INTEGRATING URBAN KNOWLEDGE INTO 3D CITY MODELS
Gilles Falquet a, Claudine Métral b
a Centre universitaire d'informatique (CUI),University of Geneva, Rue Général-Dufour 24, CH 1204 Geneva, Switzerland - [email protected]
b Institut d’architecture (IAUG), University of Geneva, Route de Drize 7, CH 1227 Carouge, Switzerland - [email protected]
KEY WORDS: 3D City Model, Geo-Information System, GIS, Semantic Model, Urban Ontology, Knowledge Base, User Interface
ABSTRACT:
There is a growing interest in the creation, visualization and use of 3D city models. But most of these models have focused on the geometric aspects. However, taking into account the non-geometrical aspects is essential to obtain genuine tools for urban planning, in particular to support the discussions, debates, etc. concerning the urban projects. The aim of our work is to integrate this knowledge into 3D city models.
As the creation of accurate 3D models is an expensive process we first concentrated on the development of a prototyping tool so as to easily produce and test different models. We use a declarative language for the knowledge representation and integration, as well as for the knowledge visualization. The formal representation of the knowledge is in fact an urban ontology. The visualization of the non-geometric knowledge in the 3D city model is not fixed but depends on the choices made by the user of our tool.
The approach we have chosen is 3D spatial hypertext. So the specifications of an interface consist in specifying how to build a spatial 3D hypertext that represents adequately and efficiently (from the user point of view) the formalized non-geometric knowledge. As the positioning of the hypertext nodes is realized by layout managers, the layout managers have to use the 3D city model as a background so as to properly integrate these representations into the 3D city model.
RÉSUMÉ:
L'intérêt pour la création, la visualisation et l'utilisation de modèles 3D urbains (3D city models) ne cesse de croître. Mais la plupart de ces modèles se concentrent sur les aspects géométriques. Or, la prise en compte des aspects non géométriques est essentielle pour obtenir de véritables outils d'urbanisme, en particulier pour supporter les discussions, débats, etc. concernant les projets urbains. Le but de notre travail est d'intégrer ces connaissances non géométriques dans les modèles 3D.
La création de modèles 3D de qualité étant un processus coûteux, nous avons débuté notre travail par le développement d'un outil de prototypage afin de pouvoir facilement produire et tester différents modèles. Nous utilisons un langage déclaratif, aussi bien pour la représentation et l'intégration des connaissances que pour leur visualisation. La représentation formelle des connaissances s'effectue sous la forme d'ontologies. Quant à la visualisation des connaissances non géométriques dans le modèle 3D, elle n'est pas figée, l'utilisateur de notre outil pouvant créer autant de représentations qu'il le désire.
Nous avons choisi une approche de type hypertexte 3D. Ainsi, spécifier une interface revient à spécifier l'hypertexte 3D permettant de représenter les connaissances non géométriques (qui ont été formalisées dans l'ontologie) de manière adéquate et efficace pour l'utilisateur. Le positionnement des nœuds de l'hypertexte s'effectuant par l'intermédiaire de layout managers, qui sont en fait des programmes, ces layout managers doivent utiliser le modèle 3D comme fond pour y intégrer de manière appropriée ces représentations.
1. INTRODUCTION
The interest for creating and using 3D city models is growing and expanding rapidly. Different projects that model an existing city have been or are developed all around the world. They are intended for a wide range of applications, such as planning and design, infrastructures and facility services, marketing or promotion.
1.1 GIS-based 3D City Models
3D city models can be built from existing GIS, which contain basically 2D information. For example, the State of Geneva has developed a geographical information system, called SITG (Système d’information du territoire genevois). By combining and expanding different SITG information layers, such as the digital terrain model (representing the ground without the
vegetation or the buildings), the building footprints and the building heights, we obtain a 3D block model of Geneva.
Strictly speaking such a model where the third dimension is expanded from 2D data using heights is a 2.5D model, but we refer to it as a 3D city model. By adding a texture mapping from the orthophotos of the area we obtain a more realistic 3D city model. Such a model is useful for the visualization of full urban environments including built and natural structures or for the simulation of new urban projects with their environmental and visual impact. It is also a visual communication tool much more efficient than, for example, an official plan.
