The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B4. Beijing 2008 
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levels of abstraction of knowledge (Mayer & Steger, 1998). The 
extraction benefits from using different scales, since initial 
hypotheses can be efficiently formed in coarse scale, which are 
validated and tested in fine scale. Vice versa, intermediate 
results obtained in fine scale can be evaluated with respect to 
their importance for the global functional properties of the 
objects described in coarse scale. 
Recently, (Drauschke et al. 2006) analyzed the scale-space 
behaviour of building features. They show that there is no 
particular optimum scale for building reconstruction, yet they 
observe a stability of the features over certain intervals in scale- 
space. Thus, a low number of selected scales seems to be 
sufficient for building extraction. In the same spirit (Forberg, 
2004; Forberg & Mayer, 2006) show how the behaviour of 
buildings in different scale-spaces can be used for their 
generalization in 3D. They use so-called scale-space events in 
the morphological and curvature space to reduce the level of 
detail of buildings for cartographic generalization. For road 
extraction, usually two (Baumgartner et al. 1999, Hinz & 
Baumgartner 2003) but in some special cases also three (Mayer 
1998, Hinz 2004a) different scales have been used. While the 
coarse scale, i.e., 2m to 4m, allows to extracting roads based on 
their fundamental characteristics and functional properties - 
such as curvilinear shape, network characteristics, and optimum 
routes between certain places - the fine scale, i.e., less than 50 
cm, enables detailed analysis and precise geometric delineation 
of the road layout. (Heuwold 2006, Heuwold & Pakzad 2006, 
Heuwold et al. 2007) aim at generalizing this issue for a quasi- 
continuous scale space in 2D. The results shown in (Heuwold 
2006) are promising for the case of parallel linear structures as 
they often appear in the context of road extraction, but they also 
show that this kind of fundamental research is far from being 
solved in general. 
Remote sensing can contribute significantly to all these 
components in different ways, especially because of the large 
coverage of remotely sensed imagery and its global availability. 
Time, however, is the overall dominating factor once a disaster 
has hit a particular region. This becomes manifest in several 
aspects: firstly, available satellites have to be selected and 
commanded immediately. Secondly, the acquired raw data has 
to be processed with specific signal processing algorithms to 
generate images suitable for interpretation, particularly for 
Synthetic Aperture Radar (SAR) images. Thirdly, the 
interpretation of multi-sensorial images, extraction of 
geometrically precise and semantically correct information as 
well as the production of (digital) maps need to be conducted in 
shortest time-frames to support crises management groups. 
While the first two aspects are strongly related to the 
optimization of communication processes and hardware 
capabilities (at least to a large extend), the main methodological 
bottleneck is posed by the third aspect: the fast, integrated, and 
geometrically and semantically correct interpretation of multi- 
sensorial images. 
Concerning the automated extraction of objects, special focus is 
on the extraction, analysis and characterization of infrastructural 
objects like roads and buildings due to their importance for the 
immediate planning and implementation of emergency actions. 
In the following, we concentrate on the detection of intact road 
networks under the constraints of an effective disaster 
management. This means, that those parts of the road network, 
which are not detected by the system, should be regarded as 
destroyed and not accessible for rescue and evacuation teams 
anymore. A similar approach for buildings can be found in 
(Rehor & Bahr, 2006), where the goal is to decide if buildings 
in laser-scanner data are damaged, e.g., by an earth quake, and 
what kind of damage has occurred. 
Many of the discussed aspects are highly relevant for the current 
activities directed towards the design and implementation of an 
image understanding system for disaster management at the 
German Aerospace Center (DLR). The scientific challenges are 
moreover accompaigned with requirements of an operational 
environment, reasonable processing times and efficient 
workflows. The next section will outline this in more detail. 
3. OBJECT EXTRACTION FOR DISASTER 
MANAGEMENT 
There is no doubt, that remote sensing methods can provide 
valuable support for activities directed towards the prevention 
of natural hazards and managing the consequences of natural 
disasters. Techniques for automatic object extraction and 
change detection consequently play a major role in this context. 
However, in addition to the scientific and methodological 
challenges, the approaches to object extraction must be integral 
part of a comprehensive system of actions, documentations and 
planning activities during, after and also before a (potential) 
disaster. This is only possible if the image understanding 
methods are embedded into well-defined strategies for 
supporting the preparedness and prevention of hazards, for fast 
reaction in case of disasters, as well as for damage 
documentation, planning and rebuilding of infrastructure after 
disasters. 
In the following, we link the description of the system’s 
underlying concept and methodologies with the discussion of 
the issues of object extraction posed at the beginning: (1) 
system design and different levels of automation; (2) automatic 
extraction under the light of available sensors and data 
characteristics; (3) effectiveness/processing time and the need 
for internal evaluation; and (4) quality of the final results and 
verification/post-editing. 
3.1 System design: Different levels of automation 
At the moment, fully automatic approaches for object extraction 
must stiil be regarded as a subject of fundamental research, and 
they seem not to be able to find their way into operational work 
flows in the very near future. On the contrary, semi-automatic 
approaches seem more likely to be useful in operational 
applications. Automatically achieved results nonetheless may 
provide a basis for efficient checking, editing and improving. 
Hence, the framework for the automated detection of intact 
roads from multi-sensorial imagery is conceptually divided into 
three main parts. The first part comprises the (fully-) automatic 
extraction of roads. The results will be of course not 100% 
complete and correct. It follows therefore an internal evaluation 
of the automatically achieved results as second module. This 
provides measures about the reliability of road parts, which 
should guide an operator during editing the results. This editing 
phase - the third part - involves user-assisted tools to support 
effective checking and completing the results.