also uses these models to implement progressive streaming in client-server and peer-to-peer network architectures. In order for such services to appeal to a broad audience, these 3D models must achieve sufficient authenticity and accuracy. There are many solutions that can be used to automatically generate 3D models of huge urban environments. The geographic information model database provides a good foundation for generating such models at an affordable cost with minimal manual intervention. The experimental results lack more data support so that the accuracy and authenticity of the 3D city model are still in doubt [5].Through the establishment of a three-dimensional urban design visualization model, this paper strives to realize a real-time creation platform for three-dimensional urban design scenes, provides spatial comparison analysis of urban status and urban design and a full-scale virtual roaming display of design schemes, and provides analysis of planning information through a database. And query to realize the creation, editing, and replacement of different building models in the plan, thereby providing a new evaluation method for plan evaluation, macro decision-making, and reasonable construction in urban design. This article focuses on the application of visualization in the field of urban planning. Necessity and feasibility analysis proposed the technical route and realization of the three-dimensional visualization model in urban design. On the basis of existing data, materials, and project requirements, the three-dimensional visualization technology in urban design was discussed.


## 2. 3D City Online Visualization Technology and Cluster Application Architecture

### 2.1. Necessity and Feasibility of Planning Visualization

#### 2.1.1. The Necessity of Planning Visualization

(1) The Objectivity of Urban Development and Overall Planning. The development of a city cannot be separated from scientific master planning and regional planning. Economic prosperity must also be carried out in conjunction with urban planning. In turn, urban planning and the growth of economic benefits will greatly strengthen the intensity of urban planning and construction. “Visualized city management platform” is the use of virtual reality technology to build a visualized 3D city virtual simulation system and with the help of data fusion and interconnection mechanisms to achieve smart management of the city [6]. The object of management that involves all aspects of the smart city, including urban architecture, landscaping, energy supply, communications, roads and transportation facilities, infrastructure, and geographical environment, is a set of super large-scale virtual simulation and visualization integrated management platform. Taking the field of urban planning as an example, the use of planning visualization technology will greatly improve efficiency and optimize different designs. Thereby, the overall quality of the project and the scientific nature of the feasibility analysis need to use planning visualization [7, 8]. A simple three-dimensional urban space model is shown in Figure 1.Figure 1
Three-dimensional urban space model (image source:https://image.cnki.net/).(2) The Necessary Means to Correct Mistakes and Deviations in Urban Design. Urban planning is the macrograsp and scientific guidance of urban development. Due to the overall nature of urban planning, a small mistake may lead to the slightest loss and thousands of miles away, leading to stagnant and sometimes even retrogressive urban economic development. Although some primitive and simple visualization methods have been used in the planning and design process before, such as diagrams, photos, models, and even animations [9, 10].While the introduction of real 3D images brings a new experience to the user interface, its development also led to the development of 2DGIS to 3DGIS. The advent of realistic 3D images allows casual GIS users not to experience validation and boring 2D images, and it also makes using GIS programs easier, intuitive, and flexible. However, the abundance of 3D images is more complex than 2D images, and the application requirements are higher. One of the most important functions of the GIS system is retrieval and data analysis. The demand for fast interaction and speed and analysis of information in a complex 3D graphic environment is a concern of every developer of the 3DGIS system and is also an important function of the 3D urban imaging system.


#### 2.1.2. Feasibility Analysis of Planning Visualization

(1) The Industry Characteristics of Planning and Design Itself Determine That Planning Visualization Is Feasible. In traditional planning and design, visualization is only applied to the visualization technology during the approval stage of the planning and design results, or when the design intent is explained. The development of visualization technology has led to the diversification of planning and design methods, and planners will welcome a new round of planning technology innovation with a proactive attitude [11, 12]. The application of urban planning visualization can not only display any state of urban space but also effectively virtualize the development process of urban space and compare the status quo with the past and the future. It can be seen that the use of visualization technology in planning and design is realistic and feasible. Whether it is scientific computing visualization, data visualization, or information visualization, it has a wide range of application prospects. Urban planning provides a good development space for visualization [13]. The advantages of image-based modeling technology include highly realistic, easy to connect to relevant world information, and easy to install topographical relationships in 3DGIS. Moreover, its weaknesses have also emerged, which are not suitable for current computer algorithms. The highly innovative 3D geometric modeling also has a large amount of geometric data, which damages the real-time nature of the 3D landscape. Image-based 3D modeling technology overcomes the weakness of large geometric data sets, replacing compact models with text-rich images and creating more realistic 3D landscapes with less data. Therefore, this method also has shortcomings such as poor interactivity, low understanding of reality, and difficulty in integrating with 3DGIS database.(2) The Rapid Spread of Informatization and Networking. Today, planning visualization has developed a considerable technical foundation, and the lack of basic data and irregularities in the past no longer exist. The collection and collection of urban geographic information needs to reach a level of standardization and digitization. Many planning units have achieved paperless office, approval, and program evaluation. The use of computers and networks has become popular, and computer-aided drawing and design-aided design have also become common methods; these have shortened the planning and design cycle and enhanced the flexibility of the planning process; in addition, new concepts such as dynamic planning have emerged [14, 15]. In addition, with the development of remote sensing technology and geographic information models, digital topographic maps generated by aerial photography and satellite technology have begun to appear [16]. The standardization of these design, management, and planning informatization results has laid a good foundation for the visualization of information to go to the society and serve the public. It also prepares a data foundation for planning visualization. With the advent of the Internet age and the information age, resources and information are becoming more and more abundant, and the development of technical means is getting faster and faster. The digital and networked environment makes the realization of visualization more and more close to us [17, 18]. In urban planning and management, the design of urban long-distance network is a very important link, and its accuracy, stability, or indirectly affects the design, construction, and management of the city. Especially in recent years, many roads have been renovated and expanded, and many districts have been newly built, which has caused major changes in land pipelines. The previous data is outdated and cannot reflect the true state of the ground network. However, the traditional paper data update is very difficult, and it takes time and effort to ask questions.


### 2.2. 3D Visualization of Terrain

#### 2.2.1. Topographic Data Structure Design

(1) Digital Elevation Model Modeling. A model form that describes terrain changes through discrete lattices, and its abstraction can be described as an ordered set of two-dimensional functions:
(1)Hp=fkUp,Vpk=1,2,⋯,n.Among them, the elevation information of a point (in fact, a tiny neighborhood of this point) is determined on the surfaceHp, usually relative to the standard altitude or a certain reference plane, and for this purpose, the coordinates of the landmark point UpVp under the geodetic projection. The modeling of the digital elevation model refers to the establishment of a realistic terrain surface through surface fitting in the visualization process [19, 20]. According to different data sources, there are three common modeling methods: based on irregular triangulation, based on regular grid, and based on contour model. The relationship between them is shown in Table 1.Table 1
Comparison of three digital elevation models.
StructureRegular gridContour lineIrregular triangulationData sizeView grid sizeSmallBigGeneration methodData interpolationRemote sensing topographic mapDiscrete point networkingTopological relationshipGoodNo goodGoodArbitrary point interpolationDirectIndirectDirectSuitable for terrainGentle but not severeGentle but not severeArbitrary terrainAmong them, the triangle mesh model can be changed in size and shape according to the actual degree of terrain change, but its topological rules are complicated, and it is difficult to establish and store, so it is usually used for small-scale terrain data with local high changes, etc. The high-line model expresses topographical changes through a collection of iso-elevation data and corresponding elevation values. This data generation method will generate obvious step mutations and cannot be smooth and continuous [21]. Public 3D viewing must take into account the characteristics of the subject matter, not just the data object itself. In recent years, researchers have made valuable discoveries. In the process of perception, what is made in the human brain is a complete and complete model of the various properties, parts, and relationships of objects. When people observe a very large area of modern cities, they all adopt a technological approach to gain an understanding of the general structure of the city.When performing regular grid interpolation, there are mainly three interpolation ideas: overall interpolation, partial block interpolation, and point-by-point interpolation. In the global digital elevation model, if the overall data is used for interpolation, there will be a phenomenon of high interpolation, which is obviously distorted, and sparse sampling makes the point-by-point interpolation effect unsatisfactory [22]. Therefore, this paper adopts local interpolation. In the common local interpolation method, bilinear surface is selected to describe the topographical change, and its function form is
(2)Z=a0+a1x+a2y+a3xy.Suppose four elevation sampling pointsP1x1,y1,z1, P2x2,y2,z2, P3x3,y3,z3, and P4x4,y4,z4 and the four parameters are calculated as follows:
(3)a0a1a2a3=1x1y1x1y11x2y2x2y21x3y3x3y31x4y4x4y4z0z1z2z3.After obtaining the four parameters, you can bring in the coordinates to know the elevation value estimated inside the grid. In the actual project, it is found that when the tree-shaped multiresolution structure is used to represent the terrain, the elevation mutation caused by the different interpolation coefficients will appear at the adjacent tiles. In order to eliminate this effect, it is necessary to leave the hierarchy when generating the hierarchical structure processing to eliminate the impact. With the rapid development of smart cities and mobile Internet, users will increase the demand for 3D mapping technology, especially large 3D cities limited by projector display capabilities and network transmission capabilities give a solution.(2) Tile Pyramid Structure. The tile pyramid model is a common multiresolution hierarchical structure. Its core idea is to dynamically select a suitable resolution through the change of the visual domain range during the zooming process of the scene by layering and partitioning [23]. Through this method of presegmentation and block division, it is directly transmitted through the network when the user actually uses it without real-time sampling calculation. Suppose the original resolution of the image data is R0 and the magnification is m, then the resolution of the layer data Rk is equal to:
(4)Rk=R0×mk.(3) Frustum Culling Algorithm. Since the terrain tiles are divided into squares, the maximum elevation is generally lower than the side length of the square. Let the terrain tiles be tile, the radius of the tile enclosing sphere is R, and the directed distance from the center of the sphere to each clipping plane of the frustum is Di,i∈0,5, and the relationship of is tilei obtained by distance judgment:
(5)tilei=outside,Di≤−R,inside,Di≤R,inside,Di>−R.After calculating the orientation of the tile and the six clipping planes, at that time, the tile∀tilei=outside was considered invisible and was removed; otherwise, the tile completely contained or intersected with the frustum, keep this tile [24, 25].


#### 2.2.2. Topographic Factors

According to the digital elevation model, the approximate surface, that is, the approximate surface is determined for each grid unit. The slope angleθ between the approximate surface and the horizontal plane is used to represent the slope angle of the basic face element θ, which can be determined by the vector method [26, 27]. Z1, Z2, Z3, and Z4 are the absolute elevation of the corner point of the approximated surface; a and bare the vector, N is the normal vector of the plane xoy, and n is the normal vector of the approximated surface; the slope angle θ is calculated as follows:
(6)tgθ=az2+bz2d.Converted:(7)θ=arctgaz2+bz2d.Among them:(8)az=−Z1+Z2−Z3+Z42.Through formulas (6), (7), and (8), we can get the algorithm source of formula (9):
(9)bz=−Z1−Z2+Z3+Z42.d is the grid spacing, tgθ is the slope of this basic facet [28], and θis the slope angle.


#### 2.2.3. Calculation of Aspect

Suppose that the angle [29] between the projection of the normal vector of the approximation surface xoy of the fundamental face element on the plane and the axis x is β, then
(10)tgβ=−azbz.Buttgβ is a periodic function [30, 31], which β can be taken in the range of 0° to 360°, but arctg−a/b can only be taken in the range of (-90°, 90°), so β cannot be represented, and further judgment is needed.


#### 2.2.4. Surface Area Calculation

The surface area of the surface unit can be easily obtained from the digital elevation model. According to the physical properties of the space vector, the surface area of the unit surface can be expressed as(11)si,j=ni,j=Δy2zi,j+zi,j+1−zi+1,j−zi+1,j+12/4+Δx2zi,j+1+zi+1,j+1−zi,j−zi+1,j2/4+Δx2Δy22.The surface area is calculated as the real area of the surface grid unit [20], and the surface area can be solved by this method.


## 2.1. Necessity and Feasibility of Planning Visualization

### 2.1.1. The Necessity of Planning Visualization

(1) The Objectivity of Urban Development and Overall Planning. The development of a city cannot be separated from scientific master planning and regional planning. Economic prosperity must also be carried out in conjunction with urban planning. In turn, urban planning and the growth of economic benefits will greatly strengthen the intensity of urban planning and construction. “Visualized city management platform” is the use of virtual reality technology to build a visualized 3D city virtual simulation system and with the help of data fusion and interconnection mechanisms to achieve smart management of the city [6]. The object of management that involves all aspects of the smart city, including urban architecture, landscaping, energy supply, communications, roads and transportation facilities, infrastructure, and geographical environment, is a set of super large-scale virtual simulation and visualization integrated management platform. Taking the field of urban planning as an example, the use of planning visualization technology will greatly improve efficiency and optimize different designs. Thereby, the overall quality of the project and the scientific nature of the feasibility analysis need to use planning visualization [7, 8]. A simple three-dimensional urban space model is shown in Figure 1.Figure 1
Three-dimensional urban space model (image source:https://image.cnki.net/).(2) The Necessary Means to Correct Mistakes and Deviations in Urban Design. Urban planning is the macrograsp and scientific guidance of urban development. Due to the overall nature of urban planning, a small mistake may lead to the slightest loss and thousands of miles away, leading to stagnant and sometimes even retrogressive urban economic development. Although some primitive and simple visualization methods have been used in the planning and design process before, such as diagrams, photos, models, and even animations [9, 10].While the introduction of real 3D images brings a new experience to the user interface, its development also led to the development of 2DGIS to 3DGIS. The advent of realistic 3D images allows casual GIS users not to experience validation and boring 2D images, and it also makes using GIS programs easier, intuitive, and flexible. However, the abundance of 3D images is more complex than 2D images, and the application requirements are higher. One of the most important functions of the GIS system is retrieval and data analysis. The demand for fast interaction and speed and analysis of information in a complex 3D graphic environment is a concern of every developer of the 3DGIS system and is also an important function of the 3D urban imaging system.