3D city models differ by elements such as their degree of reality, i.e. the amount of geometrical details that are rep- resented within them, their data acquiring methods and their functionality, i.e. the degree of utility and analytical features that they allow (Shiode, 2001).
Reconstructing the 3D-geometry of urban areas can be done in a (semi-)automatic way from digital aerial images or laser data (Brenneret al., 2001). This process is usually done using high- level knowledge about the objects of the scene so as to set up models of the objects in the scene (Fischer et al., 1999).
Different models exist, such as parametric models, prismatic models, union of blocks, or polyhedral models.
According to the levels of detail (LoD) of the model:
- buildings can be reconstructed as wrap bodies from ground plan and building height;
- there can be addition of geometrical roof shapes, textures, some street and vegetation objects;
- higher level of realism can be obtained by adding more detailed street and vegetation objects supplied with photo-textures.
In order to allow efficient visualization, different levels of detail can be handled dependent on the vicinity of the objects observed to the observer's location.
1.2
1.3
Ontologies
According to Gruber, an ontology is a specification of a conceptualization (Gruber, 1993). A conceptualization is an abstract, simplified view of the world that we wish to represent for some purpose, i.e. the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them.
In ontology-driven geographic information system (ODGIS), ontologies are used to formalize existing data and knowledge from previous GIS projects, so as to enable their reuse (Fonseca et al., 2002).
A geographical ontology is at the heart of the SPIRIT system (Joneset al., 2003). Here, the ontology is used to support the retrieval of geographical information on the web.
In the domain of urban civil engineering (UCE), the development of an UCE ontology is planned at a european level (Action COST C21). Its main objective is to increase the knowledge and promote the use of ontologies in the domain of UCE projects, in the view of facilitating the communications between information systems, stakeholders and UCE specialists.
3D City Models and Ontologies
A 3D city model cannot represent all the knowledge that is necessary when working on an urban planning project. This knowledge usually takes the form of documents (texts, blueprints, pictures, videos) or database attributes that have text or numeric values that do not correspond to geometric entities (building period, parcel owner, building permit). In this paper we propose an approach to build knowledge bases, grounded on urban ontologies, to integrate this type of knowledge within 3D city models, both at the semantic level and at the presentation level (i.e. within 3D scenes).
Our integration approach consists in two levels:
- a knowledge representation and integration level which is intended to formally describe the non- geometric knowledge stored in documents and data structures;
- a knowledge visualization level, which specifies a 3D representation of this knowledge and associates it with the existing (geometric) 3D model.
In section 2, we introduce the knowledge representation and integration level. In section 3 we present the knowledge visualization level. In section 4 we briefly show the current implementation strategy. Section 5 gives our conclusions and future direction of work.
2. KNOWLEDGE REPRESENTATION AND INTEGRATION
A considerable amount of knowledge required for urban planning tasks is currently stored in documents (texts, pictures, etc.). Thus the first step toward integration of this knowledge is to describe it formally, so as to obtain a knowledge base that can be machine processed. This knowledge base is composed of:
- an conceptual layer that describes all the concepts that appear in the documents and in the GIS database (for instance: parcel owner, building permit, legal plan, legal constraint);
- a factual knowledge layer that is composed of concept instances and their relationships.
The conceptual layer is in fact an urban ontology obtained by extracting the concepts that appear in the documents and the concepts that correspond to the GIS database schema. During the last years several approaches and tools have been developed to do these concept extractions automatically or semi- automatically. For instance (Stojanovic, 2002) and (Astrova, 2004) propose techniques to extract ontologies from relational database schemas, while (Velardi, 2001) use text analysis technique to help in the construction of ontologies.
The factual knowledge layer also acts as a linking layer between the documents and the knowledge base. Each concept instance that appears in this layer is linked to the document or to the database entity that describes or represents it.
The advantage of this representation, compared to the database representation, is that it is open. Each concept instance can be freely linked to others in order to enrich its description, which is not possible in database with a fixed schema.
Figure 1. Knowledge integration at the specification level The knowledge base can be expressed in the RDF/RDFS/OWL family of knowledge description language.
RDF is a very simple semi-structured data model based on (subject, predicate, object) triples that form a semantic graph.
The subject and object are resources identified by their URI (Universal Resource Identifier). The predicate is a name that indicates the relation holding between the subject and the object.
