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CAD data play a key role in 3D city models. Most of the buildings with required level of details as mentioned above can be designed in a CAD environment. Other infrastructure such as railway network, road network and other utilities can be modeled in CAD environment. The outcome in the form of wire frames or surface models will add flavor to the 3d city models. The specifications of the buildings in the form of attributes can also be incorporated in CityGML data model.


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So there cannot be a uniform data base schema for the same. The data base schema will be depending on the requirement and application of the models. The component GIS data sets can be raster rata sets as well as vector data sets. Reliable data base management is a prerequisite for complex analysis and result derivation from 3D city models. So a relational data base is very much required for running and utilizing any 3D city models. In this relational 3D city models, hierarchically structured, multi scale data can be stored, retrieved and analyzed.

For Updation of the 3D city model data based is also possible only through a relational data base. Also the relational data base should able to hold multiple formats of the data and its rendering. Open geospatial consortium standards and norms can be followed in 3D city models creations as well. Such data bases are referred as city GML. One of the major challenges in automatic acquisition of 3D city models is its visualization and semantic rendering.

So a common frame work schema based on the requirement of the model need to be established beforehand through a relational data base.

Introduction

Visualization and rendering are based on the core functionality required through interactive application suites. Typically 3D city model rendering should be fast, scalable, and cost effective. In case of 3D geometry and 3D texture display a specialized algorithms need to be established. In general rendering can be service based or maps based. Virtual 3D city models are used today for a wide range of applications like urban planning, environmental and training simulations, navigation, disaster management, energy assessment, and many more.

In their paper Biljecki et al. It was shown that most of the applications do not just need data about the 3D geometry and graphical characteristics, but also require semantic information like object types, thematic attributes as well as spatial and semantic interrelationships. Semantic 3D city models, hence, are virtual models of the physical urban environment, i. In contrast to 3D models used in Computer Graphics they are structured e.

The objects of a semantic 3D city model represent the respective real-world objects with their thematic, geometrical, topological, and appearance properties cf. Many cities worldwide and even entire countries today have created and are maintaining CityGML-based 3D city models. CityGML defines an object-oriented data model of the most relevant urban objects like buildings, vegetation, roads, water bodies, terrain etc.

The structural and spatial complexity of CityGML-based 3D city models can range from very simple to complex and nested entities. Each object can be spatially represented by multiple geometries of different types in 3D space e. While on the one hand applications and users benefit from the rich data model in many ways cf. Above, 3D city models can be very large and single CityGML files for a bigger city or region can have from tens over hundreds gigabytes in size.

However, applications and users need efficient tools to query, visualize, and update the 3D city model. In fact, the development of 3DCityDB was started by the last author of this paper back in at the Institute for Cartography and Geoinformation at the University of Bonn. Between and the development was continued by the Institute for Geodesy and Geoinformation Science at Technical University of Berlin. Today, third-party developers and software vendors from different domains are not only using the 3DCityDB, but they also contribute to the improvement of the functionalities and quality of the software tools and they create their own extensions.

Spatial Data Modelling For 3d Gis

While some basic concepts of the 3DCityDB have already been explained in an earlier paper [ 39 ], much has happened since then. Therefore, this paper gives more details on the one hand, and especially puts focus on the changes, new features, tools, and application examples that have been developed over the last ten years on the other hand. The rest of this paper is structured as follows: Section 2 gives a brief introduction to the international standard CityGML. In addition, the essential aspects and approaches for realizing the efficient management using spatially-enhanced relational database management system SRDBMS are discussed in order to provide the foundation for designing a compact CityGML-compliant relational database schema for 3DCityDB.

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Section 3 presents the 3DCityDB software tools with details about the conceptual design and technical implementations. Section 4 at first highlights some application areas, use cases, research projects, and users currently employing 3DCityDB. The last section draws the conclusions about the presented work and outlines the relevant aspects of our future research and development tasks. CityGML defines a conceptual schema for the most relevant entities of the urban space like buildings, roads, railways, tunnels, bridges, city furniture, water bodies, vegetation, and the terrain.

The conceptual schema specifies how and into which parts and pieces physical objects of the real world should be decomposed and classified. All objects can be represented with respect to their semantics, 3D geometry, 3D topology, and appearances in five predefined levels of detail LOD 0—4. The first official version of CityGML was released in the year and the current version 2.

In CityGML, all classes and data types are grouped into a number of thematic modules. The modules and their relationships are shown in the UML package diagram in Fig.

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Overview of the CityGML modules cf. Supported geometric primitives include Point , Curve , Surface , and Solid, which allow to represent spatial properties of city objects in different dimensions ranging from zero to three. Volumetric geometries are modeled using the well-known boundary representation B-Rep, cf. The orientation of surfaces can be specified explicitly when using the geometry type OrientableSurface.