### 2.1.2. Feasibility Analysis of Planning Visualization

(1) The Industry Characteristics of Planning and Design Itself Determine That Planning Visualization Is Feasible. In traditional planning and design, visualization is only applied to the visualization technology during the approval stage of the planning and design results, or when the design intent is explained. The development of visualization technology has led to the diversification of planning and design methods, and planners will welcome a new round of planning technology innovation with a proactive attitude [11, 12]. The application of urban planning visualization can not only display any state of urban space but also effectively virtualize the development process of urban space and compare the status quo with the past and the future. It can be seen that the use of visualization technology in planning and design is realistic and feasible. Whether it is scientific computing visualization, data visualization, or information visualization, it has a wide range of application prospects. Urban planning provides a good development space for visualization [13]. The advantages of image-based modeling technology include highly realistic, easy to connect to relevant world information, and easy to install topographical relationships in 3DGIS. Moreover, its weaknesses have also emerged, which are not suitable for current computer algorithms. The highly innovative 3D geometric modeling also has a large amount of geometric data, which damages the real-time nature of the 3D landscape. Image-based 3D modeling technology overcomes the weakness of large geometric data sets, replacing compact models with text-rich images and creating more realistic 3D landscapes with less data. Therefore, this method also has shortcomings such as poor interactivity, low understanding of reality, and difficulty in integrating with 3DGIS database.(2) The Rapid Spread of Informatization and Networking. Today, planning visualization has developed a considerable technical foundation, and the lack of basic data and irregularities in the past no longer exist. The collection and collection of urban geographic information needs to reach a level of standardization and digitization. Many planning units have achieved paperless office, approval, and program evaluation. The use of computers and networks has become popular, and computer-aided drawing and design-aided design have also become common methods; these have shortened the planning and design cycle and enhanced the flexibility of the planning process; in addition, new concepts such as dynamic planning have emerged [14, 15]. In addition, with the development of remote sensing technology and geographic information models, digital topographic maps generated by aerial photography and satellite technology have begun to appear [16]. The standardization of these design, management, and planning informatization results has laid a good foundation for the visualization of information to go to the society and serve the public. It also prepares a data foundation for planning visualization. With the advent of the Internet age and the information age, resources and information are becoming more and more abundant, and the development of technical means is getting faster and faster. The digital and networked environment makes the realization of visualization more and more close to us [17, 18]. In urban planning and management, the design of urban long-distance network is a very important link, and its accuracy, stability, or indirectly affects the design, construction, and management of the city. Especially in recent years, many roads have been renovated and expanded, and many districts have been newly built, which has caused major changes in land pipelines. The previous data is outdated and cannot reflect the true state of the ground network. However, the traditional paper data update is very difficult, and it takes time and effort to ask questions.


## 2.1.1. The Necessity of Planning Visualization

(1) The Objectivity of Urban Development and Overall Planning. The development of a city cannot be separated from scientific master planning and regional planning. Economic prosperity must also be carried out in conjunction with urban planning. In turn, urban planning and the growth of economic benefits will greatly strengthen the intensity of urban planning and construction. “Visualized city management platform” is the use of virtual reality technology to build a visualized 3D city virtual simulation system and with the help of data fusion and interconnection mechanisms to achieve smart management of the city [6]. The object of management that involves all aspects of the smart city, including urban architecture, landscaping, energy supply, communications, roads and transportation facilities, infrastructure, and geographical environment, is a set of super large-scale virtual simulation and visualization integrated management platform. Taking the field of urban planning as an example, the use of planning visualization technology will greatly improve efficiency and optimize different designs. Thereby, the overall quality of the project and the scientific nature of the feasibility analysis need to use planning visualization [7, 8]. A simple three-dimensional urban space model is shown in Figure 1.Figure 1
Three-dimensional urban space model (image source:https://image.cnki.net/).(2) The Necessary Means to Correct Mistakes and Deviations in Urban Design. Urban planning is the macrograsp and scientific guidance of urban development. Due to the overall nature of urban planning, a small mistake may lead to the slightest loss and thousands of miles away, leading to stagnant and sometimes even retrogressive urban economic development. Although some primitive and simple visualization methods have been used in the planning and design process before, such as diagrams, photos, models, and even animations [9, 10].While the introduction of real 3D images brings a new experience to the user interface, its development also led to the development of 2DGIS to 3DGIS. The advent of realistic 3D images allows casual GIS users not to experience validation and boring 2D images, and it also makes using GIS programs easier, intuitive, and flexible. However, the abundance of 3D images is more complex than 2D images, and the application requirements are higher. One of the most important functions of the GIS system is retrieval and data analysis. The demand for fast interaction and speed and analysis of information in a complex 3D graphic environment is a concern of every developer of the 3DGIS system and is also an important function of the 3D urban imaging system.


## 2.1.2. Feasibility Analysis of Planning Visualization

(1) The Industry Characteristics of Planning and Design Itself Determine That Planning Visualization Is Feasible. In traditional planning and design, visualization is only applied to the visualization technology during the approval stage of the planning and design results, or when the design intent is explained. The development of visualization technology has led to the diversification of planning and design methods, and planners will welcome a new round of planning technology innovation with a proactive attitude [11, 12]. The application of urban planning visualization can not only display any state of urban space but also effectively virtualize the development process of urban space and compare the status quo with the past and the future. It can be seen that the use of visualization technology in planning and design is realistic and feasible. Whether it is scientific computing visualization, data visualization, or information visualization, it has a wide range of application prospects. Urban planning provides a good development space for visualization [13]. The advantages of image-based modeling technology include highly realistic, easy to connect to relevant world information, and easy to install topographical relationships in 3DGIS. Moreover, its weaknesses have also emerged, which are not suitable for current computer algorithms. The highly innovative 3D geometric modeling also has a large amount of geometric data, which damages the real-time nature of the 3D landscape. Image-based 3D modeling technology overcomes the weakness of large geometric data sets, replacing compact models with text-rich images and creating more realistic 3D landscapes with less data. Therefore, this method also has shortcomings such as poor interactivity, low understanding of reality, and difficulty in integrating with 3DGIS database.(2) The Rapid Spread of Informatization and Networking. Today, planning visualization has developed a considerable technical foundation, and the lack of basic data and irregularities in the past no longer exist. The collection and collection of urban geographic information needs to reach a level of standardization and digitization. Many planning units have achieved paperless office, approval, and program evaluation. The use of computers and networks has become popular, and computer-aided drawing and design-aided design have also become common methods; these have shortened the planning and design cycle and enhanced the flexibility of the planning process; in addition, new concepts such as dynamic planning have emerged [14, 15]. In addition, with the development of remote sensing technology and geographic information models, digital topographic maps generated by aerial photography and satellite technology have begun to appear [16]. The standardization of these design, management, and planning informatization results has laid a good foundation for the visualization of information to go to the society and serve the public. It also prepares a data foundation for planning visualization. With the advent of the Internet age and the information age, resources and information are becoming more and more abundant, and the development of technical means is getting faster and faster. The digital and networked environment makes the realization of visualization more and more close to us [17, 18]. In urban planning and management, the design of urban long-distance network is a very important link, and its accuracy, stability, or indirectly affects the design, construction, and management of the city. Especially in recent years, many roads have been renovated and expanded, and many districts have been newly built, which has caused major changes in land pipelines. The previous data is outdated and cannot reflect the true state of the ground network. However, the traditional paper data update is very difficult, and it takes time and effort to ask questions.