RDFS adds a schema layer on top of RDF. RDFS mainly adds the notions of class (of resource), class instance, subclass, and property. RDFS schemas are similar to class specifications in object-oriented systems and languages. The RDFS layer is expressed in terms of RDF triples. For instance, a triple (R, rdf:type, C) indicates that the resource R is an instance of the class C.
OWL is a concept definition language that belongs to the description logic family of languages. OWL can be viewed as an extension of RDFS in that it has more powerful constructs to define classes (union, intersection, and negation operators, property restrictions, etc.). There is a standard (normalized) way to represent OWL definitions with RDF triples. The OWL language is less expressive than the first order logic. Hence, there are several proposals to complemented OWL with a rule definition language (e.g. SWRL) that contains first order formulae.
Several query languages have been proposed for RDF, the most recent one being SPARQL (Prud’hommeaux and Seaborne, 2005). They are mostly based on triple patterns. A triple pattern is a triple in which zero ore more components are replaced by variables. Such a pattern matches all the RDF triples that have the same constant values as the pattern. For instance, the pattern (?X owner Bob) matches all the triples with predicate owner and object Bob. And thus a query of the form SELECT ?X FROM (?X owner Bob) returns all the resources X such that the owner of X is Bob.
3. KNOWLEDGE VISUALIZATION
The main visualization problem we face is to integrate the visualization of geometric and non-geometric abstract entities.
These last entities, for instance a goal or a policy in a comprehensive plan, a regulation, the ownership of a parcel, etc., are generally expressed in a text document but don't have a canonical or standardized geometric representation. A first solution could consist of a split presentation showing textual information in a window and 3D objects in another one, with hypertext links connecting (parts of) the text with related objects in the 3D view. For instance, when reading the text of a
"building constraint" the user could click on a building or parcel number to see this object in the 3D view. However, this solution has some drawbacks, in particular it forces the user to frequently switch between a 2D and a 3D view, thus loosing his or her focus in the working context.
What we seek is a way to represent the non-geometric knowledge within a 3D city model. In this case, the problem is to find adequate and efficient (from the user point of view) 3D representation of this kind of knowledge.
3.1
3.2
4.1 Existing Systems
During the last decade, human-computer interaction researchers have invented various visualization techniques to efficiently present and interact with different data types (linear structures, two-dimensional maps, three-dimensional worlds, temporal structures, multi-dimensional data, trees, and networks). Here we are particularly interested in techniques for visualizing the network structure of a formal ontology. The techniques used until now remain simple and traditional: hypertext interfaces, tabular views, graphs, etc. As mentioned by Schneiderman (Schneiderman, 1998), there is still much to do in this area.
Apart from basic graph drawing, one can mention general techniques like fisheye views (Schaffer et al., 1996), or lenses to visualize large networks on a single screen. When a tree structure exists (or can be extracted from the network), techniques like hyperbolic trees (Lamping et al., 1995) or 3- dimensional embedded objects can be used. These techniques have been evaluated with users to assess their effectiveness, see for instance (Hornbk et al., 2001) and (Cockburn and McKenzie, 2001). The SemNet system (Fairchild et al., 1988) is one of the rare attempts at proposing a 3-dimensional view of a
knowledge base. It represents concepts and their semantic links as a 3D graph. Another system called MUT (Travers, 1989) proposes a virtual museum metaphor with nested boxes for representing nodes and links from network-structured knowledge bases. Some systems let the user rearrange the visual elements according to their own cognitive model, for instance Workscape (Ballay, 1994) or Web Forager (Card et al., 1996).
Spatial Hypertext Approach
Few work has been carried out for representing urban concepts in 3D. Furthermore, there is not one ideal representation for such concepts. Thus we do not propose one (fixed) geometric representation for each concept that belongs to the urban ontology but we propose a system to specify the representation that best fits the designer's needs. This representation can be specified with a declarative specification language that we re- cently developed (El Atifi and Falquet, 2004).
The idea is to consider the 3D visualization as kind of spatial hypertext. A spatial hypertext is a hypertext whose nodes are spatially positioned so as to represent some semantic relationship. For instance, the nodes can be grouped by topic or by content similarity. In our case, the 3D city model serves as a
"background" or support for the 3D representation of abstract concepts.
