DATA SOURCES FOR THREE- THREE-DIMENSIONAL MODELS
3.3 In the foreground, both tree objects and trees in the geospecific texture
image coincide sufficiently well that the trees in the image appear to be shadows of the tree objects. This is fine until changes in tree distribution are modelled
image, it appears as a green speck or, in a high-resolution aerial photo, as a green circle.
If this geospecific texture is naïvely applied on the ground, the result looks good from a distance but, on closer inspection, the tree looks simply like a green circle on the ground.
If the landscape data includes the location of the tree, it can be drawn as three-dimensional geometry when close, and not drawn from far away. At the transition point, the ground texture must change from a green circle to a best-guess geotypical texture of the ground under the tree. Figure 3.3 shows a geospecific ground texture including trees and tree objects placed at the corresponding location.
Vegetation
Data describing vegetation comes in a very wide variety of forms:
• point features may be used to describe the locations of individual plants, sometimes including attributes for each instance;
• polygonal data (coverage) may represent any aspect of the vegetation in that area—
species, species mix, height, density, etc.
• bitmap or raster data (bitmap/raster coverage) is an alternative to polygons, which also may indicate any number of vegetation attributes.
This is a field traditionally handled by GIS software, so the data is likely to be found in any of the common GIS formats or, for US data, USGS formats. Unfortunately, to date, there are no standards for the attributes in these formats. Attributes vary widely and depend heavily on the particular needs and interests of the producer of the data.
It is sometimes necessary to combine more than one source of data in order to determine vegetation. For example, an agricultural department may provide a land cover map, with classification of land into urban, agricultural, desert, sparse and heavy wild vegetation. Biologists may provide a species distribution map, showing the mix of species (sometimes called an ecotype, ecotope, ecosystem or biotype). By combining these two sources (data fusion), it is possible to estimate both species and density for visualization purposes.
Other possible sources include using elevation or slope (from an underlying elevation layer) as an input to the distribution heuristic. This is very valuable if certain species are known to prefer specific ranges of elevation or slope, especially in the absence of precise land cover data.
Some types of vegetation such as large trees are well suited to point data, whereas others such as grasses can only be practically represented as a coverage distribution. In fact, many groundcovers are represented as a colony of clones or even a single organism/object with a wide horizontal extent! Efficiently modelling of grasses is still an active research area (see Chapter 4, the section ‘Efficient modelling and rendering of landscapes’, p. 56).
When rendering vegetation for visualization, there are a few approaches for creating three-dimensional geometry.
• Billboards—a texture map with a complete image of the plant is applied to either a single rectangular polygon which turns to face the user, or a set of two interpenetrating rectangles which gives some illusion of parallax.
• Directional billboards—a set of texture maps of the plant from several angles is stored, then the appropriate image is used at rendering time. This requires a great deal more memory than simple billboards, but looks more realistic and is still fast to render.
• Explicit modelling—a conventional three-dimensional modelling tool, or a software package that knows about plant geometry, can be used to produce a detailed three-dimensional model of the plant. This requires a great deal of memory and is slow to render, but can be worthwhile when the visualization requires specific plant instances to be draw realistically from all viewpoints.
One very active area of vegetation modelling is forestry (see Chapter 6). Modern forestry depends on accurate GIS data for its operation, so there is a large body of work on this subject. Many of the attributes used for representing forests, such as diameter at breast height (DBH), a measure of the thickness of the trunk of a tree, evolved specifically for their importance to the forest industry. Unfortunately, there is not a standard for these attributes either, so it is a manual process of discovery when a (typically GIS format) file is found with forest data, to understand and utilize the attribute fields.
Water
Moving and stationary bodies of water are represented in a variety of ways depending on their area of application. The general term for mapping and analysing bodies of water is hydrography. The term bathymetry is also used, most commonly when describing oceans.
The freely available USGS Hydrography DLG dataset demonstrates a common approach from the cartographic domain: bodies of water are describes as polygons, and
Data sources for three-dimensional models 41
rivers are described by polylines (i.e. lines defined by multiple points). This simple approach is sufficient for drawing a map, and for some visualization tasks, but not sufficient for more advanced tasks such as analysis, simulation or navigation. Hence, more detailed models are used.
Historically, the depth of water bodies was first measured as sample points, known as soundings or depth readings. These were gathered because of their importance for navigation of ships. Later, for areas which were well surveyed, contour lines came into use, similar to those for elevation of dry land. Finally, more recently, grid representations have been used, especially for large bodies of water, including oceanography.
This progression is fortunate for the field of visualization, since grids are well suited for that task. However, when you are faced with a visualization project for an area that includes water, you are very likely to encounter either contours or, most probably, sample points. These will need to be converted to a grid with appropriate software which performs the interpolation guesswork. Water depth is clearly significant when seeking to simulate underwater conditions. However, it is also relevant to above surface visualization because of changes in water appearance with depth.
Buildings and other artificial structures
There are two common domains for representing buildings: CAD and GIS. Traditional drafting and CAD describes a building with a blueprint drawing, which provides just enough detail to describe how the building is to be constructed, but generally lacks any kind of context such as geographic location. Three-dimensional CAD extends blueprints to an actual three-dimensional model, but still lacks geographic awareness.
The GIS approach, and paper-based cartography before it, describes a building with, at most, a footprint, but it does explicitly state where the building is geographically. A footprint is simply a polygon which roughly describes the area covered by the building, and possibly some overall attribute fields, such as height.
Visualization needs both these domains—it needs to know what the building looks like, and where to place it on the terrain. The convergence of these representations is still an area of research and development, but here are two ways in which visualization generally works.
First, in the case where only footprints are known, some software can create procedural geometry for the building. This process takes very minimal inputs (such as the footprint and height) and produces a ‘best guess’ three-dimensional building for rendering. This is sometimes called parametric representation because the building is described implicitly by a set of parameters, rather than explicitly. An example is shown in Figure 2.9(b). Advantages of procedural representation include efficiency, since very little information needs to be stored, and power, since major changes like adding a storey can be done with one simple operation. The disadvantage is that the uniform approach makes it difficult to represent complex and irregular buildings, which are increasingly common in modern architecture.
The second approach is to leverage the power of three-dimensional modelling software by using existing detailed building models or creat-