Geoscience Reference
In-Depth Information
2 Data Generation Process for 3D Network Model
When we consider 3D navigation systems we may need to solve complex topologies,
3D modeling, 3D network analysis and so on. For realizing all these processes we
need 3D spatial data. Data collection used to be the major task which consumed over
60 % of the available resources since geographic data were very scarce in the early
days of GIS technology. In most recent GIS projects, data collection is still very time
consuming and expensive task; however, it currently consumes about 15-50 % of the
available resources (Longley et al.
2001
).
Data generation is also still a problem for the researchers who work on GIS
based 3D navigation systems which consumes their time more than achieving
their applications or doing their research. Pu and Zlatanova (
2005
) point out that
automatically extracting geometry and logic models of a building is difficult. In
their study they shortly explain the advantages and disadvantages of the methods
used for constructing geometry models of buildings and state that there is not an
automatic approach for 3D reconstruction of the interior of buildings. They also
indicate that it is very difficult to generate logical model of a building from its
geometry model automatically as the nodes and links have to be created manually
or half-manually with computer aided applications.
To overcome this deficiency, 3D geometric and logical data of a building is
obtained in CityGML format using a 3D model generation software which is based
on a novel method called Multidirectional Scanning for Line Extraction (MUSCLE)
(Karas et al.
2008
). This model is a conversion method which was developed to vec-
torize the straight lines through the raster images including township plans, maps for
GIS, architectural drawings, and machine plans. Unlike traditional vectorization pro-
cess, this model generates straight lines based on a line thinning algorithm, without
performing line following-chain coding and vector reduction stages. By using this
model, it is also possible to generate 3D building models based on the floor plan of
the buildings (Karas et al.
2006
). This model can be described in 5 main steps:
• Threshold processing
• Horizontal and vertical scanning of the binary image
• Detecting wrongly vectorized lines
• Correcting wrongly vectorized lines by using diagonal scanning
• Comparing the lines created by Muscle model and correction of the topological errors
Process of the MUSCLE model can be summarized as showed in Fig.
1
.
2.1 Graphical User Interface Wizard for Generating 3D
Network Models
By using the MUSCLE Model described in Fig.
1
, the 3D Building and Topological
Network model of a building can be generated automatically from raster floor plans.
The user interface of 3D Model Generation Software is shown in Fig.
2
.
