CC BY
62
УДК 528.9 Ch. Bolorchuluun
National University of Mongolia, email: [email protected]
D. Tuvshinbayar
Mongolian University of Agriculture
Ulan Bator, Mongolia, email: [email protected]
SYSTEMS FOR INTEGRATED 3D GEOINFORMATION
Introduction
The basic idea of integrating DTM-TIN and 2D GIS data has already been mentioned by Peucker ET AL. (1976). Since then various authors have worked on this topic. As an important property of an integration process, stipulated that the shape of the DTM-TIN should not be altered while adding nodes and edges of the 2D GIS data to it.
One main focus of current geographic information systems (GIS) research is the extension of traditional 2D data structures to incorporate height information (Goodchild, 1997), thus, to integrate digital terrain models (DTM) or products derived from them into GIS. These sloppy words already lead to the first clarification that is required.
The restriction of this research to 2.5D models has several reasons. Firstly, real 3D models are already discussed by numerous authors who work on 3D city models. Details of these approaches may hence be found there. Secondly, the existing national core geospatial data bases are mainly 2.5D, i. e. they provide a unique height value for a position in the horizontal plane in contradistinction to real 3D applications where a multitude of height values may exists at each position. It is one major intention of this article to discuss procedures which use as input the existing data sets. This will allow for upward compatibility of the existing data sets with new methods and thus will save investments that have been made.
A 3D geoinformation system (3D GIS) that integrates all the necessary elements into a 3D spatial model, and provides functions efficiently to create and utilize the 3D spatial model, must be constructed to fulfill several needs from the earth science community. Using a tool set provided by a 3D GIS, a 3D spatial model can be constructed to permit the virtual performance of many tasks that would otherwise be carried out in reality, or may be too difficult, expensive, or destructive to carry out at all.
In general, a 3D GIS should aim to integrate all the necessary elements of the spatial model and functions to create and utilize the spatial model efficiently. This chapter therefore analyses the architectures of GIS’s in order to assess their present status and development trend, to assist the definition of the direction of the related research and stimulate the development of 3D GIS that can readily be used. The first part of this chapter reviews some general aspects of systems for integrated 3D geoinformation, their functional components, and some related and available technological developments to support their functionality with respect to the user's perspective. The main concern is system usage, investment, maintenance, and
productivity and the reliability of information. With respect to an integrated 3D spatial model as a basis of 3D GIS, besides taking into account present developments in information and communication technology, four evolutionary stages of system architecture for 3D GIS are distinguished, ranging from loosely to well-constructed systems. It is also helpful to identify the technological developments to which the present thesis contributes. The last part of the chapter reviews current attempts in conceptual design aiming at a robust 3D spatial model with the potential to be the basis of a 3D GIS. This review helps evaluate the necessity for further development of the more integrative data model.
1. General Aspects
The different stages of the design explained in chapter 2 lead to the actual implementation of a geoinformation system used to produce a 3D geo-spatial model in the form of a database which contains a selected set of aspects of the reality. In addition to the production role of spatial model, a GIS should also play an interface role between human users and the database. This role includes data entry, information query and processing, and presentation. Data entry translates human knowledge into a component of the model conforming to the data model and data structure. Information query performs the reverse operation, by translating the electronic components into information made understandable to users by the presentation mechanism.
Another important role of the GIS is the maintenance and management of the database. The GIS should make updating the database possible. The management role is the handling of various requests from users and the activation of the appropriate operation on the database in response to a user's request.
Effective exploitation of the technological development requires the spatial model to:
- Include three-dimensional (3D) aspects of reality;
- Allow both direct and indirect representation of the determinate and indeterminate spatial objects respectively, at the desirable level of abstraction;
- Be capable of accommodating data from various sources and seamlessly integrating them into one spatial model.
The first requirement results from the fact that only limited spatial analysis of our 3D real world was possible until we had a 3D model. The direct representation referred to in the second requirement is suitable for determinate spatial objects. The original observations must be maintained by the model, so that knowledge about the reality is not degraded. Such a postulate favors the vector structure as a suitable basis of the model because the vector structure is resolution independent. Regarding the indeterminate spatial objects, their geometry cannot be directly represented in the model. An indirect representation must be provided through the spatial units formed from the proximal neighbours (which are, in fact, the direct representations of determinate spatial objects). Information about the indeterminate spatial objects can be obtained from the model by means of the classification or interpolation of property values of the neighbours, using those spatial units. Various forms of the (direct)
representation of determinate spatial objects (that is to say, in the form of points, lines, surfaces, bodies) should contribute to the derivation of the spatial extent of the indeterminate spatial objects. The relationships between the neighbours given by the direct representation must be used as constraints to make the derivation result accurate.
