A GIS-Based Approach to Real-Time Open Space Visualization

Transcription

1 A GIS-Based Approach to Real-Time Open Space Visualization Lutz ROSS and Birgit KLEINSCHMIT 1 Introduction Visualizations have always been a technique for planners to communicate their ideas. Today s computer-generated visualizations and models are increasingly involved in environmental and spatial planning processes. These methods are used to achieve an improved knowledge of spatial structures and phenomena. Moreover they are used to communicate projects to the public, stakeholders and decision makers. To support the creation of appealing and realistic visualizations used not only for computer graphics specialists, it is necessary to develop easily applicable tools. This presumes that visualization tools are adaptable to the typical workflow in planning processes. Therefore they have to support automated visualizations from CAD and GIS input data. Recent studies on 3D landscape and city visualization led to the development of software systems and methods for GIS based visualizations (e.g. APPLETON & LOVETT 2003, APPLETON ET AL. 2002, WERNER ET AL. 2005). At the same time, advances in computer graphics allowed for these visualizations to become real-time, interactive and very complex. Still many different difficulties have to be overcome. The department of Computer Graphics Systems of the Hasso Plattner Institute (HPI) developed LandXplorer, an interactive, real-time 3D geo visualization system supporting city model generation, large-terrain rendering and interactive editing. The objective of the study is to investigate the suitability of LandXplorer for open space visualization. Common GIS software, however, was used for data preparation, analysis, and visualization. 2 Public Open Space Planning Public open space includes public gardens, parks, greenways, village green and squares. In short one could say that all accessible and unbuilt land in urban areas can be defined as public open space. The term unbuilt refers to areas without buildings. Nevertheless, the terrain surface is a conglomeration of man-made surfaces. The great number of open space types lead to a wide diversity of structures and objects that can be found in these areas. The typical scale of open space plans ranges from 1:100 to 1:5,000. On this large scale detailed information about ground cover, vegetation and additional objects are included. They include an abstract visualization of the adjacency, which typically includes buildings, linking elements and important lines of sight, too. Considering this general framework, a visualization of an open space planning task has to include detailed ground structures, vegetation models and other structures. Moreover, abstract models of the adjacency are needed. When dealing with such miscellaneous structures, it is important to classify these objects, using existing methods and integrating different levels of detail.

2 2 L. Ross and B. Kleinschmit In cooperation with the city council of Potsdam near Berlin, Germany, a suited area for the case study was found. The urban development plan envisions the reconstruction of the church Garnisionskirche (Fig. 1), which was destroyed during the Second World War. Moreover, the nearby city channel, which was removed in the 1960s, is planned to be restored. This implicates the demolition of existing buildings, a new traffic concept and the rearrangement of the public open space. Fig. 1: Study area in Potsdam

3 A GIS-Based Approach to Real-Time Open Space Visualization Landscape Elements and Existing Visualization Methods ERVIN (2001) distinguishes six basic landscape elements out of which a landscape is composed: landform, vegetation, water, structures, animals and atmosphere. The landform (or terrain) is the base layer for most landscape models and describes the surface of the landscape. Research by SUTER (1997) and LANGE (2001) showed the suitability of digital terrain models (DTM) in combination with draped geotextures from remote sensing systems for the visualization of the background of landscape scenes. The visualization of the foreground by image draping has two major disadvantages. The resolution of remote sensing images is too low for a realistic visualization. High-resolution satellite sensors reach a ground resolution of 0.65 meters, which is far from the resolution the human visible system is able to perceive. This ground resolution can be enhanced up to 0.1 meters which is still too low for objects at a very close distance using aerial images. The geometric shape of visible objects is an important aspect of perception, but geometry is disturbed in remote sensing images. The distance at which the human visual system perceives an object s shape instead of its surface differs depending on the objects size and structure (BOOTHE 2002). The restrictions described above can be improved by the use of high-resolution textures and by the creation of detailed models for foreground objects. Therefore a digital terrain model for the representation of the adjacency is combined with a ground cover made out of 3D objects for the planning area and its closest adjacency. Realistic modeling and visualization of plants or even vegetation proves to be a very difficult task. DEUSSEN (2003a, 2003b) described state of the art plant modeling methods for generating realistic-looking 3D models. However, the sheer size of geometric primitives needed for these models makes it difficult to create realistic-looking vegetation with millions of 3D models. The problem of visualizing vegetation in landscape scenes is addressed by MUHAR (2001), APPLETON et al. (2002), and by the Lenné3D project (PAAR ET AL. 2004). Plants and vegetation can be visualized using textures, billboards and 3D models. In this study, detailed 3D models and geotextures are used in LandXplorer and billboards are used in ESRI ArcScene. The visualization of realistic-looking and acting water is another complex task. Recent advancements in programmable graphics processing units make these special effects available to real-time rendering software. The visualization of people and animals is not part in this study, as well as, the modeling of atmosphere. The latter is visualized using the standard lighting system of LandXplorer and ArcScene. Many approaches exist for the modeling of structures. This is due to the fact that the word structure is a very unspecific term, which includes many different things. A very broad definition for structure is: something that is constructed. This includes buildings, streets, sidewalks, squares, walls, traffic lights, stairs, parking lots, energy and water supply structures, disposal structures, etc. In order to classify structures in this study, four basic types of structures are distinguished: buildings, surface structures, utility structures and supply structures.

4 4 L. Ross and B. Kleinschmit The modeling and visualization of buildings is one key point of research (e.g. DÖLLNER 2005, KOLBE & GRÖGER 2005). In architecture, it is common to construct high-resolution models in CAD systems. This approach is suitable for one to a few buildings but not for larger areas. In order to visualize a great amount of buildings, more abstract models are needed. These can be created using existing 2D geodata, aerial photographs, laser scanning or by a combination of datasets and methods. The German federal geoinformation dataset (Amtliches Topographisch-Kartographisches Informationssystem ATKIS) includes the ground plans of buildings and attributes about the eaves height and the roof type. From this data, simple block models can be created through extrusion. Moreover it is possible to generate simple roof types in LandXplorer. The city of Potsdam was modeled by the use of these methods. Buildings in the planning area and the direct adjacency are modeled in more detail using a LandXplorer plug-in called Smart-Buildings (DÖLLNER 2005). One way of modeling surface structures is by the use of 2D vector information to which high-resolution textures are applied. These models lack volume and depending on the terrain modeling approach, are often not applicable to modeling vertical surfaces. CAD or 3D modeling software is often used to model single objects or detailed ground structures. These models lack semantic information and do not allow for further analysis. A different way of modeling surface structures will be discussed in section 3.2. Utility structures like fences, benches, bus stops, traffic and streetlights are important elements in large-scale visualizations, as they determine the character of an area. Furthermore utility structures carry secondary information, e.g. a bus stop sign indicates public transport. So far, no data model exists for the description of utility structures. For this study utility structure elements are added as.3ds objects. Supply structures and disposal structures like power and water supply lines and canalization are often underground and they are not primarily important for the task of open space planning. In this research we only address visible parts of supply structures and add them as textures or 3ds objects. 3 Methods As shown, a visualization of an open space plan includes many different elements. Major visualization tasks can be completed using existing methods and models. For the city of Potsdam an existing city model including a DTM, block buildings with simple roof types and an aerial photograph is used. This city model was created by the HPI using LandXplorer and is made from data granted by the land surveying office of Brandenburg (Landesvermessung und Geobasisinformation Brandenburg). Plants and vegetation can be visualized using 3D objects or billboards. Open space planning models require surface structures with a high level of detail. To fulfill the requirements, an object-oriented approach is used. For the case study the city of Potsdam provided the so-called City Map. This geodataset is created from ground survey and includes data about buildings, borders between different surface types and the type of covering, curb stones, trees, walls, stairs, fences, high points, gullies, etc. The original data is a set of polygons, polylines and points including z-values. After export to Shapefile format, no height information was stored in the data. In order to create a visualization of