## 2.2. 3D Visualization of Terrain

### 2.2.1. Topographic Data Structure Design

(1) Digital Elevation Model Modeling. A model form that describes terrain changes through discrete lattices, and its abstraction can be described as an ordered set of two-dimensional functions:
(1)Hp=fkUp,Vpk=1,2,⋯,n.Among them, the elevation information of a point (in fact, a tiny neighborhood of this point) is determined on the surfaceHp, usually relative to the standard altitude or a certain reference plane, and for this purpose, the coordinates of the landmark point UpVp under the geodetic projection. The modeling of the digital elevation model refers to the establishment of a realistic terrain surface through surface fitting in the visualization process [19, 20]. According to different data sources, there are three common modeling methods: based on irregular triangulation, based on regular grid, and based on contour model. The relationship between them is shown in Table 1.Table 1
Comparison of three digital elevation models.
StructureRegular gridContour lineIrregular triangulationData sizeView grid sizeSmallBigGeneration methodData interpolationRemote sensing topographic mapDiscrete point networkingTopological relationshipGoodNo goodGoodArbitrary point interpolationDirectIndirectDirectSuitable for terrainGentle but not severeGentle but not severeArbitrary terrainAmong them, the triangle mesh model can be changed in size and shape according to the actual degree of terrain change, but its topological rules are complicated, and it is difficult to establish and store, so it is usually used for small-scale terrain data with local high changes, etc. The high-line model expresses topographical changes through a collection of iso-elevation data and corresponding elevation values. This data generation method will generate obvious step mutations and cannot be smooth and continuous [21]. Public 3D viewing must take into account the characteristics of the subject matter, not just the data object itself. In recent years, researchers have made valuable discoveries. In the process of perception, what is made in the human brain is a complete and complete model of the various properties, parts, and relationships of objects. When people observe a very large area of modern cities, they all adopt a technological approach to gain an understanding of the general structure of the city.When performing regular grid interpolation, there are mainly three interpolation ideas: overall interpolation, partial block interpolation, and point-by-point interpolation. In the global digital elevation model, if the overall data is used for interpolation, there will be a phenomenon of high interpolation, which is obviously distorted, and sparse sampling makes the point-by-point interpolation effect unsatisfactory [22]. Therefore, this paper adopts local interpolation. In the common local interpolation method, bilinear surface is selected to describe the topographical change, and its function form is
(2)Z=a0+a1x+a2y+a3xy.Suppose four elevation sampling pointsP1x1,y1,z1, P2x2,y2,z2, P3x3,y3,z3, and P4x4,y4,z4 and the four parameters are calculated as follows:
(3)a0a1a2a3=1x1y1x1y11x2y2x2y21x3y3x3y31x4y4x4y4z0z1z2z3.After obtaining the four parameters, you can bring in the coordinates to know the elevation value estimated inside the grid. In the actual project, it is found that when the tree-shaped multiresolution structure is used to represent the terrain, the elevation mutation caused by the different interpolation coefficients will appear at the adjacent tiles. In order to eliminate this effect, it is necessary to leave the hierarchy when generating the hierarchical structure processing to eliminate the impact. With the rapid development of smart cities and mobile Internet, users will increase the demand for 3D mapping technology, especially large 3D cities limited by projector display capabilities and network transmission capabilities give a solution.(2) Tile Pyramid Structure. The tile pyramid model is a common multiresolution hierarchical structure. Its core idea is to dynamically select a suitable resolution through the change of the visual domain range during the zooming process of the scene by layering and partitioning [23]. Through this method of presegmentation and block division, it is directly transmitted through the network when the user actually uses it without real-time sampling calculation. Suppose the original resolution of the image data is R0 and the magnification is m, then the resolution of the layer data Rk is equal to:
(4)Rk=R0×mk.(3) Frustum Culling Algorithm. Since the terrain tiles are divided into squares, the maximum elevation is generally lower than the side length of the square. Let the terrain tiles be tile, the radius of the tile enclosing sphere is R, and the directed distance from the center of the sphere to each clipping plane of the frustum is Di,i∈0,5, and the relationship of is tilei obtained by distance judgment:
(5)tilei=outside,Di≤−R,inside,Di≤R,inside,Di>−R.After calculating the orientation of the tile and the six clipping planes, at that time, the tile∀tilei=outside was considered invisible and was removed; otherwise, the tile completely contained or intersected with the frustum, keep this tile [24, 25].


### 2.2.2. Topographic Factors

According to the digital elevation model, the approximate surface, that is, the approximate surface is determined for each grid unit. The slope angleθ between the approximate surface and the horizontal plane is used to represent the slope angle of the basic face element θ, which can be determined by the vector method [26, 27]. Z1, Z2, Z3, and Z4 are the absolute elevation of the corner point of the approximated surface; a and bare the vector, N is the normal vector of the plane xoy, and n is the normal vector of the approximated surface; the slope angle θ is calculated as follows:
(6)tgθ=az2+bz2d.Converted:(7)θ=arctgaz2+bz2d.Among them:(8)az=−Z1+Z2−Z3+Z42.Through formulas (6), (7), and (8), we can get the algorithm source of formula (9):
(9)bz=−Z1−Z2+Z3+Z42.d is the grid spacing, tgθ is the slope of this basic facet [28], and θis the slope angle.


### 2.2.3. Calculation of Aspect

Suppose that the angle [29] between the projection of the normal vector of the approximation surface xoy of the fundamental face element on the plane and the axis x is β, then
(10)tgβ=−azbz.Buttgβ is a periodic function [30, 31], which β can be taken in the range of 0° to 360°, but arctg−a/b can only be taken in the range of (-90°, 90°), so β cannot be represented, and further judgment is needed.


### 2.2.4. Surface Area Calculation

The surface area of the surface unit can be easily obtained from the digital elevation model. According to the physical properties of the space vector, the surface area of the unit surface can be expressed as(11)si,j=ni,j=Δy2zi,j+zi,j+1−zi+1,j−zi+1,j+12/4+Δx2zi,j+1+zi+1,j+1−zi,j−zi+1,j2/4+Δx2Δy22.The surface area is calculated as the real area of the surface grid unit [20], and the surface area can be solved by this method.


