2004 
| the 
a has 
1ages 
| also 
be a 
r the 
king 
tion. 
2 À 
aken 
        
  
> 
72. 
a are 
ts of 
Fer 
'ency 
data 
earch 
data 
] put 
] the 
0gics 
their 
gling 
y be 
e-by- 
n of 
same 
ation 
y the 
The 
color 
ation 
ation 
picts 
d on 
y the 
axis 
es in 
nages 
| this 
nages 
lution 
od to 
pping 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
Resolution 
  
^ 
Figure 3: 3D representation of image resolution. 
As an applicability example of the above visualization, 
imagine a forester conducting a tree vitality evaluation. Our 
visualization in figure 3 gives her an at-a-glance overview of 
available resolutions within the spatial extent of her 
interest. 
For better navigation in the 3D model, the user can zoom 
into an area of interest to further explore the available 
resolution. This is done by a sliding window, which is 
marked by an X in figure 3. The zoom-in version 
overlapping the selected window area is shown in greater 
detail in figure 4. This figure shows two images, with the left 
image having a resolution of 2m and the right one a 
resolution of 20m. To enhance the selection process the 
original images can be superimposed on the 3D illustration. 
The user can also rotate the 3D surface to investigate hidden 
areas. 
Resolution 
5200 NO 
  
14 
Figure 4: Zoom-in of selected area from figure 3 with 
superimposed images. 
4.1.2 Multiple Instances of a Single Attribute: An 
extensive data collection is likely to contain multiple 
images of varying quality covering the same area. In the 
following visualization (figure 5) the data with the highest 
quality data value is chosen for the z-axis. Color is used to 
convey whether there are additional datasets of lower 
resolution available for the same area. The lighter (yellow) 
color shows that there are no additional datasets, while the 
darker (red) color shows that there are additional data (i.e. 
high multiplicity). 
Multiplicity 
Resolution 
  
Figure 5: Representation of additional available imagery in 
lower resolutions (multiplicity). 
Applying the above representation to our previous scenario 
gives our forester access to additional information that she 
would not be able to get solely from the 3D model. The user 
would like to know what other datasets exist to facilitate 
more detailed processes. This would be especially useful 
when multiple images of different resolutions covering the 
same area would be required. For example she might be 
interested in a low resolution satellite image to get an 
overview of forest density areas, and based on that, 
subsequently use a high resolution aerial photograph to 
extract the vitality of single trees. 
4.2 Combination of Attributes 
The next step is to combine information on multiple quality 
attributes in one image. For this we use 3D representations 
and superimposed color. Unlike the previous example that 
used color and 3D to communicate different aspects of the 
same attribute, below we discuss how to combine different 
attributes in a single visualization. 
4.2.1 Combination of Two Attributes: In order to 
effectively combine the visualization of two attributes, the 
first attribute is depicted along the z-axis of the 3D image. 
The second attribute is conveyed by the color that is 
overlaid. Figure 6 shows the combination of the attributes 
of resolution and currency. The images of the highest 
available resolution are displayed on the z-axis (as before), 
while the currency is communicated with the help of 
variations in color hue. Lighter shades of green represent 
older data, while the more the color changes towards blue 
(darker) the more recent the data is. When multiple images of 
the same resolution but taken at different times are present, 
we communicate the most recent available image. 
This visualization supports users that are interested in the 
currency of the data in addition to resolution. A city planner, 
for example, whose task is to identify a site for a new 
housing development wants to use geospatial images for a 
first overview of the area. One of the important parameters he 
considers in his evaluation is road access to the site. In order 
to effectively do so, he can use the above visualization to 
extract the most current data with sufficient enough 
resolution to identify roads. 
1005