5 A GIS-Based Approach to Real-Time Open Space Visualization 5 the surface structures, three steps were carried out: data preparation, definition of surface structure classes and creation of assignment of height information to the objects. 3.1 Data Preparation Data preparation involves the creation of an area-wide polygon dataset and assignment of existing ground-cover information to the resulting polygons. Data editing and storage is done using ESRI ArcGIS 9. In order to create an area-wide polygon dataset, the existing features were topologically corrected. As topology is not supported by the Shapefile format, the data therefore was transferred to a geodatabase. After that, the polylines and polygons were converted to a polygon dataset. Detailed information of ground cover stored in point features was transferred through a point in polygon selection to the new dataset. 3.2 Surface Structure Classes Six basic surface structure classes can be identified in the research area. In order to automate the visualization of surface structures, mandatory attributes are specified for each surface structure class. All objects have to be assigned the attributes CATEGORY, MATERIAL and TEXTURE. These three attributes given a polygon is described as surface structure object with information about the material it is made of and a texture that will be used for visualization. In the following section the surface structure classes and mandatory properties will be explained in detail. Table 1: Mandatory properties for surface objects CATEGORY MATERIAL TEXTURE DEPTH STAIR_ID GROUND_Z BUILDING X X GREEN_SPACE X X STREET X X X - - RIVER X X X - - STAIR_COMPONENT X X X X - WALL X X -/(X) - X BUILDING: Objects of this category are placeholders for buildings, which are stored in another layer. The reason we store these faces as surface structures and assign a texture to them is that buildings can be edited or masked out. If the ground plan of a building is changed or the visibility is turned off, gaps in the surface could emerge, which do not exists in reality. GREEN SPACE: The value is assigned to all objects that carry vegetation. In the model presented, these areas are textured faces without volume. We do not apply a depth to green space objects because they are a representation of the soil surface not the soil. A more global approach would be to integrate a 3D soil model, in order to compute ecological models with the data. STREET: This class includes all faces that are manmade for transportation and pedestrians movement including roads, sidewalks, bike paths or squares. These elements typically have a certain thickness, which is stored in the attribute DEPTH. The value specified here is used to extrude the polygons to create 3D objects and represent the thickness of the upper layer of ballast.

6 6 L. Ross and B. Kleinschmit RIVER: A river is treated as a surface structure with a certain depth, which is stored in the corresponding attributes. In order to model natural rivers, profile information would be needed. In the study area we dealt with a channel, which could be approximated by a rectangular profile. STAIR_COMPONENT: A stair is defined as a stack of polygons. Each polygon represents one step and is extruded with the value stored in the key DEPTH. In order to identify each stair, the attribute STAIR_ID is introduced. A stack of polygons with the same stair ID represents one stair object. WALL: Another typical surface structure element found in urban and rural environments are walls, which can have many different forms and functions. For our approach, we defined walls as linear elements with a rectangle profile. As the top of a wall can be horizontal or tilted we use the z-values of the Shapefile to describe the wall s top and define a ground value GROUND_Z to which it is extruded. This approach neglects the possibility, that a wall s profile can be trapezoid or irregular. 3.3 Height Information As no height information is stored in the provided dataset, it had to be reconstructed. Therefore digital terrain models are made from selected ground points and the polygons are projected on the resulting terrain surface. In order to visualize the vertical breaks between the main roads, the sidewalks and green space, one DTM is created from height information measured on the major roads and one is made from height points on sidewalks and green space areas. This approach works well for plane surfaces, which cover the predominant part of the study area. Still some refinements had to be carried out: Stairs and freestanding walls were constructed manually and areas containing retaining walls were corrected manually. 3.4 Data Processing and Visualization The resulting dataset describes the top face of each object in the study area as a set of x, y, z-coordinate triples. By vertical extrusion with the values from the DEPTH-attribute, this dataset can be displayed as a 3D Scene in ArcScene, which supports the use of textures and 3D symbols. The use of textures in ArcGIS is limited, because extruded features are not created as 3D objects. Currently, it is not possible to assign textures to single facades of buildings. 3D symbols can be used to create textured buildings but they have to be constructed in the external software, and inserted at anchor points, which force the user to store extra information regarding their position and alignment. As this workaround was considered too time-consuming, only a block model is presented in ArcScene and 3D symbols are used for the visualization of trees. An introduction to further visualization possibilities in ArcGIS 9 is given by TIEDE & BLASCHKE (2005). The visualization restrictions in ArcGIS do not apply to LandXplorer. Buildings exist as 3D geometry and can be refined and textured using the Smart-Buildings editor. At the same time, the rendering engine and software architecture can handle a very large amount of high-resolution textures, making it possible to assign individual textures to a great number of objects. The prototype software using the LandXplorer libraries is programmed by the HPI to visualize the surface structures. Geometric information from the polygons and the specified attributes are used to create textured 3D objects. The resulting geometry is