## 2.2.1. Topographic Data Structure Design

(1) Digital Elevation Model Modeling. A model form that describes terrain changes through discrete lattices, and its abstraction can be described as an ordered set of two-dimensional functions:
(1)Hp=fkUp,Vpk=1,2,⋯,n.Among them, the elevation information of a point (in fact, a tiny neighborhood of this point) is determined on the surfaceHp, usually relative to the standard altitude or a certain reference plane, and for this purpose, the coordinates of the landmark point UpVp under the geodetic projection. The modeling of the digital elevation model refers to the establishment of a realistic terrain surface through surface fitting in the visualization process [19, 20]. According to different data sources, there are three common modeling methods: based on irregular triangulation, based on regular grid, and based on contour model. The relationship between them is shown in Table 1.Table 1
Comparison of three digital elevation models.
StructureRegular gridContour lineIrregular triangulationData sizeView grid sizeSmallBigGeneration methodData interpolationRemote sensing topographic mapDiscrete point networkingTopological relationshipGoodNo goodGoodArbitrary point interpolationDirectIndirectDirectSuitable for terrainGentle but not severeGentle but not severeArbitrary terrainAmong them, the triangle mesh model can be changed in size and shape according to the actual degree of terrain change, but its topological rules are complicated, and it is difficult to establish and store, so it is usually used for small-scale terrain data with local high changes, etc. The high-line model expresses topographical changes through a collection of iso-elevation data and corresponding elevation values. This data generation method will generate obvious step mutations and cannot be smooth and continuous [21]. Public 3D viewing must take into account the characteristics of the subject matter, not just the data object itself. In recent years, researchers have made valuable discoveries. In the process of perception, what is made in the human brain is a complete and complete model of the various properties, parts, and relationships of objects. When people observe a very large area of modern cities, they all adopt a technological approach to gain an understanding of the general structure of the city.When performing regular grid interpolation, there are mainly three interpolation ideas: overall interpolation, partial block interpolation, and point-by-point interpolation. In the global digital elevation model, if the overall data is used for interpolation, there will be a phenomenon of high interpolation, which is obviously distorted, and sparse sampling makes the point-by-point interpolation effect unsatisfactory [22]. Therefore, this paper adopts local interpolation. In the common local interpolation method, bilinear surface is selected to describe the topographical change, and its function form is
(2)Z=a0+a1x+a2y+a3xy.Suppose four elevation sampling pointsP1x1,y1,z1, P2x2,y2,z2, P3x3,y3,z3, and P4x4,y4,z4 and the four parameters are calculated as follows:
(3)a0a1a2a3=1x1y1x1y11x2y2x2y21x3y3x3y31x4y4x4y4z0z1z2z3.After obtaining the four parameters, you can bring in the coordinates to know the elevation value estimated inside the grid. In the actual project, it is found that when the tree-shaped multiresolution structure is used to represent the terrain, the elevation mutation caused by the different interpolation coefficients will appear at the adjacent tiles. In order to eliminate this effect, it is necessary to leave the hierarchy when generating the hierarchical structure processing to eliminate the impact. With the rapid development of smart cities and mobile Internet, users will increase the demand for 3D mapping technology, especially large 3D cities limited by projector display capabilities and network transmission capabilities give a solution.(2) Tile Pyramid Structure. The tile pyramid model is a common multiresolution hierarchical structure. Its core idea is to dynamically select a suitable resolution through the change of the visual domain range during the zooming process of the scene by layering and partitioning [23]. Through this method of presegmentation and block division, it is directly transmitted through the network when the user actually uses it without real-time sampling calculation. Suppose the original resolution of the image data is R0 and the magnification is m, then the resolution of the layer data Rk is equal to:
(4)Rk=R0×mk.(3) Frustum Culling Algorithm. Since the terrain tiles are divided into squares, the maximum elevation is generally lower than the side length of the square. Let the terrain tiles be tile, the radius of the tile enclosing sphere is R, and the directed distance from the center of the sphere to each clipping plane of the frustum is Di,i∈0,5, and the relationship of is tilei obtained by distance judgment:
(5)tilei=outside,Di≤−R,inside,Di≤R,inside,Di>−R.After calculating the orientation of the tile and the six clipping planes, at that time, the tile∀tilei=outside was considered invisible and was removed; otherwise, the tile completely contained or intersected with the frustum, keep this tile [24, 25].


## 2.2.2. Topographic Factors

According to the digital elevation model, the approximate surface, that is, the approximate surface is determined for each grid unit. The slope angleθ between the approximate surface and the horizontal plane is used to represent the slope angle of the basic face element θ, which can be determined by the vector method [26, 27]. Z1, Z2, Z3, and Z4 are the absolute elevation of the corner point of the approximated surface; a and bare the vector, N is the normal vector of the plane xoy, and n is the normal vector of the approximated surface; the slope angle θ is calculated as follows:
(6)tgθ=az2+bz2d.Converted:(7)θ=arctgaz2+bz2d.Among them:(8)az=−Z1+Z2−Z3+Z42.Through formulas (6), (7), and (8), we can get the algorithm source of formula (9):
(9)bz=−Z1−Z2+Z3+Z42.d is the grid spacing, tgθ is the slope of this basic facet [28], and θis the slope angle.


## 2.2.3. Calculation of Aspect

Suppose that the angle [29] between the projection of the normal vector of the approximation surface xoy of the fundamental face element on the plane and the axis x is β, then
(10)tgβ=−azbz.Buttgβ is a periodic function [30, 31], which β can be taken in the range of 0° to 360°, but arctg−a/b can only be taken in the range of (-90°, 90°), so β cannot be represented, and further judgment is needed.


## 2.2.4. Surface Area Calculation

The surface area of the surface unit can be easily obtained from the digital elevation model. According to the physical properties of the space vector, the surface area of the unit surface can be expressed as(11)si,j=ni,j=Δy2zi,j+zi,j+1−zi+1,j−zi+1,j+12/4+Δx2zi,j+1+zi+1,j+1−zi,j−zi+1,j2/4+Δx2Δy22.The surface area is calculated as the real area of the surface grid unit [20], and the surface area can be solved by this method.


## 3. Visualization Technology and Experimental Design of Cluster Application Model Architecture

### 3.1. Data Preparation

Interpretation data such as earthquake and drilling are commonly used modeling data sources in SKUA-GOCAD. Considering that the test area is small and there are buildings under construction on the ground, no drilling and seismic methods are used, but transients are used. Electromagnetic geophysical methods combined with expert experience inversion and finally obtained the geological layered profile of the test area. The establishment of the three-dimensional structural model of the experimental area is mainly based on the profile data, and the depth of the model establishment is 200 meters above sea level from the surface to the underground. A total of 5 transient electromagnetic measurement lines were set up in the test area. The coordinates and elevations of the starting and ending points are shown in Table2.Table 2
Transient electromagnetic measuring line start and end coordinates.
Line numberEndpoint numberPlane coordinatesElevationXYHABA3368643.313401213.057431.459B3367249.552402853.602447.558CDC3368933.378401152.239455.568D3367029.613402986.887494.535EFE3368597.542401831.119453.048F3367632.677403481.072475.526GHG3368954.667401461.174470.508H3367744.295403237076477.474IJI3368849.272402149.206467.493J3367843.981403153.732553.968


### 3.2. Modeling Scope

The research object of this paper is mainly 3D urban space. To ensure the complete integration of the final 3D scene, the geological structure model in the geoscience space should keep the same scope with the geospatial model. The coordinates of the inflection point of the recording model range are the following: North West:x=401992.479, y=3369679.002; North East: x=402441.823, y=3368392.535; South West: x=408085.112,y=3367401.984; and South East: x=402257.469, y=3367115.517. Save the four-point coordinate value as a text file format for determining the scope of 3D structural modeling in SKUA-GOCAD. In recent years, imaging and data mining technologies have developed rapidly, and information technologies such as the Internet of Things and cloud computing have become a gradual process. IBM developed the concept of “smart cities” in 2009. The key features of smart cities with “extensive connections” and “more comprehensive overview,” i.e., real and accurate transmission of information over the network, require efficient imaging methods.


### 3.3. Data Import

SKUA-GOCAD supports the import of multiple tier data formats. Here, the tier data is imported in a column-based format. Choose to save as a geological boundary point file in .txt format. The interpretation point data of each layer in the profile is organized in a text file based on column format according to number,X coordinate, Y coordinate, and Z coordinate. Open the layer interpretation data import wizard in SKUA-GOCAD, and import the saved .txt file.