For each geometry type, more complex geometries with composite or aggregated hierarchies cf. The difference between aggregate and composite geometries lies in the topological relationships between the respective geometry components. For aggregate geometries such as MultiCurve , MultiSurface , and MultiSolid , the spatial relationships between components are not restricted and primitives can, hence, overlap, touch, or be disjoint.

In contrast, a composite geometry like CompositeCurve , CompositeSurface , or CompositeSolid is a special case of the aggregate geometry which must be isomorphic to a single respective geometric primitive. This implies that the underlying elements must be topologically connected along their boundaries.

In addition, the GML geometry type GeometryComplex can be used to represent a complex consisting of geometric primitives of different types e.

Data model (GIS) - Wikipedia

Point and Curve. The members of a geometric complex must not overlap and can touch at their boundaries only. CityGML allows to assign appearances to individual surfaces like Polygons , composite, and aggregate surfaces. Appearances can be specified by colours or textures, and each surface can be assigned any number of appearances. Textures are represented by raster images. Each geometry object can have a unique identifier and can form a shared part of different aggregate or composite geometries.


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  4. For example, one polygon may be member of the outer shells of two solids in order to explicitly express that the two solids are touching along one side. The shared polygon is then not represented redundantly, but is referenced from the outer shell of the second solid by an XLink to the shared polygon. CityGML is very flexible regarding the expression of spatial properties of semantic objects.

    For example, the geometry of a Building object may be given in any of the LODs 0, 1, 2, 3, and 4 also simultaneously. Besides geometrical decompositions, CityGML can also decompose objects semantically into parts. For example, a building can consist of building parts, which again can consist of roof, wall, and ground surfaces etc. When objects are decomposed in the same way regarding their semantic as well as their spatial structure, they are considered to be spatio-semantically coherent. This is illustrated for a building model in Fig. Most of the LOD2 CityGML building models available today are semantically describing the wall, roof, and ground surfaces and additionally provide a solid geometry for the geometric representation of the building hull and its 3D shape.

    The semantic objects are usually used to query and analyse the building components and their thematic attributes, whereas the solid geometry represents the whole body and is useful for geometric calculations such as the building volume and surface areas. Both aspects of describing the building are complementary and provide a very flexible modelling structure ranging from simple geometric models to semantically rich models.

    Coherence of semantics and geometry in CityGML taken from [ 38 ]. This means, each semantic object like a building or a tunnel can be decomposed into parts and subparts. Each semantic object can have a number of geometric properties of different geometry types and LODs. Some geometry elements can be shared from different aggregate geometries. Also semantic objects can be part of multiple semantic aggregate objects. Each surface can be assigned an arbitrary number of appearances.

    The geodatabase must also be able to handle appearance data like individual surface textures, which typically are given in binary image file formats e.

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    Data model (GIS)

    All semantic objects have predefined thematic attributes and, in addition, can have an arbitrary number of generic attributes. Finally, since 3D city models cover large areas up to entire countries, the geodatabase must be able to manage the large data volumes and provide efficient access to the stored data for thematic and spatial queries.

    Exploring GIS: Spatial data representation

    In the following, a selection of these software packages are listed, along with their major characteristics with respect to CityGML support. The named systems all extract CityGML data from CityGML files and insert the data into tables of spatially-extended relational database management systems. In fact, data ingest and retrieval is a lot faster than with RDBMS due to the smaller serialization effort [ 20 ]. While documents stores have their weakness with more complex queries including joins and spatial operations, they can be a great choice for web application backends sitting in between the RDBMS and a client.

    GeoRocket is available in an Open Source and a commercial version. The latter software has been made available as Open Source software on Github. Footnote 9. There are strong reasons to employ spatially-extended relational database management systems SRDBMS to store and manage complex 3D city models. First, SRDBMS support all required geometry types and provide means for proper spatial indexing as well as for geometric and topological analyses. As described above there exists a variety of non-relational databases like object-oriented databases, document-oriented databases, and graph databases, which are increasingly investigated and employed in many application fields cf.

    However, they are currently still more or less limited in their capabilities and performance regarding spatial operations and coordinate transformations, which are of great importance for the enterprise use in GIS applications cf. The conceptual solution for handling object-oriented data models like CityGML in SRDBMS can be abstracted to solving the problem of mapping the object-oriented data model onto a relational data model. For example, a class shall be mapped onto one table where each row should represent an instanced object of the respective class.

    Additional columns can also be added to the mapped table for storing the spatial and non-spatial attribute values of the respective class objects.