The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi XXXVII. Part B4. Beijing 2008 
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A thorough comparison of road tracking and path/network 
optimization in terms of effectiveness and acquisition quality 
has not been conducted up to now. Nonetheless, behavior and 
acquisition time of the tracking algorithm of (Baumgartner et al., 
2002), for instance, has been extensively tested and evaluated 
(Scholderle, 2005). Neglecting the time for data handling, geo 
coding, and so forth, we experienced a reduction in plotting time 
of up to 50% depending on the complexity of the scene. For 
most rural scenes the time effort was reduced to 50%-70%. For 
more complex scenes, i.e., for urban or suburban areas, the 
performance of the tracking tool was too poor to compete with 
snake algorithms. As expected, in urban areas the automatic 
tracking failed very often, and putting the tracker back on the 
road every few seconds is quite annoying and time consuming. 
4. CONCLUSION AND OUTLOOK 
Figure 7. User-assisted road tracking in aRADARSAT SAR image. 
Yellow: tracked road sections; blue: user clicks. 
Both strategies of semi-automatic road extraction have 
complementary advantages and limitations. Ideally, it would be 
possible to combine both in a unique framework. From a 
methodological point of view, this would include that 
appearance-based components of road tracking should be 
incorporated into the optimization framework of network 
snakes. This would help to utilize more knowledge about the 
objects during optimization. Furthermore, it would allow to 
evaluating the results obtained with snake algorithms - an issue 
still not solved for the general case so far. Although first 
attempts have been undertaken, a sound solution has not been 
found for this so far. Nor it seems possible to estimate from the 
data which strategy for user-assisted road extraction is 
promising for a given scene and which not, so that the better one 
could be provided to an operator. Therefore, our current concept 
for road extraction in the context of disaster management is 
designed to provide both modules to the operator and hand over 
the choice of the appropriate procedure to him. 
Especially under the light of today’s and tomorrow’s available 
optical and SAR satellite systems, the development of integrated 
approaches for object extraction from multi-sensorial images are 
a substantial element to support fast and accurate information 
extraction. To this end, models and extraction strategies need to 
be developed that integrate the different geometric and 
radiometric sensor characteristics attached with stochastic 
models to accommodate for the inherent modeling and 
measurement uncertainties. Despite of encouraging results, there 
are still many fundamental questions to be solved for object 
extraction, e.g.: 
• Which type of modelling is appropriate to capture the vari 
ability of the object classes, especially under the light of the 
success of appearance-based approaches? 
• Which relations between objects can support object extrac 
tion, and which are more or less clutter? 
• Is it possible to design a strategy that adapts itself to the 
given extraction without loosing control over the computa 
tional load? Or, is it better to start with a monolithic strategy 
and incorporate dynamic elements or use generic search al 
gorithms and apply heuristics to control the search space? 
• Which decisions should be handed over to a human operator 
and which can be done by the computer? 
The challenges for research and development in this area are 
laid out and well-known. Time will show whether they can be 
successfully met in the long run. 
Figure 8. Road network optimization in a SAR image. Top: Interactive 
initialization; bottom: result after optimization.