3D GIS空间数据建模技术探析

GIS

空间分析

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"3D GIS空间数据模拟" 是一本由Alias Abdul-Rahman和Morakot Pilouk编著的专业书籍，主要关注如何在GIS（地理信息系统）中处理三维空间数据和进行空间数据建模。该书对那些研究GIS技术的人来说具有一定的指导价值。在GIS领域，三维空间数据模拟是关键组成部分，它允许我们对地表、建筑物、地形等复杂环境进行精确的数字化再现。通过3D GIS，可以实现对城市规划、环境分析、地质灾害预测等多个领域的深度分析和决策支持。书中可能涵盖了以下关键知识点： 1. **空间数据模型**：介绍了不同类型的3D空间数据模型，如拓扑数据模型、矢量数据模型、栅格数据模型，以及它们在3D环境中的应用和优势。 2. **数据采集与处理**：讲解了如何获取三维空间数据，包括遥感影像解析、激光雷达（LiDAR）数据处理、无人机航拍等现代技术，并介绍了数据预处理步骤，如噪声去除、空洞填充和数据整合。 3. **3D建模技术**：详述了创建和编辑3D对象的方法，如使用多边形网格、BIM（建筑信息模型）集成，以及如何构建复杂的3D地形表面。 4. **空间分析**：探讨了3D空间分析的各个方面，如视线分析、阴影分析、体积计算、距离测量等，这些都是GIS在城市规划、环境影响评估中的核心工具。 5. **可视化与展示**：讲述了如何利用3D GIS进行高质有效的数据可视化，包括交互式地图、动态演示和虚拟现实技术，这些对理解和传达空间信息至关重要。 6. **软件与平台**：可能提到了一些主流的3D GIS软件，如ArcGIS、QGIS的3D功能，以及专业3D建模软件如SketchUp、CityGML等，并可能涉及软件的接口开发和定制化应用。 7. **版权与法律问题**：书中的警告部分提醒读者，使用任何出版物的内容时，必须遵守版权法，尤其是在复制、分发和存储数据时，应获得版权所有者的许可。 "3D GIS空间数据模拟" 是一本深入探讨3D空间数据处理与GIS建模的专业著作，对于想要掌握3D地理信息科学的读者来说，是一本不可多得的参考资源。通过学习这本书，读者能够提升在3D环境下进行空间分析和决策的能力，同时了解相关法规，确保合法合规使用数据。

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6 Chapter 1

location (see Figure 1.1a). DTM cannot accommodate a 3D (solid) object,

or a surface with multiple height values at a given planimetric location (see

Figure 1.1b and Figure 1.1c, respectively).

Although raster-based systems which could be regarded as 3D GISs are

available, they may not be able to maintain the knowledge about reality

available in the original data set. This knowledge may be lost because of

problems in resolution and resampling. As a remedy, the original data set

would have to be stored separately from the model, for example, for:

• recreating the model if the result proves to be unsatisfactory because of

unsuitable mathematical definition

• creating another model with different resolution

• merging with another data set to create a new model

• archiving as a reference to, or evidence of, the model.

These activities imply the need to store original data in an appropriate

structure ready for future use. Necessary information about the data should

be attached to each data element. In DTM for instance, information that a

line is a breakline should be kept because it will have an impact on the in-

terpolation. Similarly, other information can be attached which influences

data handling strategies.

Since neither CAD nor GISs can at present fulfil the requirements of earth

science applications, further research and development of a 3D GIS would

seem appropriate.

Who needs 3D GIS?

As in the popular 2D GIS for 2D spatial data, 3D GIS is for managing 3D

spatial data. Raper and Kelk (1991), Rongxing Li (1994), Förstner (1995),

and Bonham-Carter (1996) present some of the three dimensional applica-

tion areas in GIS, including:

• ecological studies

• environmental monitoring

• geological analysis

• civil engineering

• mining exploration

• oceanography

• architecture

• automatic vehicle navigation

• archaeology

INTRODUCTION 7

Objects with known or

well-defined spatial ex-

tent, location and prop-

erties

Objects with unknown

or not well-defined spa-

tial extent, location and

properties

Fig. 1.2 Two types of real world

objects with respect to their spatial

extent.

• 3D urban mapping

• landscape planning

• defence and intelligence

• command and control

The above applications may pro-

duce much more useful information

if they were handled in a 3D spatial

system, but 3D spatial objects on

the surface and subsurface appear to

demand more complex solutions

(e.g. in terms of modelling, analysis,

and visualization) than the existing

systems can offer.

1.3 The Need for 3D Spatial

Data Modelling

In addition to the problem of creating a system capable of offering 3D

modelling and functionality, there is a further problem concerning the type

of 3D model chosen as the basis for 3D GIS. The model contains knowl-

edge about reality, so we consider below the types of real world objects it

must represent. Two kinds of real world objects may be differentiated in

terms of prior knowledge about their shapes and location, as shown in Fig-

ure 1.2. In reality, objects from the two categories coexist. Traditional GIS

models the objects of each category independently with the result that two

separate kinds of systems or subsystems have been developed.

Raper (1989) has also defined these two categories of objects. The first

category, regarded as ‘sampling limited’, is for objects having discrete

properties and readily determined boundaries, such as buildings, roads,

bridges, land parcels, fault blocks, perched aquifers. The second category,

known as ‘definition limited’, is for objects having various properties that

can be defined by means of classification, using property ranges. For ex-

ample, soil strata may be classified by grain-size distribution; moisture

content, colloid or pollutant in the water by percentage ranges; carbon

monoxide in the air by concentration ranges, and so forth. Molenaar

(1994a) regards these objects as ‘fuzzy spatial objects’.