The next requirement points out the necessity of multiple representation in semantic terms (an object may have different meanings to different observers) and dimensionality (ranging from OD to 3D). The spatial database of a GIS should have the capability of storing and maintaining multiple representations. Integrating data from various sources implies that redundancy must be minimized, and human intervention is probably required to decide how uncertainly should be resolved. Moreover, to achieve a spatial model that represents well the relationships between real world objects, the underlying data structure must permit the unification of various types and components of representation of spatial objects {Pilouk and Tempfli 1994, Pilouk et al 1994).
2. Functional Components
The requirements listed above are supported by the five major functional components of a system for integrated geoinformation outlined below. This outline may be derived from existing 2D CISs and other systems that deal with 3D spatial modelling, such as CAD, DTM.
(1) Data acquisition
As a result of the limited capability of each sensor and for economic reasons, different sensors are used to acquire different types of data where georeferencing systems may not be the same and accuracy can vary. Problems of uncertainty arise when data from different sources have to be integrated.
There are different stages of data acquisition:
- Primary; the acquisition of raw data taken from the real world directly, for example, from terrestrial surveys, aerial photography, satellite imagery by remote sensing, synthetic images by radar, GPS, laser profiles
- Secondary; the extraction of information from the raw data or other existing documents, for example, by 2D map digitizing, or 3D digitizing in a stereo model, automated object recognition and reconstruction, interpretation, and classification.
Data input may be on-line if the data acquisition device can directly transmit data to the system database during acquisition, or off-line if the data is recorded in temporary storage and transmitted to the system database at a later stage. The system should provide adequate input channels for the different types of data acquisition.
The collection of attribute data must also be included at this stage. Such compilation may be possible during the information extraction from raw data if there is some prior knowledge about the objects; otherwise, field work may be necessary for collecting thematic data directly from the reality.
3D data may be obtained by means of measurement for example, terrestrial surveys, GPS, laser profiles, stereo digitizing, or by computation, for example, image matching, and monoplotting and should be georeferenced.
If the geometric measurements are carried out by a human operator, it is beneficial to use a topological structure for data storage. A topological structure provides an opportunity for the operator to acquire directly the spatial relationships between the spatial objects, and also to verify these relationships with the
representation in the database at the time of data acquisition. Direct recording
topology is more reliable than creating the relationships in a later stage. One thing for certain is that the creation of 3D spatial relationships based, for example, on metric computation is rather complicated. As yet, there are few algorithms available to
automate this process; some are still in the process of research and development. The
studies of 3D data acquisition by Bric (1993) show that a systematic approach that also captures some of the initial spatial relationships realized by a human operator during the time of data acquisition can significantly simplify the creation of a more complete topology at a later stage.
(2) Data structuring
Regardless of the data format, the data set obtained from the data acquisition must be transferred to this component for structuring in a proper format. The database management system must be able to recognize this format It must also comply with the conceptual, logical, and internal design.
(3) Data storage and management
Data organized in the required structure must be maintained in the storage media as a database. Many databases may be stored within one system. A subset of real world aspects is represented by a database in which the relationships between the data elements created during data structuring must be kept valid and up to date. The overall management of each database and its elements is the responsibility of the DBMS. This should provide the tools to deliver data to the client and ensure the validity and consistency of the database, especially when the state of the database has been changed by such operations as insertion, deletion, or modification of data elements. 3D GIS, this management task is one of the most important, because many functions, for example, hidden line and surface removal, and shading, exploit specific data structures to speed up the operations. The typical solution is to perform data conversion to reorganize the data into the required structure for each operation.
(4) Data processing
Data processing with respect to 3D modelling is far more complex than in 2D. A large number of data processing tasks, spatial and non spatial analyses, must be handled by this component of the system. Such simple tasks as the searching of data components using certain criteria and retrieving them from the database are known as query. Other processes might involve complex computation or classification, for example, coordinate transformation, computation of slope from DTM, interpolation
of contour lines or surfaces, computation of mean population per unit area, cartographic generalization, flow accumulation, or shortest path determination.
(5) Data output and information presentation
After obtaining the results from data processing, the information needs to be sent on as output to other media, or presented in an appropriate way. This output component of the system should provide a high quality cartographic and presentation capability to portray the information to users. The information may be in the form of graphics, tables, or reports. The graphic presentation of 3D geoinformation usually needs further processing to obtain an acceptable level of realism. These processes include perspective transformation, surface illumination, hidden line-surface removal, or texture mapping. Since the display media are still limited to 2D- paper, or a monitor screen- the final display still has to operate in a 2D manner, for example, by drawing pixels, line segments, and filling polygons.
Each of these output components of a system has been separately developed in the past and somehow many of these components have become disciplines of their own. The next section reviews the pertinent technologies related to each component.
3. Technological Supporting Functionality of 3D GIS
The emerging technologies related to 3D GIS are reviewed in this section. Those needed for common operations or routine tasks should be regarded as 'core components' of GIS. The others that strengthen and extend the functionality of a component GIS for special tasks may be regarded as 'supplementary components'. Justifying which of the following should be considered core components and which supplementary depends on the major application of GIS.
- Map digitizing, although only providing 2D information, is still an important technology for data acquisition provided. The completion of 3D coordinates for an object represented by a surface may be achieved by interpolating height from the DTM at every planimetric location. In many representations, especially in the case of man made objects of regular shape, like buildings, the 2D components like the footprints of the buildings, or their roof outlines shown on the 2D map, are still crucial information requiring further, but rather simple 3D completion by supplementing data in the form of height and elevation (Gruen et al 1993, Bric 1993).
- Terrestrial surveys make the acquisition of 3D information possible. State-of-the-art surveying instruments are supported by portable computers for recording field data that can be directly transferred to the GIS workstation.
- Global Positioning Systems (GPS) provide another means of 3D data acquisition. GPS is based on NAV star satellites that help determine positions in the WGS coordinate system.
- Borehole and seismic surveys provide the means for 3D data acquisition specific to geological applications. This type of data acquisition is rather expensive and cannot provide comprehensive data to represent the real situation.
- Photogrammetry, in many cases, provides the means to obtain 3D information. Measurements can pass directly into a stereo model of photographs or images taken from the aircraft or satellite, and simultaneous interpretation of some spatial relationships may also be possible. The indirect approach to producing 3D information is usually based on such digital techniques as object recognition and image matching. A digital photogrammetric workstation that exploits computer graphics also makes it possible to overlay vector data with raster images, providing 3D visualization in stereo mode that helps verify the data contents. This is very convenient for change detection.
- Digital Terrain Model is used for the representation of relief. Two forms of DTM are typically used: regular-grid (also known as raster DTM); triangular irregular network (TIN). DTM supports various computation-based analyses in GIS, such as slope, aspect, visibility, volumes, contour lines, surface area, the derivation of a morphological feature.
- Remote sensing is a technology for primary data acquisition producing images, mostly in digital form, from sensors on satellites such as LANDSAT, SPOT, ERS1, MOSS, or sensors mounted on aircraft which provide images with higher resolution. Information can be extracted by interpreting the raw image using manual digitizing, or digital image processing techniques, if images are provided in a digital form.
- Digital Image Processing (DIP) is used for information extraction and spatial analysis. Raw images can be processed by certain operators and the results classified into themes. DIP is increasingly applied to enhance the functionality of digital photogrammetry for visualization enhancement to improve image interpretation.
- Computer Graphics are used mainly for graphic presentation. During the last decade, considerable advances have been made in the hardware development known as the 'graphic accelerator'. Its use for drawing operations, 2D and 3D transformations, rendering engine, graphic user interfaces (GUI), and so forth to support CG, has become affordable. A number of programming standards, such as GKS, PHIGS, OPENGL, have been made available which makes the implementation of realistic visualization easier.
- Computer Aided Design is typically applied in engineering. CAD provides many 3D editing capabilities that can be utilized by 3D GIS, for example, during 3D digitizing or editing in a later stage. ArcCAD (ESRI), which is the combination of Arc/Info and AutoCAD (Autodesk, inc.) is an example of exploiting 3D capability in CAD for GIS. For 3D GiS, CAD should be strengthened with better 3D topology. Adjustment of user-interface towards requirements in geoinformation, for example, terminology, additional functions and their operational sequence, would make it more suitable for 3D GIS. Examples of CAD that adapt to 3D GIS are MicroStation and MGE (Intergraph).
- Virtual Reality (VR) is a highly interactive and realistic 3D graphic display of information that allows the system to interact with the human user in a more natural way, thereby offering fast, intuitive understanding of information. This technology exploits highly sophisticated CG in both software and hardware, as a result of the fact that a large number of data elements have to be displayed in a very
short period of time (30 frames per second) to achieve a high degree of continuation. VR techniques allow users to modify the virtual environment navigate from place to place to match their analyses and receive more natural feedbacks and responses from the virtual objects residing in the database.
- Database Management Systems have been continuously developed over a long period. Progress has been made in adding the capability of handling spatial data by both relational and object-oriented DBMSs in, for example, ORACLE, PostCres, ILLUSTRA, 02. The ease of handling spatial objects in GIS seems very promising.
- Expert systems and artificial intelligence add the capability of storing human knowledge about how to deal with complex problems based on known facts and rules in, for example, data processing and spatial analysis. Such a capability would be useful where no human expert is available.
- Computational geometry is used to compute the spatial relationships between the objects using metric computation, for example, the intersection between two lines, point-in-polygon tests, the angle between two vectors, or the circumference of a triangle. Algorithms for computational geometry are now mostly available for 2D. Further development for full 3D computational geometry would ease the topological structuring of 3D data.
In summary, we can relate each technology with the functional components of 3D GIS as shown in the table below.
Acquiring Structuring Storing & Managing Processing Presenting
Map digitizing * *
Terrestrial Survey *
GPS *
Borehole & seismic •
Photogrammetry * * * *
RS *
DTM * *
DIP * *
CG *
CAD *
VR * *
DBMS * * *
Expert System * *
Computational geometry * *
The construction of 3D GIS brings together the above technologies into its functional components. The different approaches found at the time of writing are outlined in the next section.
4. Evolution Stages of System Architecture
The composition of a system for integrated geoinformation always depends on the state of the art, policy and economic constraints. Technology develops, and we
can consider various system architectures as stages of evolution in the handling of geoinformation. We use the following fourteen criteria to analyse the different evolutionary stages.
1. Compactness of the system
2. Common operating system (OS) or hardware platform
3. Functional access
4. Data access
5. Relationships between components of the spatial model
6. Commonness of user-interface
7. Investment cost
8. Maintenance
9. Data redundancy
10. Handling of uncertainty
11. Productivity of geoinformation
12. Potential towards automation
13. Supporting personnel
14. Size of user organization
1.1.Evolution Stage 1: Independent Subsystems
A GIS can be composed from a set of subsystems, as shown in Figure 1. Although this would seem to be an easy (but expensive) approach to constructing a GIS, it can only be regarded as a low level of integration with a low degree of unification. Each subsystem offers only a subset of all the functions of a GIS to carry out some specific tasks along the geoinformation production line. With respect to the above defined criteria, this kind of system has the following characteristics.
1. The system is composed of several subsystems, either in the form of hardware or software. Therefore, it is not compact
2. Different subsystems may need different hardware and OSs, for example, VMS, Unix, MSDOS, MacOS.
3. The system cannot provide a central control panel. The functions of a subsystem can only be reached from the respective local control panel.
4. The data cannot be accessed from a single entry point Data transfer from one subsystem to another may need to be done manually, for example, by using floppy disks, if the subsystems used for the consecutive operations are not connected on line. Data conversion is likely to be required because typically each subsystem will use its own data structure.
5. Components of information are usually stored separately in the local database of each subsystem. For example, data representing man made objects may be stored in the CAD subsystem, data of terrain relief in the DTM subsystem, and data of other terrain objects in the 2D geoinformation subsystem. This segregation means that metric computation must be used to integrate data from different subsystems before topological relationships can be created.
6. The system does not provide a common user-interface. The user-interface is locally provided and depends on each subsystem. Elaborate user training is needed and operation is liable to mistakes.
Figure 1: 3D GIS by Independent sub-systems
7. Investment costs are high because each subsystem has to be purchased separately. Several subsystems are required to achieve the required functionality.
8. Maintenance is difficult and expensive. Different vendors may be responsible for different subsystems.
9. Data redundancy will be high, because of the separate and independent storage.
10. Dealing with uncertainty is necessary in operations that involve data sets from different subsystems.
11. Users have to cope with many problems, so the productivity is not likely to be very high.
12. The production line is difficult to automate because of the limitations mentioned.
13. This approach requires various supporting personnel, for example, an OS specialist an application specialist application programmer to ensure operation.
14. The size of the user organization is quite large in terms of number of personnel and space required for placing the subsystems.
Figure 1 is a graphical illustration of this approach. Data conversion plays a central role in integrating the components of the spatial model stored separately and independently as databases in various subsystems.
1.2.Evolution Stage 2: Functional Integration
A system based on this architecture combines all the necessary functions into one software package. Figure 2 illustrates this approach. Below are listed the characteristics of the system. It:
1. Is compact, because all subsystems are shrunk into functions or software modules implemented within the system
2. Is based on one OS and hardware platform
3. Provides a central control panel
4. Accesses data from a single entry point; data transfer between software modules can take place as a background process
5. Has separate data structure to store data in each module; for example, coverage data and TIN data in Arc/Info are stored in separate data sets with different data structures. Topological relationships between data elements across different data sets do not exist.
6. Provides a common user-interface
7. Is less expensive than the independent subsystems and the client/server, because it is based on only one software package
8. Is easy to maintain, having fewer pieces of hardware and software and only one vendor to deal with
9. Still has data redundancy from different data sets
10. Has problems in handling uncertainty similar to evolution stage 1
11. Has better productivity than the client/server approach, because all processes are locally performed under one system shell, requiring less time for data transfer and message translation
12. Has many operations which can be automated, making it more feasible to automate the wholeproduction line. When familiar with the system, the user can optimize and streamline the operation, for example, by using script or macro language, usually provided by this kind of system, to combine basic functions, so reducing many inter-processes requiring manual operation
13. Requires fewer support personnel than evolution stage 1
14. Requires a smaller sized user organization in terms of number of personnel and space for accommodating the system. The vendors' organization, however, becomes larger because the design and implementation of the system require personnel from many disciplines.
Although this approach represents most of the present attempts of development of GISs, it turns out that no single system can offer all required functions yet An example is that most of the GISs still use 2D spatial models as bases and cannot offer a 3D modelling capability. Users still have to adopt the architecture of evolution stage 1 to achieve the required functionality.
1.3.Evolution Stage 3: Client/Server Architecture
This approach is based on the communication between a 'client' and a 'server'. The client is a module provided to interface with the end-user. The server provides operations to process requests from, and gives feedback to, the client The server and the user can only communicate via the client module. Typically, the client/server approach:
1. Does not attempt to improve the compactness of the system; it is still composed of several independent subsystems, as in stage 1.
2. Is able to carry out tasks on different hardware platforms and OSs by using a standard communication protocol. The role of the OSs on different platforms are
suppressed, so the user only needs to deal with the OS and the hardware platform of the client module.
3. Provides the client with a central control panel. Necessary functions can be reached from the client module. Each client function turns a user action into an appropriate request message high is sent to the server. On receiving the message, the server evaluates the request and triggers a process, if the request is valid. The client can attach to the request message data to be processed by the server. The server processes the data and sends back the result to the client The user may not have the freedom to explore the functionality of each server unless better access to the functions of the server is provided through a mechanism called 'object link and embedding' (OLE). This mechanism maintains the link between each data set and its specific server application. More than one application server can be attached to a data set, and a choice of servers may be provided. On selecting the data set and server, OLE activates and transfers all control and the user-interface to the server, which allows the user to access all functions provided by the server. The user returns to the client by quitting the server. The OLE approach works well on a single OS platform. The client/server approach does not require the user to move around and enter several OS shells to reach functions that are available on different subsystems.
4. Accesses data through the client whose database is a container embedding and encompassing different data sets which may have different data structures native to specific servers. Each data set embedded in the client database may only be recognizable by a specific server. The data is transferred from one server to another server on-line. The client application may have to provide a data conversion function locally if the destination server cannot recognize a nonnative data structure.
5. Stores components of information separately in different embedded data sets. client database can be regarded as a collection of different data sets. There are no topological relationships across different data sets.
6. Has a common user interface with an intuitive graphical user interface (GUI) provided by the client; however, this is limited by the extent to which the client knows the functions of the different servers.
7. Has investment costs as high as the independent subsystems approach, with the additional cost of the client application and the network connection to all server applications.
8. Must have the client application upgraded in accordance with the upgrading of one of the servers or communication protocol.
9. Fails to minimize data redundancy caused by the independency of the embedded data sets.
10. Has to deal with uncertainty every time relationships between data elements across different data sets have to be created.
11. Requires users to deal with fewer problems in functional access and data access and transfer, thereby improving productivity.
12. Facilitates automation of the production line through eliminating some manual processes.
13. Requires fewer supporting personnel, because users need not deal with many low level operations.
14. Has smaller sized organization than the independent subsystems approach with respect to personnel, but not with respect to the space accommodating the subsystems.
This approach was developed after evolution stage 2 in response to demands from the user community. It can be regarded as an intermediate solution because complete functionality is not yet available on a single system. The client/server approach can be a good solution for providing access to all the required functions on the different independent subsystems. The client may be provided by a third party experienced in assisting in interfacing users with systems from various vendors (for example, training, design of process flow), or systems provided through cooperation between the vendors (see Intergraph 1995).
1.4.Evolution Stage 4: Structural Integration
Creating a spatial model that better represents reality requires more closely related geoinformation components. Relationships between data elements need to be well defined by means of topology, which is likely to be difficult without an appropriate unified data structure (UNS). The proposed architecture is based on UNS as described in Pilouk and Tempfli (1994), Pilouk et al (1994).
4.4.1 General Consideration
This approach aims at a better representation of real world objects and relationships between them while maintaining the good aspects of evolution stages 1 to 3. The major considerations for the structural integration are:
- All components of the 3D spatial model must be stored in one database, so that topology can be applied to represent the spatial relationships of the real world objects. The use of topology avoids metric computation and speeds up many operations.
- Both direct and indirect representation must be possible within the system. Direct and indirect representations need different processes to present the information to the user. Allowing both representations in one database requires the system to use appropriate data structure.
- Data redundancy must be minimized. Redundancy is usually introduced by a lack of awareness of existing data. Storing redundant data provides no additional information; it consumes storage space and may lead to conflicts whose resolution requires additional operations.
- The frequency of handling of uncertainty must be minimized and be taken away from the end-user as much as possible by converting uncertainty into a data quality attribute beneficial to the end-user. The database creator has better access to the original sources and is in a better position to resolve the uncertainty.
4.4.2. A Proposed System Architecture
The proposed architecture for a GIS is illustrated by Figure 3. There are various layers of the system encompassing the integrated database based on UNS. The integrated database contains a spatial model accommodating both direct and indirect representations of real world objects. The database management shell embraces this database, on which various indices or database views exist. Spatial access to some specific data elements can be speeded up by a database index. This can be regarded as a specific view of the earth that is closer to the application domain or operation requirement The existence and status of each index and view depend on the integrated database. All indices and views are updated according to the changes in the integrated database. Any changes must be directed first to the integrated database and subsequently to the indices and views. The database management shell provides functions and rules to access and update the integrated database or views. The process shell is the outer level next to the database management shell. It contains various functions to process the integrated database or views by using database management functions provided by the database management shell.
Figure 3: Overview of the structural integration architecture.
Figure 4: Different data structures can be used at the view level in the structural integration.
The next outer shell is the generalization shell which simplifies the input from the real world to the GIS and provides the means for presenting or delivering the information stored in the GIS to the users or to the client databases (see also Richardson 1993, Peng and Molenaar 1995, Peng et al 1996).
Figure 4 shows a more elaborate architecture of a 3D GIS. The focus is on the views of the integrated database. The suggestion here is to use already existing data models, for example, SWM, MWM, TIN-DTM, 3D FDS, as the underlined structures of the views. The reason is that those data models in fact represent different views of the reality. The integrated database can be regarded as the integration of views, so it may contain over extensive data for an individual application, resulting in bad access and response time.
5. Comparison of Different System Architectures
The evolution stages of the system architectures can be compared as in the table below. The qualitative assessments presented result from the analysis in the previous section.
Criteria Independent subsystems Functional Integration Client/Server Structural Integration (Expected)
System compactness No Yes No Yes
OS & hardware platform Multiple Single Multiple Single
Functional access Poor Good Good Good
Data access Poor Good Fair Good
Data relationships Poor Poor Poor Good
User-interface Poor Good Fair Good
Investment cost High Low High Low
Maintenance Complicate Simple Complicate Simple
Data redundancy High High High Low
Handling of uncertainty Frequent Frequent Frequent Good
Productivity Poor Good Fair Good
Automation Poor Possible Poor Potential
Supporting personnel Many Fewer Fewer Fewer
User organisation Urge Small Small Small
When comparing the four evolution stages, we can say the performance of structural integration is better or at least equal to any other form of system architecture with respect to all fourteen criteria.
6. Attempts at Structural Integration
Efforts to establish 3D CIS start from the 'Integration of DTM and GIS' reported in Males (1978), Ebner et al (1990), Mark et al (1989), Fritsch (1990), Pfannenstein and Reinhardt (1993), Pilouk and Tempfli (1993). The aim is to tackle the aspect of dimensionality of geoinformation, focusing especially on the integration of 2D-GIS and DTM. Early researches divide into two groups.
Fritsch (1990) investigated different approaches at the level of the data model, suggesting an object-oriented approach. The discussion of the conceptual aspects of data models and data structure is reviewed by Pfannenstein and Reinhardt (1993). Pilouk and Tempfli (1993, 1994) have reported the formalization of the data model and data structure for the integration of DTM and GIS, proposing an integrated data model and a unified relational data structure. All the basic topological relationships between data elements and between simplices and complexes are clearly defined and used as a basis for the data model for 3D CIS developed here (see chapter 4 for details).
1.1.‘2.5D’ Approach
The 2.5D approach can be regarded as a first integration step seeking to combine the separated data sets of 2D geoinformation and DTM. The separation of these two components of spatial objects has been inherited from 2D map production, where DTM was used to automate the production of contour lines for relief representation in
a 2D map. DTM has merely been a matrix storage of height information which helps speed up the contouring process. DTM has become more important because it facilitates the derivation of a wide range of information products serving various applications, for example, contours, slope and aspect maps, relief shading, perspective view, cross-section and profile, visibility, volume and surface area. DTM can be used as a reference (mapping) surface for other terrain features, for example, for draping terrain features on a (DTM) surface and display in a perspective view, which gives a more naturalistic visualization requiring less map-reading skill.
The most traditional attempt in 2.5D integration is the 'height attributing' approach (Fritsch and Pfannenstein 1992a) that considers height as thematic information. In the case of point objects, this approach seems to be adequate. For a line or surface object the representation of height can only be done if there is an assumption that the object is on a horizontal flat plane, or every part of the object has the same elevation. This approach can only solve problems for a limited number of applications (Ebner and Eder 1992, Fritsch and Pfannenstein 1992b). Figure 5 demonstrates the approach.
This approach is dictated by the fact that most commercial GISs do not allow the user to have direct access to the geometric data elements kept in a fixed structure. Although the system may provide ways to retrieve this data into a database table and allow the user to utilize it, or add additional attribute columns, possibly one containing height information (z coordinate), the table is only regarded as external and is not part of the geometric components.
Besides the height attributing approach, Fritsch (1990) has suggested an object oriented alternative for handling a hybrid database that embraces three separate data sets: two for geometry (terrain data and situation data); one for non graphical data (attributes).
Figure 6 Hybrid database (after Fritsch 1990, Kufoniyi and Bouloucos (1994) suggested a model using the multiple theme approach by taking DTM as a thematic layer and the database maintaining the links to DTM via triangle edges and vertices that are respectively isomorphic to the primitive arcs and nodes of the multi-theme 2D FDS.
1.2.3D Approach
For applications that need to model solid objects, the 3D FDS, proposed by Molenaar (1990), offers a data model with 3D topology. This data model permits the representation of spatial objects in the different dimensions OD, 1D, 2D and 3D, that is the point line, surface and body features respectively. It is therefore possible to model terrain as a surface feature. Figure 7 is the graphical illustration of this data model.
Closed structure Open Structure
Node Point
Nid X Y
1 90 50
2 50 40
3 25 30
4 10 20
Pid Nid Name Elavation
1 2 Bus station 311
2 3 Well 250
3 1 Tower 285
4 4 284
Figure 5: Height attributing approach maintain elevation in a thematic table.
Database management system
Object oriented management
Figure 6: Hybrid database (after Frisch 1990)
Figure 7: Formal data structure for 3D vector map (after Molenaar 1990).
The conceptual design of the 3D FDS is the same as for SWM or 2D FDS. The design is based on the decomposition of a feature into identifier, geometry, and
theme. Components for representing a spatial object are grouped into three levels, namely the geometric, feature and class levels, in the same way as in 2D FDS. Faces are 2D geometric primitives in addition to nodes and arcs, respectively of 0 and 1 dimension. Edges are additional geometric primitives providing the link between arcs and faces and so permitting the unique reference to /eftand r/ghtbodies that are 3D features. Figure 10 illustrates this concept.
The 3D FDS is suitable for direct representation and query-based spatial analysis; it does not, however, provide the 3D primitive data type and is therefore less suited to solving problems for applications that involve complex computation, for example, using the finite element method, where a 3D primitive is crucial because it is needed as a computation unit The 3D FDS is based on single-valued concepts; however, it can readily be extended to accommodate spatial coincidence, or multivalued aspects.
Figure 8: Relational database structure based on 3D FDS (adapted from Rikkers et al
1993).
Discussion
Many technological developments have to be incorporated into the functional aspects of 3D GIS. Many different approaches to the construction of a 3D CIS can be used, ranging from loosely to well-constructed systems. These approaches are differentiated into four evolution stages of system architecture; independent subsystems, functional integration, client/server architecture, and structural integration. The proposed structural integration involves considerable effort in designing and constructing the system. This effort, however, is transferred to the vendor rather than left to the user, as in evolution stage 1. The expected superiority lies not only in the handling of uncertainty, but also in the benefits accruing from explicit relationships and minimized redundancy, with all the consequences for the user.
Current systems are in evolution stage 1, with increasing attempts concerning stage 2. There is a trend towards evolution stage 3 as a result of commercial driving forces that prefer to keep proprietary developments from the public while offering
improved user-friendliness, partially fulfilling user demands. This trend is evidenced by the adoption of specifications for OLE extensions for computer-aided-design (CAD), computer-aided-manufacturing (CAM), computer-aided-engineering (CAE) and CIS by a group of vendors related to spatial information industries, namely the Design & Modelling Application Council (DMAQ, which includes ANSYS Inc., Autodesk, Bentley System Inc., Cadence Design Systems Inc., Crisis in Perspective Inc., Intergraph, Microsoft, Ray Dream, SDRC, Shapeware Corp. and Spatial Technology, since the beginning of 1995 (Intergraph 1995). The evolution stage 4 promises to be an ideal system, but it can only be achieved if there is a consensus on the formal design of the spatial data model. Such a consensus could also result in the adoption of the client/server architecture on top of the structural integration.
The state of progress towards structural integration through current attempts shows that further investigation and extension of the existing data models are looked for. The essence of the matter is to be able to accommodate both direct and indirect 3D representations of real world objects within an integrated 3D spatial model, similar to the normal situation in reality, which would allow the representation and analysis of spatial relationships between the two types of objects. A 3D spatial data model with this capability should be further developed to serve as a basis for a 3D GIS adopting structural integration architecture.
Bibliography
1. Bric,V., and Pulouk.M.,1994, Computation of topologic space. ITC, Enscgede, The Netherlands
2. Bric,V., and Pulouk.M., and Tempfli, K., 1994 Towards 3D-GIS: experimenting with a vector data structure, International Archives of Photogrammetry and Remote sensing., Vol. XXX., Part 4, Athens, Georgia, USA, pp. 634-640
3. Bric,V., 1993, 3D vector data structures and modeling of simple objects in GIS, Master thesis, ITC, Enschede, The Netherlands
rd
4. Ebner,H. and Eder, K., 1992, State-of the art in digital terrain modeling, proceeding 3 European conference on geographical information systems (EGIS’92)Vol1, Munich, Germany, pp.681-690
5. Fritsch,D., 1990, Towards three-dimensional data structures in geographic information systems, Proceeding first European conference on Geographical information systems (EGIS’90),vol 1, Amsterdam, The Netherlands, pp.335-345
6. Fritsch,D., and Pfannenstein, A., 1992a, Integration DTM data structures into GIS data models, International Archives of Photogrammetry and Remote sensing, Vol. XXIX, PartB3, commission III, Washington, D.C., USA, pp.497-503
7. Fritsch,D., and Pfannenstein, A., 1992b, Conceptual models for efficient DTM integration into GIS, proceeding 3rd European conference on geographical information systems (EGIS’92)Vol. 1, Munich, Germany, pp.701-710
8. Goodchild, M. F., 1997: What Next? Reflections from the Middle of the Growth Curve. In: FORES-man, 1997, 369-381.
9. Gruen, A., Streilein, A., Stallman, D., and Dan, H., 1993, Automation of house extraction from aerial and terrestrial images, Conference ASIA, Wuhan, China
10. Intergraph, 1995, New OLE extensions for CAD/CAM/CAE and GIS adopted, press releases Intergraph corp., Huntscille
11. Males, R.M.,1979, ADAPT-a spatial data structure for use with planning and design models, In:Dutton, G., (Ed.), First international symposium on advance study on topological data structures for geographic information systems, Vol. 3,19pp
12. Mark, D.M., Lauzon, J.P., and Cebrian, J.A., 1989, A review of quadtree-based strategies for interfacing coverage data with digital elevation models in grid form, International journal of GIS, Vol.3, No. 1, Taylor&Francis, London, pp3-14
13. Peucker, T. K., Fowler, R. J., Little, J. J. & Mark, D. M., 1976: Digital Representation of Three-Dimensional Surfaces by Triangulated Irregular Networks (TIN) (Revised). Technical Report Nr. 10, Simon Fraser University, Burnaby, British Columbia, Canada & Office of Naval Research, Geography Programs, Arlington, Virginia, USA.
14. Pfanenstein, A., and Reinhardt, W., 1993, Data analysis in GISs in combination with integrated digital terrain models, Proceedings 4th European conference on geographical information systems (EGIS’93), pp. 1341-1349
15. Pilouk, M., Tempfli, K., Molenaar, M., 1994, A tetrahedron-based 3D vector data model for geoinformations, In: Molenaar, M., and De Hoop, S., (Eds.), Advanced geographic data modeling: spatial data modeling and query languages for 2D and 3D applications, Netherlands Geodetic Commision, No. 40, Delft, The Netherlands, pp. 129-140
16. Pilouk, M., Tempfli, K., 1994, An object oriented approach to the unified data structure, GIS’94, Vol. 1, Vancouver, Canada, pp/ 163-169
17. Pilouk, M., Tempfli, K., 1993, An integrated DTM-GIS data structure: a relational approach, Proceedings 11th international symposium on computer assisted cartography (AutocCarto 11), Minneapolis, Minnesota, USA, pp. 279-287
18. Pilouk, M., Tempfli, K., An digital image processing approach to creating DTMs from digitized contours, international archives of photogrammetry and remote sensing, Vol. XXIX, Part B4, Commission IV, Washington, D.C., USA, pp 956-961
© Ch. Bolorchuluun, D. Tuvshinbayar, 2006