7 A GIS-Based Approach to Real-Time Open Space Visualization 7 integrated into the existing city model of Potsdam. Plants can be represented as billboards or as 3D models, which creates a far more realistic impression. 4 Results The first results are presented in Fig. 2, which shows the data visualized in ArcScene with added tree symbols and buildings as block models. The visualization gives a first impression of the spatial structures in the planning area. As one can see, the aim to create a detailed 3D visualization of the surface structures could be achieved. At the same time, the visualization is also linked to the database in ArcGIS, and thus it is possible to execute a 2D spatial analysis. A typical question in landscape and urban planning is the relationship between sealed area and green space. This question can easily be answered based on the model, using established GIS analysis methods. Another field of application is the use of visualizations in public participation. Fig. 2: Visualization in ArcScene 5 Discussion and Outlook With the approach presented, it is possible to generate detailed visualizations of surface structures from GIS datasets for open space planning and city models. All important open

8 8 L. Ross and B. Kleinschmit space planning elements are represented through a combination of the surface model with a vegetation layer, a city model, aerial images and utility objects. The simple data model for surface structures presented is suited for visualization and typical landscape planning analysis. For other open space structures, the model will surely need extensions. For example, underpasses or bridges cannot be represented. Another restriction of the model is the manual assignment of height information, which can be solved using a high-resolution digital surface model. The definition of a comprehensive data model for landscape elements is still a challenge for the future. In the field of city modeling efforts are made to define a model scheme (CityGML) to describe buildings, man-made artifacts, vegetation, water bodies and transportation facilities. This scheme includes the geometrical, topological and semantic aspects of 3D models and supports five levels of details (KOLBE ET AL. 2005). Further research should be done to test if the CityGML model can address the requirements of landscape and urban planning or if an integration of the presented model in CityGML is possible. A survey among landscape planning students is planned for the future. Therefore the students will be given typical planning questions, which can be answered by navigating through the scenes. The use of navigation techniques, editing and information tools will be written to log files. We hope to find indices for which navigation techniques are preferred and what information tools are used. Another question for the future is the suitability of the model for interactive real-time open space planning. 6 Acknowledgement We would like to thank Jürgen Döllner, Henrik Buchholz and Oleg Dedkow from the Department of Computer Graphics Systems at the HPI for their helpful discussions, programming effort and the provided city model of Potsdam. We also would like to thank Philip Paar, who talked us into the project and made the use of plant models out of the Lenné3D project possible. Furthermore we would like to thank the city of Potsdam for their cooperation and the data provided. 7 References Appelton, K., A. Lovett, G. Sünnenberg, T. Dockerty (2002): Rural landscape visualisation from GIS databases: a comparison of approaches, options and problems. Computers, Environment and Urban Systems, 26: Boothe, R.G. (2002): Perception of the visual environment. Springer-Verlag New York. Deussen, O. (2003a): A framework for geometry generation and rendering of plants with applications in landscape architecture. Landscape and Urban Planning, 64(1-2): Deussen, O. (2003b): Computergenerierte Pflanzen Technik und Design digitaler Pflanzenwelten. Springer Verlag, Berlin Heidelberg Döllner, J. (2005): Smart-Buildings 3D Stadtmodelle. Geobit 3:

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