Geoscience Reference
In-Depth Information
1 Introduction
Application and analysis of geo data is moving from traditional GIS applications
with 2D map data towards deployment of real 3D data. Virtual 3D city models
become more and more available for urban areas. More sophisticated tools for
data analysis and information extraction are under development. Quality assess-
ment becomes mandatory because reliable and reproducible processing results can
only be obtained with correct original data. Different views on the term “correct-
ness” exist, existing standards such as ISO 19107 or CityGML specification pro-
vide a good starting point. This is not sufficient for an unambiguous definition of
modeling guidelines. Consequently, a discussion of the definition of guidelines for
modelers and users and methods to check the data set for compliance with these
specifications are necessary.
A general overview of the concept of data quality in the geographic domain is
included in Kresse and Fadaie (
2004
), which offers a comprehensive summary of
the relevant standards, notably of the ISO 19100 series. The paper of Akca et al.
(
2010
) has a focus on geometric accuracy with respect to the generation process of
a model from Lidar data. Discussing the problems of polygonal models, Krämer
et al. (
2007
) define quality measurements for 3D city models. Some simple algo-
rithms for quality assessment and healing of geometries are presented.
Campen et al. (
2012
) provide an extensive collection of typical defects of
polygonal 3D models and existing techniques for processing and repair with
respect to different fields of application. A detailed analysis of completeness
and separation issues in city models is presented by Zhao (
2012
). They consider
typical properties of semi-automatic generated models and their insufficiencies
and develop a generalization method. However, other geometric errors are not
investigated.
Limited research was done regarding healing of 3D city models so far.
Approaches to repair triangle meshes such as Liepa (
2003
), Attene and Falcidieno
(
2006
) exist but can only be tied loosely to our approach. We map CityGML fea-
tures to an internal data structure which is designed to maintain links to the seman-
tic properties of the original model. Using volumetric techniques, as suggested by
Nooruddin and Turk (
2003
) requires conversion to a voxel representation which cre-
ates difficulties in maintaining model-inherent semantics. An alternative approach
is presented recently by Ledoux (
2013
). A top-down approach is described as
favorable because it enables repairing a model in one single step. The implemen-
tation shows that a hierarchical processing of the model is necessary before the
actual volume-based approach for healing solid defects can be performed. We pre-
sent an overview of our research results leading to the definition of certain quality
criteria for CityGML models and the development of an automated validation tool.
A quality report is the result of this processing step. It includes detailed descriptions
of all detected errors. This information is used as input of a healing process which
tries to repair as many errors automatically as possible. The healing procedures are
described in detail and experiences with the tool are discussed.
