International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004
the type of the building. Furthermore, also the outer surface of
the building, where the heat can emit, plays an important role.
Depending on the age of the building assumptions on the
insulation of the outer surface can be made and introduced in
the estimation of the building-specific heat requirement.
There are different typologies for residential buildings, for
which specific heat coefficients are known. These typologies
classify the buildings by means of size (one-family house,
multi-family residence) and age. This typology, however,
describes the characteristics of the buildings on a level which is
made for human interpretation. Therefore, the main task is to
set up rules that can infer the building type from a set of
observable and measurable building characteristics, and thus
link the mere geometric data with specific heat coefficients.
Besides geometric information of the building itself, namely
ground plan and volume, also their relative arrangement among
each other and to roads have to be taken into account.
The idea in our approach is to extract the geometric
characteristics using all available data (cadastral map,
topographic data, streets, ...). The characteristics relate to the
building itself, i.e. length, width, width-to-length-ratio, roof
type, etc. and they regard also the context, i.e. distance to
neighbouring buildings and roads. We use the given building
typologies and determine significant characteristics for every
type. This will be achieved using a Machine Learning
approach, that automatically derives the discriminating and
characterising attributes of a given classified data set.
Furthermore, we will also identify settlement areas with similar
characteristics using a clustering approach. Based on such a
settlement typology, the estimation of the heat demand for very
large areas will be eased.
The structure of the paper is as follows: after a description of
the workflow, the building and settlement typology is
introduced. Then methods for identifying building types and
settlement types will be presented. Finally, results are shown
that can be achieved using this method. A summary and outlook
on future work concludes the paper.
2. WORKFLOW
The way from the raw data to the heat demand consists of the
main steps.
l. determination of building volumes
2. classification into different building types and
3. calculation of the heat demand using the volume and
the specific heat coefficient.
The first step is the determination of building volumes. For the
determination of building volumes the main data are the laser
scanning data. But we also have tried out different
combinations of laser scanning and other GIS data. For the
following steps the combination of laser scanning and the
cadastral map is useful. With the attributes from the cadastral
map it is possible to link additional data with the building
volumes.
The next step is to classify the building volumes to different
building types. Each building type has a specific heat
coefficient. A lot of preparatory work has to be done because
there is no direct link between the geometry of the building
volume and the building types. To do so, the buildings are
classified into different types according to a given typology,
using only geometric properties available.
In the last step the volumes are combined with the specific heat
coefficient from the building typology. Then the heat demand
can be calculated and represented in a map.
For test purposes, the results achieved with the method
described here are compared with the data from a heat atlas,
that was available and was acquired with conventional methods.
A link between our results and the data from the heat atlas is
possible using the addresses of the buildings which are present
in both data sets.
ATKIS LIDAR ALK Heat Building
| } Atlas Typology
Volume Volume Volume
Determination Determination Determination
s SUE Building
Building Building Volume —— Link
Volume Volume with Attributes
Building Volume
with Attributes from
ALK and Heat Atlas
Classification. 44— ———— ——1 Analysis «— —
Calculation Enhanced
Building
Typology
Heat Demand
Figure 1: Workflow to derive heat demand map from
original spatial data.
3. BUILDING AND SETTLEMENT TYPOLOGY
A building typology classifies residential buildings according to
their size and the age. The used building typology is shown in
Table 1. Depending on the size and the age each type has a
specific heat demand.
From the one-family house towards the large morc-family
house the buildings become more compact. Therefore the
specific heat demand is reduced.
The limits for the building ages relate to different building
regulations which concern the quality of heat insulation. The
newer regulations demand better insulation. So newer buildings
have lower specific heat demands.
716
specific heat demand [KWh/m?*a]
building one- row-house small large
age family more more
house family family
house house
until 1918 205,5 199,6 187,8 124,4
1919-1948 206,0 173,2 151,1 168,9
1949-1957 252,4 162,5 174,9 140,6
1958-1968 185,3 161,8 179.7 160,4
1969-1977 155,4 146,2 136,6 139,4
1978-1984 139,5 133.3 109,0 105,9
1985-1995 139,5 115,5 81,4 75,8
1996-2000 105,9 106,2 95,4 86,4
Table 1: Building Typology.
One can easily observe that the description of the building types
only contains information about the heat demand and no
geometry. For statistical methods this is sufficient because
cities often have statistics about the types of buildings in their
town. However, if this is not available, the evaluation has to be
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Table 2:
