Download Functional object grouping – An advanced method for integrated

Functional object grouping – An advanced method
for integrated spatial and space related data mining
Christoph AUBRECHT*, Mario KÖSTL and Klaus STEINNOCHER
Austrian Research Centers GmbH – ARC, systems research
Donau-City-Str. 1, A-1220 Vienna, AUSTRIA
Abstract
In this paper an integrative modeling approach – functional object grouping – is presented
featuring the combination and joint analysis of several spatial and space related data sets
differing both in their thematic and geometric representation. Remote sensing data and
socioeconomic information are considered as one base data pool in order to derive process
related information rather than being limited to detection of physical properties. Especially
with regard to natural hazard related analyses like assessment of damage potential and risk
exposure this is a promising approach. Through the creation of object based models it is
possible to expand processes to different spatial regions with minimal adaptation efforts.
1
Introduction
The information content of Earth Observation data comprises objects and phenomena on
the surface of the earth including different types of vegetation or man-made objects such as
buildings and infrastructure. Classification of these data is therefore limited to physical
properties of these objects but does not include process related information. With respect to
man-made objects this means that buildings can be detected, and their size and roof material can be determined. However, the use of the building, whether it is an apartment building
or a department store, how many people it may accommodate or the number of employees
working there, cannot be derived from image classification alone. With regard to a possible
natural hazard related assessment of damage potential and risk exposure this information is
essential.
In order to collect potential damage as a whole a terrestrial survey of each building would
be necessary. This is neither feasible nor effective, as it would require an enormous effort in
time and work and result in tremendous costs. Much more practical is basing the assessment on functional groups whose objects show similar characteristics. These characteristics
are derived on the one hand from the geometric properties of the objects and on the other
hand from socio-economic and other geo-spatial information. Functional object grouping is
therefore related to the integration of remote sensing and socio-economic information, a
research task that is relatively new and challenging (CHEN 2002, POZZI & SMALL 2005,
STEINNOCHER ET AL. 2006).
*
Correspondence to: [email protected]
C. Aubrecht, M. Köstl and K. Steinnocher
Due to the increasing availability of geo-spatial data the integrative approach can be extended to additional data sets, such as zoning plans, census statistics and address data.
While census data are normally aggregated to areal units address data refer to points,
representing “the ultimate in disaggregation” (MESEV 2005). As addresses are usually
linked to buildings (or parts of buildings) combination of these point data with remote sensing has a huge potential for improving the classification of built-up areas and add valuable
information on the use of buildings. However, the integration of these information sources
into remote sensing or more general into mapping processes is a challenging task due to the
different thematic and spatial representations of the data sets.
Objective of the functional object grouping is to assign each relevant object – building or
infrastructure – to one of the resulting functional groups. The result will be a map layer
where each building and infrastructure object is attributed with the characteristics of one
functional object group. In the next project stage this map layer will be one of the spatial
inputs to the modeling of potential economic damages and losses.
2
Data and study area
The study area which covers approximately 25 km2 is located in the northeastern part of the
Austrian province Vorarlberg (compare Fig. 1). The wider Arlberg region – a famous tourist destination – includes the towns Lech, Zürs, Stuben, St. Christoph and St. Anton. Lech
which has around 1,300 inhabitants and lies at an elevation of 1,450 m above sea level
shows all typical characteristics of an alpine touristic environment – hotels, vacation homes
and an overall rather low building density. The study area is divided by a main valley including the river Lech going from the southwest to the northeast. A second, smaller, valley
including the river Zürsbach enters the center of Lech from the south. The main residential
areas are situated at the bottom of the valleys near the rivers while the hillsides are mostly
covered by forest and alpine meadows.
For the presented study a set of diverse spatial and space related data is used. Firstly high
resolution optical satellite data (SPOT) and aerial imagery were analyzed applying an automated object-oriented classification approach in order to create a 2-dimensional layer of
building outlines. For visualization purposes a GIS-based generalization process was run
resulting in the final 2D building layer with smoothed contours (algorithm described in
AUBRECHT ET AL. 2007). Based on Airborne Laserscanning (ALS) data several topographic
models like Digital Terrain Model (DTM), Digital Surface Model (DSM) and various difference models (e.g. normalized Digital Surface Model, nDSM) were derived. The ALS
data had been acquired in the framework of a commercial terrain mapping project covering
the entire province Vorarlberg. Due to the requirements of snow-free and leaf-off conditions several flight campaigns had been carried out between 2002 and 2004.
Regarding socioeconomic information both zoning plans (as defined by the Austrian Planning Law) and hazard zoning plans, geocoded address data as provided by the Austrian
Post (Data.Geo) and company information (derived from HEROLD yellow pages) are used.