Separate modelling of these two categories of objects tends to contradict

the reality, which leads to difficulties in representing their relationships.

Such a question as, ‘how many of the people working in a 50-storey office

8 Chapter 1

building are affected by polluted air generated by vehicles in nearby streets

during rush hours?’ cannot be answered until the two separate models are

combined, as shown in Figure

1.3. Modelling them together

with more accurate represen-

tation of their relationships in

the 3D environment requires

the integrated 3D modelling.

Note also that the properties

of an object may be well de-

fined in some specific dimen-

sions and ill defined in others.

For example, given a DTM

data set representing a sur-

face, the planimetric extent of

regions at the elevation of 100

metres above mean sea level

cannot be defined until the re-

sult of interpolation based on a mathematical definition (for example, lin-

ear interpolation) is obtained. That is to say, although the spatial extent of

this region may be known in the z-dimension, the spatial extent in plani-

metry (x, y) has still to be discovered. The model must contain the aspect

allowing the appropriate operation, such as interpolation or classification,

if the required description of the properties of an object is to be obtained.

Apart from the problem of the separate modelling of the two types of ob-

jects, there remains the further problem of the separate modelling of an ob-

ject’s components. These components are relief and planar geometry asso-

ciated with thematic properties. This separation has resulted in

independent systems and data structures, DTM and 2D GIS, respectively.

The consequences are data redundancy, which may lead to uncertainty

when the two data sets are combined and only one data set has been up-

dated.

DTM can facilitate several GIS analyses and visualization taking into ac-

counts the third dimension. The spatial information stored in DTM and in

GIS, however, can only be related through coordinates. This implies that

relationships between different components may not be properly repre-

sented because of metric computation instead of topology. To overcome

this, information derived from DTM must be converted into a form GIS

can recognize. For example, information about a slope or height zone must

first be converted into a thematic layer of GIS for further overlaying before

Fig. 1.3 An example of two types of real

world objects

INTRODUCTION 9

the spatial analysis can be carried out. Imagine having information about

the relief, planimetry and themes integrated into one model, so that con-

version of such information as slope, height zone and so forth were no

longer necessary. Such a question as, ‘which land parcels are subject to

one-metre flooding?’ could be answered from one model. Integrated mod-

elling of this kind is evidently also required for 3D GIS.

1.4 Problems Associated with Spatial Modelling

Establishing a 3D GIS while taking into account the integration of the nec-

essary components and different types of objects requires the solution of

the following problems related to the spatial model representing reality:

1) Design of a spatial model

• design of an integrated data model, or a scheme, permitting the deri-

vation of a unified data structure capable of maintaining all the com-

ponents of the geometric representation of real world objects, whether

obtained from direct measurements or from derivations, in the same

database. Each geometric component must be capable of representing

a real world object differently understood by different people.

2) Construction of a spatial model

• development of appropriate means and methods for 3D data acquisi-

tion;

• coordinate transformation into common georeferencing when differ-

ent components are to be included into one database;

• development of a data structuring method that unites the data from

various inputs of multi sources into an integrated database capable of

being maintained by a single database management system;

• design of thematic classes to organize representation of real world ob-

jects with common aspects into the same category;

• solving the uncertainty arising from discrepancies from different data

sets during the integration process and converting the uncertainty into

a ‘data quality’ statement to be conveyed to the end user.

3) Utilization of a spatial model

• utilization of existing components, such as 2D data and DTM (back-

ward compatibility) and preparation of those components for future

incorporation into the higher-dimension model (forward compatibil-

ity) to save the costs of repeating data acquisition.

for 3D GIS

10 Chapter 1

• development of additional spatial operators and spatial analysis func-

tions;

• development of maneuverable graphic visualization permitting the se-

lection of appropriate viewpoints and representation enabling conven-

ient, adequate uncovering of the details of objects stored in the data-

base;

• design of 3D cartographic presentation of information, including

name placement, symbol, generalization, etc.;

• design of a user interface and query language allowing users access to

the integrated database;

• development of a spatial indexing structure that speeds up data re-

trieval and storage processes for the integrated database, including

specific (database) views for each user group and guidelines keeping

these views updated according to the core database;

• development of tools for navigating among different models stored in

databases at different sites and computing platforms.

4) Maintenance of spatial model

• design of updating procedures, including the development of consis-

tency rules ensuring the logical consistency and integrity of the inte-

grated database, especially during the updating process.

1.5 Previous Work

The status and progress of research in the 3D GIS field within the scope of

this monograph and the identification of solutions and remaining problems

are made clear from the following review of previous work.

The development of data models for a 3D GIS has branched in two direc-

tions. The first is the full 3D approach that looks directly into the design of

a data model suitable for 3D GIS. Molenaar (1989) proposes a formal data

structure (FDS) for a 3D vector map which may be regarded as a generali-

zation of the 2D version of FDS. Shibasaki and Shaobo (1992), Rikkers

(1994), and Wang (1994) have reported

experimental use of 3D FDS.

The second approach comes from the viewpoint referred to as the ‘integra-

tion of DTM and GIS’. DTM became a discipline in its own right in the

late 1950s (Miller and Laflamme, 1958). Fritsch (1990) has recognized the

work of Makarovic (1977) as a proposer of this integration. Males (1978)

though not addressing the integration issue, demonstrated the use of a

et al. (1993), Bric (1993), Bric et al.,

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