Semantic Interpretation of Digital Aerial Images Utilizing ...

4 Chapter 1. Introduction
Figure 1.2: A scene of Berlin rendered and visualized in Google Earth (left) and the
corresponding CityGML model (right) with overlaid buildings, represented in LoD 1 to
3. Note that both models have been generated with massive human interaction.
out any attention to photographic realism. This gets improved by a LoD-2 with building
blocks showing generalized rooftop shapes. LoD-3 is the photo-realistic full model of
each building and LoD-4 contains sufficient details to enter a building. Standardized files,
compactly representing the queried data, can be streamed efficiently over the Internet and
get rendered immediately at the users workstation. Figure 1.2 depicts a photo-realistic
visualization provided by Google Earth and a corresponding CityGML rendering of a
scene located in Berlin. Similar to the CityGML standard, procedural modeling [Parish
and Müller, 2001] aims at constructing synthetic 3D models and textures from sets of
defined rules and shape grammars.
1.3 Semantic Interpretation of Aerial Images
In order to construct semantically enriched virtual cities that support search queries or
specific information extraction, every mapped object has to be recognized and assigned
a semantic class label as a first step toward a full image understanding. The problem of
image understanding covers the interpretation of the scene with respect to mapped objects,
locations and the their relationships. Figure 1.3 shows a scene taken from two different
viewpoints. The high overlap enables an estimation of 3D scene geometry, which can be
used to improve the semantic labeling of the scene. In this thesis we treat the problem
of scene understanding as a semantic labeling process, where each pixel is assigned a
specific object class.
Due to huge variability the task of visual scene understanding is still a largely unsolved
problem. Among occlusions, illumination, viewpoint and scale changes, natural or man-