### 3.4. Data Preprocessing

The section obtained by the inversion of transient electromagnetic measurement is two-dimensional data. The geological boundary in the section needs to be vectorized, and then, the two-dimensional section is converted into three-dimensional spatial data through coordinate correction. The process is shown in Figure2.Figure 2
Data preprocessing flowchart (image source:https://image.cnki.net/).The principle of coordinate conversion is in a two-dimensional plane, they-coordinate in the drawing should be the z-coordinate in the three-dimensional space, and the x-coordinate in the drawing should be the north coordinate in the three-dimensional space. Finally, the x value in the three-dimensional space should be 0. After the coordinate axis definition is completed, the relationship between the actual coordinates of the two control points in the section line file and the three-dimensional coordinates needs to be established to complete the final conversion process from two-dimensional to three-dimensional. The coordinate conversion process of the profile data in this article is all completed in the Surpac software. The digital city is an important part of the digital world. It is the foundation of digitalization and understanding of modern city management. Digital imaging is one of the basic technologies of digital citizenship. This book first explores the 3D imaging and information technology of digital cities. Further improve the 3DGIS spatial sound design, design a more integrated 3D spatial data system for specific GIS applications, and make the reconstruction of 3D world objects faster, simpler, and more intuitive.


## 3.1. Data Preparation

Interpretation data such as earthquake and drilling are commonly used modeling data sources in SKUA-GOCAD. Considering that the test area is small and there are buildings under construction on the ground, no drilling and seismic methods are used, but transients are used. Electromagnetic geophysical methods combined with expert experience inversion and finally obtained the geological layered profile of the test area. The establishment of the three-dimensional structural model of the experimental area is mainly based on the profile data, and the depth of the model establishment is 200 meters above sea level from the surface to the underground. A total of 5 transient electromagnetic measurement lines were set up in the test area. The coordinates and elevations of the starting and ending points are shown in Table2.Table 2
Transient electromagnetic measuring line start and end coordinates.
Line numberEndpoint numberPlane coordinatesElevationXYHABA3368643.313401213.057431.459B3367249.552402853.602447.558CDC3368933.378401152.239455.568D3367029.613402986.887494.535EFE3368597.542401831.119453.048F3367632.677403481.072475.526GHG3368954.667401461.174470.508H3367744.295403237076477.474IJI3368849.272402149.206467.493J3367843.981403153.732553.968


## 3.2. Modeling Scope

The research object of this paper is mainly 3D urban space. To ensure the complete integration of the final 3D scene, the geological structure model in the geoscience space should keep the same scope with the geospatial model. The coordinates of the inflection point of the recording model range are the following: North West:x=401992.479, y=3369679.002; North East: x=402441.823, y=3368392.535; South West: x=408085.112,y=3367401.984; and South East: x=402257.469, y=3367115.517. Save the four-point coordinate value as a text file format for determining the scope of 3D structural modeling in SKUA-GOCAD. In recent years, imaging and data mining technologies have developed rapidly, and information technologies such as the Internet of Things and cloud computing have become a gradual process. IBM developed the concept of “smart cities” in 2009. The key features of smart cities with “extensive connections” and “more comprehensive overview,” i.e., real and accurate transmission of information over the network, require efficient imaging methods.


## 3.3. Data Import

SKUA-GOCAD supports the import of multiple tier data formats. Here, the tier data is imported in a column-based format. Choose to save as a geological boundary point file in .txt format. The interpretation point data of each layer in the profile is organized in a text file based on column format according to number,X coordinate, Y coordinate, and Z coordinate. Open the layer interpretation data import wizard in SKUA-GOCAD, and import the saved .txt file.


## 3.4. Data Preprocessing

The section obtained by the inversion of transient electromagnetic measurement is two-dimensional data. The geological boundary in the section needs to be vectorized, and then, the two-dimensional section is converted into three-dimensional spatial data through coordinate correction. The process is shown in Figure2.Figure 2
Data preprocessing flowchart (image source:https://image.cnki.net/).The principle of coordinate conversion is in a two-dimensional plane, they-coordinate in the drawing should be the z-coordinate in the three-dimensional space, and the x-coordinate in the drawing should be the north coordinate in the three-dimensional space. Finally, the x value in the three-dimensional space should be 0. After the coordinate axis definition is completed, the relationship between the actual coordinates of the two control points in the section line file and the three-dimensional coordinates needs to be established to complete the final conversion process from two-dimensional to three-dimensional. The coordinate conversion process of the profile data in this article is all completed in the Surpac software. The digital city is an important part of the digital world. It is the foundation of digitalization and understanding of modern city management. Digital imaging is one of the basic technologies of digital citizenship. This book first explores the 3D imaging and information technology of digital cities. Further improve the 3DGIS spatial sound design, design a more integrated 3D spatial data system for specific GIS applications, and make the reconstruction of 3D world objects faster, simpler, and more intuitive.


## 4. Experimental Visualization Technology and Cluster Application Model Architecture

### 4.1. Realization of 3D Module

First, analyze the program structure of the three-dimensional module. The program structure diagram of the 3D module is shown in Figure3.Figure 3
Three-dimensional module program structure diagram.It can be seen from Figure3 that the model is first generated by a Windows application (MFC frame) to generate a frame (FRAME), a document (DOCUMNT), and a view (VIEW), and then through the interactive support of the 2D module and the model library module, a series of operation signs are generated, and then through the control of the VGThread thread in the VGOBJ library, call a series of objects derived from the VGOBJ library (such as VGObject, VGTerrain, VGRoad, and VGDynamicObject), through the scheduling of the Vega-LADBM module in the scene manager; in the VegaAPI runtime environment with support, the generated roaming scene is displayed in the view. At the same time, the document (DOCUMNT) also includes the storage of the model and the management function of the scene. The corresponding attribute table (such as building, model, and model-feature) is established to realize the storage and management of models and model attributes. The concept of complete geospatial information visualization mainly includes scientific computing visualization, data visualization, and information visualization. The core of geospatial information visualization technology is to provide users with an intuitive and interactive visualization environment for spatial information.


### 4.2. Cluster Modeling Implementation

By setting a different number of points, under the condition of the upper limit of the same category (n=50), the modeling of the 3D city visualization model takes time and searches for three different sizes of areas (full, 1/2, 1/4). Time-consuming statistics, the results are shown in Figure 4.Figure 4
Time-consuming modeling of 3D city visualization models of different capacities.It can be seen from Figure4 that with different numbers of points, the modeling time of the 3D city visualization model increases approximately exponentially with the size of the capacity. When the capacity of the cluster exceeds a certain value, the time spent on modeling will also increase rapidly. In most cases, the modeling time of the 3D city visualization model is predictable, but in a few cases, we may not be able to predict the modeling time, which greatly increases the difficulty of the experiment.It can be seen from Figure5 that from the cluster capacity of 5,000 to 100,000, no matter how much the modeling time is different, whether it is to search the entire territory or part of the scope, the search time of the 3D city visualization model is less than 20 ms. This It shows that the search efficiency of the 3D city visualization model is very high. Before the capacity of the cluster was 30,000, the time spent on modeling the 3D city visualization model and searching in three different areas was slowly increasing. From the capacity of 30,000 to about 80,000, the time spent on searching for the 3D city visualization model increased rapidly. After 80,000, the time-consuming search gradually flattened out. Since the quarter-region search is basically in a straight line under different cluster capacities, this indicates that the search in a smaller range than the entire territory and 1/2-region is time-consuming and is less affected by the capacity of the cluster. As the capacity of the cluster becomes larger, the difference in search time in three different regions for clusters of the same capacity is constantly changing. For example, before the capacity is 30,000, the absolute amount of time difference is very small.Figure 5
Comparison of time-consuming search for 3D city visualization models.


### 4.3. 3D Urban Space Scene Integration

#### 4.3.1. Create a Scene

Add a 3D scene document to the Map GIS workspace so that you can conveniently and quickly view and manage geographic elements in the 3D view. The Map GIS scene provides a total of two view modes: plane mode and spherical mode. In a 3D scene, users can add model layers, terrain layers, annotation layers, vector layers, and service layers and can set the attributes of these layers to get a better display effect.The layer management level in the 3D scene is shown in Figure6. According to the different ways of adding and using in the scene, the manageable layers in the 3D scene are divided into three categories: general layers, service layers, and maps. General layers include model layers, terrain layers, annotation layers, and vector layers.Figure 6
Map GIS 3D scene layer organization.


#### 4.3.2. Scene Display

The coordinate system used in this test area is a plane projection coordinate system, so a new plane scene needs to be created in Map GIS to complete the scene integration. The cluster display effect of the three-dimensional urban space model is shown in Figure7.Figure 7
3D city space model cluster display renderings (image source:https://image.cnki.net/).


#### 4.3.3. Application of 3D Urban Space Model

In the actual three-dimensional space, in order to facilitate the highlighting or interpretation of the model, you can use the three-dimensional labeling function. Map GIS provides a total of four labeling methods: text labeling, picture labeling, graphic labeling, and bubble labeling. The effect of using text labeling the picture is shown in Figure8.Figure 8
3D scene annotation effect (image source:https://image.cnki.net/).In the real world, there are some dynamic display effects. The Map GIS particle system management function provides a way to add these display effects, and at the same time, you can customize the dynamic display effects as needed. In the Map GIS system, all dynamic display special effects are simulated as the regular movement of several particles. From the analysis of particle motion trajectory, particle special effects can be divided into two processes: generation and movement process. Generation is the emission process of particles, which is controlled by the emitter, such as the number of particles and the initial movement angle. The movement process will be affected by the environment and controlled by the influencer, such as the magnitude and direction of the force during the movement and the color change during the movement. Figure9 shows the special effects of the snow scene.Figure 9
Snow scene special effects (image source:https://image.cnki.net/).The target point refers to theXYZ coordinates of the end point, that is, the end point of the view range; the center point is the XYZ coordinates of the view point position; the view distance is the distance between the target point and the center point; the azimuth angle is the angle of rotation along the X axis from the center point to the target point; the pitch angle is the angle of rotation from the center point to the target point along the Y axis; the horizontal angle is the angle that the direction from the center point to the target point is translated along the XY axis. This angle determines the distance in the horizontal direction of the viewing area; the vertical included angle is the angle that the direction from the center point to the target point is translated along the Z axis, and this angle determines the distance in the vertical direction of the viewing area. Figure 10(a) is an effect diagram of visual domain analysis at a certain point. Shadow rate analysis refers to calculating the length of time that the area can be irradiated by the sun in a certain period of time according to the geographical range of the designated area. At the same time, according to the specified maximum and minimum height, sampling distance, and sampling frequency, the lighting information in the designated area is obtained. The lighting value represents the percentage of the time between the start time and the end time of the sunshine time at that place. Figure 10(b) is the effect of shadow ratio analysis in a certain area. Green indicates the point with better lighting, red indicates the point with poor lighting effect, and the remaining points are in between.Figure 10
Various scene analysis diagrams (image source:https://image.cnki.net/).
(a)
Visual field analysis (the red part is not visible, the green part is visible, and the inclination angle is +20)(b)
Shadow rate analysis (different daylighting rate, different color)


## 4.1. Realization of 3D Module

First, analyze the program structure of the three-dimensional module. The program structure diagram of the 3D module is shown in Figure3.Figure 3
Three-dimensional module program structure diagram.It can be seen from Figure3 that the model is first generated by a Windows application (MFC frame) to generate a frame (FRAME), a document (DOCUMNT), and a view (VIEW), and then through the interactive support of the 2D module and the model library module, a series of operation signs are generated, and then through the control of the VGThread thread in the VGOBJ library, call a series of objects derived from the VGOBJ library (such as VGObject, VGTerrain, VGRoad, and VGDynamicObject), through the scheduling of the Vega-LADBM module in the scene manager; in the VegaAPI runtime environment with support, the generated roaming scene is displayed in the view. At the same time, the document (DOCUMNT) also includes the storage of the model and the management function of the scene. The corresponding attribute table (such as building, model, and model-feature) is established to realize the storage and management of models and model attributes. The concept of complete geospatial information visualization mainly includes scientific computing visualization, data visualization, and information visualization. The core of geospatial information visualization technology is to provide users with an intuitive and interactive visualization environment for spatial information.


## 4.2. Cluster Modeling Implementation

By setting a different number of points, under the condition of the upper limit of the same category (n=50), the modeling of the 3D city visualization model takes time and searches for three different sizes of areas (full, 1/2, 1/4). Time-consuming statistics, the results are shown in Figure 4.Figure 4
Time-consuming modeling of 3D city visualization models of different capacities.It can be seen from Figure4 that with different numbers of points, the modeling time of the 3D city visualization model increases approximately exponentially with the size of the capacity. When the capacity of the cluster exceeds a certain value, the time spent on modeling will also increase rapidly. In most cases, the modeling time of the 3D city visualization model is predictable, but in a few cases, we may not be able to predict the modeling time, which greatly increases the difficulty of the experiment.It can be seen from Figure5 that from the cluster capacity of 5,000 to 100,000, no matter how much the modeling time is different, whether it is to search the entire territory or part of the scope, the search time of the 3D city visualization model is less than 20 ms. This It shows that the search efficiency of the 3D city visualization model is very high. Before the capacity of the cluster was 30,000, the time spent on modeling the 3D city visualization model and searching in three different areas was slowly increasing. From the capacity of 30,000 to about 80,000, the time spent on searching for the 3D city visualization model increased rapidly. After 80,000, the time-consuming search gradually flattened out. Since the quarter-region search is basically in a straight line under different cluster capacities, this indicates that the search in a smaller range than the entire territory and 1/2-region is time-consuming and is less affected by the capacity of the cluster. As the capacity of the cluster becomes larger, the difference in search time in three different regions for clusters of the same capacity is constantly changing. For example, before the capacity is 30,000, the absolute amount of time difference is very small.Figure 5
Comparison of time-consuming search for 3D city visualization models.


## 4.3. 3D Urban Space Scene Integration

### 4.3.1. Create a Scene

Add a 3D scene document to the Map GIS workspace so that you can conveniently and quickly view and manage geographic elements in the 3D view. The Map GIS scene provides a total of two view modes: plane mode and spherical mode. In a 3D scene, users can add model layers, terrain layers, annotation layers, vector layers, and service layers and can set the attributes of these layers to get a better display effect.The layer management level in the 3D scene is shown in Figure6. According to the different ways of adding and using in the scene, the manageable layers in the 3D scene are divided into three categories: general layers, service layers, and maps. General layers include model layers, terrain layers, annotation layers, and vector layers.Figure 6
Map GIS 3D scene layer organization.


### 4.3.2. Scene Display

The coordinate system used in this test area is a plane projection coordinate system, so a new plane scene needs to be created in Map GIS to complete the scene integration. The cluster display effect of the three-dimensional urban space model is shown in Figure7.Figure 7
3D city space model cluster display renderings (image source:https://image.cnki.net/).


### 4.3.3. Application of 3D Urban Space Model

In the actual three-dimensional space, in order to facilitate the highlighting or interpretation of the model, you can use the three-dimensional labeling function. Map GIS provides a total of four labeling methods: text labeling, picture labeling, graphic labeling, and bubble labeling. The effect of using text labeling the picture is shown in Figure8.Figure 8
3D scene annotation effect (image source:https://image.cnki.net/).In the real world, there are some dynamic display effects. The Map GIS particle system management function provides a way to add these display effects, and at the same time, you can customize the dynamic display effects as needed. In the Map GIS system, all dynamic display special effects are simulated as the regular movement of several particles. From the analysis of particle motion trajectory, particle special effects can be divided into two processes: generation and movement process. Generation is the emission process of particles, which is controlled by the emitter, such as the number of particles and the initial movement angle. The movement process will be affected by the environment and controlled by the influencer, such as the magnitude and direction of the force during the movement and the color change during the movement. Figure9 shows the special effects of the snow scene.Figure 9
Snow scene special effects (image source:https://image.cnki.net/).The target point refers to theXYZ coordinates of the end point, that is, the end point of the view range; the center point is the XYZ coordinates of the view point position; the view distance is the distance between the target point and the center point; the azimuth angle is the angle of rotation along the X axis from the center point to the target point; the pitch angle is the angle of rotation from the center point to the target point along the Y axis; the horizontal angle is the angle that the direction from the center point to the target point is translated along the XY axis. This angle determines the distance in the horizontal direction of the viewing area; the vertical included angle is the angle that the direction from the center point to the target point is translated along the Z axis, and this angle determines the distance in the vertical direction of the viewing area. Figure 10(a) is an effect diagram of visual domain analysis at a certain point. Shadow rate analysis refers to calculating the length of time that the area can be irradiated by the sun in a certain period of time according to the geographical range of the designated area. At the same time, according to the specified maximum and minimum height, sampling distance, and sampling frequency, the lighting information in the designated area is obtained. The lighting value represents the percentage of the time between the start time and the end time of the sunshine time at that place. Figure 10(b) is the effect of shadow ratio analysis in a certain area. Green indicates the point with better lighting, red indicates the point with poor lighting effect, and the remaining points are in between.Figure 10
Various scene analysis diagrams (image source:https://image.cnki.net/).
(a)
Visual field analysis (the red part is not visible, the green part is visible, and the inclination angle is +20)(b)
Shadow rate analysis (different daylighting rate, different color)


## 4.3.1. Create a Scene

Add a 3D scene document to the Map GIS workspace so that you can conveniently and quickly view and manage geographic elements in the 3D view. The Map GIS scene provides a total of two view modes: plane mode and spherical mode. In a 3D scene, users can add model layers, terrain layers, annotation layers, vector layers, and service layers and can set the attributes of these layers to get a better display effect.The layer management level in the 3D scene is shown in Figure6. According to the different ways of adding and using in the scene, the manageable layers in the 3D scene are divided into three categories: general layers, service layers, and maps. General layers include model layers, terrain layers, annotation layers, and vector layers.Figure 6
Map GIS 3D scene layer organization.


## 4.3.2. Scene Display

The coordinate system used in this test area is a plane projection coordinate system, so a new plane scene needs to be created in Map GIS to complete the scene integration. The cluster display effect of the three-dimensional urban space model is shown in Figure7.Figure 7
3D city space model cluster display renderings (image source:https://image.cnki.net/).


## 4.3.3. Application of 3D Urban Space Model

In the actual three-dimensional space, in order to facilitate the highlighting or interpretation of the model, you can use the three-dimensional labeling function. Map GIS provides a total of four labeling methods: text labeling, picture labeling, graphic labeling, and bubble labeling. The effect of using text labeling the picture is shown in Figure8.Figure 8
3D scene annotation effect (image source:https://image.cnki.net/).In the real world, there are some dynamic display effects. The Map GIS particle system management function provides a way to add these display effects, and at the same time, you can customize the dynamic display effects as needed. In the Map GIS system, all dynamic display special effects are simulated as the regular movement of several particles. From the analysis of particle motion trajectory, particle special effects can be divided into two processes: generation and movement process. Generation is the emission process of particles, which is controlled by the emitter, such as the number of particles and the initial movement angle. The movement process will be affected by the environment and controlled by the influencer, such as the magnitude and direction of the force during the movement and the color change during the movement. Figure9 shows the special effects of the snow scene.Figure 9
Snow scene special effects (image source:https://image.cnki.net/).The target point refers to theXYZ coordinates of the end point, that is, the end point of the view range; the center point is the XYZ coordinates of the view point position; the view distance is the distance between the target point and the center point; the azimuth angle is the angle of rotation along the X axis from the center point to the target point; the pitch angle is the angle of rotation from the center point to the target point along the Y axis; the horizontal angle is the angle that the direction from the center point to the target point is translated along the XY axis. This angle determines the distance in the horizontal direction of the viewing area; the vertical included angle is the angle that the direction from the center point to the target point is translated along the Z axis, and this angle determines the distance in the vertical direction of the viewing area. Figure 10(a) is an effect diagram of visual domain analysis at a certain point. Shadow rate analysis refers to calculating the length of time that the area can be irradiated by the sun in a certain period of time according to the geographical range of the designated area. At the same time, according to the specified maximum and minimum height, sampling distance, and sampling frequency, the lighting information in the designated area is obtained. The lighting value represents the percentage of the time between the start time and the end time of the sunshine time at that place. Figure 10(b) is the effect of shadow ratio analysis in a certain area. Green indicates the point with better lighting, red indicates the point with poor lighting effect, and the remaining points are in between.Figure 10
Various scene analysis diagrams (image source:https://image.cnki.net/).
(a)
Visual field analysis (the red part is not visible, the green part is visible, and the inclination angle is +20)(b)
Shadow rate analysis (different daylighting rate, different color)


## 5. Conclusions

(1)
Urban planning is the planning of the future direction of the city based on the social and economic development of the city. Therefore, the process of urban planning is directly related to the development of the city. With the advent of the information age, the storm of visualization has brought a profound revolution, which will make the level of urban planning more and more perfect and rich. This paper studies the acquisition and processing of three-dimensional data in the city model; classifies the three-dimensional information data of the city, emphatically analyzed the application background, application status, and feasibility technical route of visualization technology in urban planning and urban design; and combined with actual topics and realized the creation of urban design, and the results display platform, made a small attempt in the visualization of urban planning, and discussed the urban planning in the realization of digital urban planning(2)
The application categories of each stage and level put forward specific functional requirements. The shortcomings of this article are: the complete 3D geological modeling should include two parts: structural modeling and attribute modeling. Structural modeling provides the basic skeleton for attribute simulation, and attribute modeling makes the structural model work. Due to the lack of attribute simulation data in the test area, further research on attribute modeling has not been possible, and only relevant research on 3D structural modeling has been carried out, so attribute modeling will be the future research direction(3)
3D digital cities are widely used in the areas of urban planning, traffic control, and emergency response. At the same time, the development of digital cities is facing major challenges: on the one hand, advances in modeling technology have accelerated the rapid development of 3D cities. It is a description of three dimensional buildings. Devices and mobile Internet are increasing demand for personalized and 3D urban services based on the development of the Internet



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*Source: 6427468-2021-11-05.xml*