Furthermore natural hazards reference data are integrated featuring cases of damage recorded at the severe flooding events in 2005.
Functional object grouping - An advanced method for integrated spatial data mining
Fig. 1:
3
Location of the study area (Lech) in the province Vorarlberg, Austria.
Workflow of the modeling process
As mentioned above the assessment of potential damage on buildings and infrastructure
cannot be based on remote sensing alone but requires additional information on the function and use. This information can be derived from a variety of spatial data sets including
zoning plans and address data. In order to integrate these different types of information a
conceptive model has been developed (see Fig. 2), ready to be implemented in ESRI’s
ArcGIS model builder for automated processing. The underlying idea with developing an
object based model is to create a concept by means of one test site that can be applied to
other test sites with minimal adaptation efforts.
Figure 2 shows a generalized version of the model constructed for this project. Basically
the model consists of three branches (blue background) whose results are joined in the end
leading to a three-dimensional functional building model that includes information valuable
for further natural hazards related assessment of damage potential and risk exposure. Input
data sets are colorized in orange, intermediate results in grey and the output file is marked
in red. White boxes indicate operations applied to input data sets.
C. Aubrecht, M. Köstl and K. Steinnocher
Fig. 2:
Functional object grouping – Process workflow of an integrated urban system
model.
At first the two-dimensional building layer providing information on footprint and shape of
the objects is reduced to a point layer consisting of the buildings’ label points. Label points
are in this case preferred to center points (also called centroids) as they are located inside
the building boundaries on all accounts. Centroids are the starting basis for label points
anyhow – the difference is that points with calculated locations outside of a building are
consequently shifted into the 2D object using a minimum distance operator (AUBRECHT &
STEINNOCHER 2007). The next step in the process workflow is to determine the mean
height of the buildings by looking at the nDSM. That model being a difference model of
DTM and DSM shows the height of objects over ground (PFEIFER 2003). Building height is
extracted by interpolating the Z value at the location of the label point. The first intermediate output file is the building points file with the object height as additional attribute.
In the next steps ancillary information is attached to this point layer. The DTM value is
extracted the same way like the nDSM value providing information on the buildings’ height
above sea level. Furthermore zoning information and hazard zones are integrated into the
model by spatially joining these 2D layers to the point layer.
In the second branch of the model geocoded address point data is spatially joined to the 2D
building layer that was also used as starting point previously. Based on the address infor-
Functional object grouping - An advanced method for integrated spatial data mining
mation of these data tabular address-coded company data can be introduced into the model
essentially enhancing the functional information content.
With a georeferenced list of cases of damage the third branch brings in another point data
set that can be joined to the initial 2D building data. This gives information on already
affected objects enabling analysis and interpretation of hazard zone delineation.
Finally this enhanced 2D building data set is joined with the other point data resulting in a
three-dimensional building layer featuring physical information like object height above
ground and height above sea level, as well as functional information on zoning, information
on constituted hazard zones, address information, company information and information on
previously recorded cases of damage.
The address data also includes the number of postal delivery spots per building and a classification of private and business addresses. Together with all the other information integrated in the model this allows a description of different building types and thus the identification and grouping of functionally similar objects.
4
Results
Figure 3 shows the functional building model derived by integrated data analysis.
Functional object grouping results
in several building classes (residential, business, hotel sector, public
service, infrastructure, agriculture
and various classes of mixed use).
For the residential buildings the
ancillary information on number of
delivery points per address can be
used to distinguish different housing types (e.g. single family, semiattached, apartment).
The analysis of spatial correlations
between buildings and hazard
zones shows that 20 % of all buildings are at risk of natural hazards.
More than 90 hotels and guesthouses are located within a yellow
or red zone (85 yellow, 7 red).
Fig. 3: Functional object grouping of building use.
C. Aubrecht, M. Köstl and K. Steinnocher
5
Conclusion and outlook
In this paper a spatial modeling approach on integrated data mining was presented. Through
combination of data sets featuring very diverse thematic and spatial characteristics functional object groups were derived. The creation of an object based model to be implemented
in ArcGIS enables applying process related modeling steps to different spatial locations
with minimal adaptation effort. A further step is to assess spatial population patterns by
disaggregating raster based census data.
The resulting functional 3D building model will form the basis for an assessment of potential economic damages and losses on a regional scale. The results of the functional object
modeling are of high relevance to urban data managers as well as to the hazard and risk
research community. It should raise awareness to open up to new approaches and research
fields in order to gain a better understanding of real-world functional spatial correlations.
References
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Acknowledgement
The presented work was funded by the FFG in the frame of the Austrian Space Applications Programme (ASAP).