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 materi-al 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 assess-ment 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: christoph.aubrecht@arcs.ac.at

C. Aubrecht, M. Köstl and K. Steinnocher

Due to the increasing availability of geo-spatial data the integrative approach can be ex-tended 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 sens-ing 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 tour-ist 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 in-cluding 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 au-tomated 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 dif-ference 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 condi-tions several flight campaigns had been carried out between 2002 and 2004.

Regarding socioeconomic information both zoning plans (as defined by the Austrian Plan-ning 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 record-ed at the severe flooding events in 2005.

Functional object grouping - An advanced method for integrated spatial data mining

Fig. 1: Location of the study area (Lech) in the province Vorarlberg, Austria.

3 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 func-tion 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 interme-diate 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 classi-fication of private and business addresses. Together with all the other information inte-grated in the model this allows a description of different building types and thus the identi-fication 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 (residen-tial, 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 hous-ing types (e.g. single family, semi-attached, apartment).

The analysis of spatial correlations between buildings and hazard zones shows that 20 % of all build-ings are at risk of natural hazards. More than 90 hotels and guest-houses 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 func-tional 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 poten-tial 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

AUBRECHT, C. AND STEINNOCHER, K. (2007) Der Übergang von Bodenbedeckung über urbane Struktur zu urbaner Funktion - Ein integrativer Ansatz von Fernerkundung und GIS. In: Schrenk, Popovich, Benedikt (Eds.): CORP2007: 12th International Conference on Urban Planning and Regional Development in the Information Society. Proceedings (pp. 667–675), CD-Rom. Vienna, May 20-23, 2007.

AUBRECHT, C., M. DUTTER, M. HOLLAUS AND STEINNOCHER, K. (2007) Objekt-orientierte Analyse von Fernerkundungsdaten mit anschließender Gebäudegeneralisierung als Ba-sis für 3D Visualisierungen im urbanen Raum. In: Strobl, Blaschke, Griesebner (Eds.): Angewandte Geoinformatik 2007. Beiträge zum 19. AGIT Symposium Salzburg (pp. 25-34). Heidelberg, Germany: Herbert Wichmann.

CHEN, K. (2002), An approach to linking remotely sensed data and areal census data. Inter-national Journal of Remote Sensing, 23(1): 37–48.

MESEV, V. (2005), Identification and characterization of urban building patterns using IKONOS imagery and point-based postal data. Computers, Environment and Urban Systems, 29: 541–557.

PFEIFER, N. (2003) Oberflächenmodelle aus Laserdaten. Österreichische Zeitschrift für Vermessung und Geoinformation (VGI) Heft 4/2003, 243–252.

POZZI, F. AND SMALL, C. (2005), Analysis of Urban Land Cover and Population Density in the United States. Photogrammetric Engineering and Remote Sensing, 71(6): 719–726.

STEINNOCHER, K., WEICHSELBAUM, J. AND KÖSTL, M. (2006), Linking remote sensing and demographic analysis in urbanized areas. In: Hostert, Damm, Schiefer (Eds.): First Workshop of the EARSeL SIG on Urban Remote Sensing “Challenges and Solutions”, March 2-3, 2006, Berlin, CD-ROM.

Acknowledgement

The presented work was funded by the FFG in the frame of the Austrian Space Applications Programme (ASAP).