The visual representation is obtained by instantiating node specifications, which results in actual 3D nodes and navigation links. The positioning of the 3D nodes is not directly specified in term of coordinates but by associating a layout manager to each node. The layout manager is in charge of computing the positions of the nodes according to their positioning constraints and to a layout style.
4. IMPLEMENTATION
We have developed an interface generator that takes as input a knowledge base and 3D interface specifications and produces as output 3D objects. The interface specification consists of two levels:
- the specification of an abstract interface (which concerns the definition of interface objects and links as so their semantic contents);
- the specification of a concrete interface (which concerns the definition of colors, shapes, layout, etc.).
In order to properly integrate the knowledge representation into the 3D city model, we are developing new layout managers that take into account the actual 3D city model. So knowledge ele- ments can be positioned relatively to 3D objects of the city model.
The Abstract Interface
At this level the specification of an interface consists in specifying how to build a spatial hypertext that represents the content of the knowledge base. Thus, the abstract interface specification defines a mapping from the knowledge base content to the abstract interface components, which are abstract spatial hypertext nodes and links. An abstract hypertext node has a content that will be interpreted in the concrete interface as geometric or appearance properties, texts, references to other resources, etc. A node can be simple or compound. In this last case, the node includes other nodes. The inclusion link between a compound and a component node can have attributes that will play a role in the positioning of the node. Other links are navigation links and semantic links. Here again, the attribute
values will be interpreted at the concrete level for positioning or presenting the linked nodes.
4.2
4.3
The Concrete Interface
The concrete interface specification defines a mapping from abstract interface components (nodes and links) to concrete components (3D objects and navigation actions). At this level the specification interface adds a geometry, an appearance, and a position to each node. In spatial hypertext there are implicit links, which are represented by the geometric proximity of nodes, and explicit navigation links, which are represented by anchor objects (buttons) that can lead the user to other nodes when activated. Explicit links can also have a graphical representation (for instance solid lines, or tubes, or roads).
A concrete specification is similar to a style sheet for document presentation but it must be more sophisticated about the positioning of subnodes. Thus, the main difficulty in going from the abstract to the concrete interface is to compute the position of each node so as to represent the inclusion (of subnodes into nodes) and the semantic relationships of the abstract model. We have chosen a layout manager approach to determine the position of each element of the scene. Although we call it
“concrete”, this model is still more abstract than models like VRML, X3D, or Java3D because the positioning of the objects is not given by 3D coordinates but left to the layout managers.
The Layout Managers
Associating a layout manager to each node represents the positioning of nodes. A layout manager is essentially an algorithm that takes as input a set of (sub) nodes and computes their location according to their content and to constraints represented by semantic links. A layout manager can have parameter of two kinds:
- parameters that usually receive a value which can be given by a link;
- constraints that will be associated to implicit links.
The association between a node and its layout manager is in fact a binding of the layout manager parameters with values, which can be constants (numeric or text) or semantic relations.
There are two strategies to translate a concrete interface into a 3D scene in one of these implemented models. In the static approach, the concrete interface is given as input to the different layout managers that compute the node positions and create a static scene. In the dynamic approach, the 3D scene is created with “active” components that dynamically recompute the object positions each time an event occurs. For instance, a hyperbolic tree layout manager must recompute the positions each time the user selects a new object to become the centre of the view.
We are still working on the layout managers so as to find the best way to represent the urban knowledge in the 3D city models. For example, is it better to represent a semantic link as a positioning constraint or as a geometrical form, which might be a tube?
5. CONCLUSIONS
In this paper we have presented a global semantic model to integrate non-geometric urban knowledge and data into 3D city models. In addition to this semantic integration a declarative specification language enables the designer, or the expert user, to specify 3D representations for the non-geometric knowledge elements and to include them into the 3D scenes that represent
3D city models. Thus the users are provided with direct view of this type of knowledge within the 3D scene: this avoids shifting from the 3D view to another view when such knowledge is required. Moreover, this knowledge integration can take the form of semantic navigation links between 3D elements, thus accelerating the access operations within the 3D scene.
However, there is still work to do, especially in finding and testing the best way for representing the non-geometric urban knowledge and integrating it into 3D city models.
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7. ACKNOWLEDGEMENTS
We would like to thank El Mustapha El Atifi for his work on the definition and implementation of the 3D knowledge visualization system.
