In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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order to remove elevation differences along edges of unchanged 
buildings. A building change detection method using 
exclusively ALS data in an object-based approach was 
presented by Vogtle and Steinle (2004). Vosselman et al. (2004) 
developed a method for updating cadastral maps by detecting 
buildings from ALS data and comparing them to an existing 
building footprint database. Further work on building footprint 
extraction and changed detection such as combining aerial 
images with ALS DTMs or using more recent remote sensing 
data for updating of existing cadastral maps is not reviewed 
here since the work presented focuses on the usage of ALS data 
only. However, for a comprehensive overview of related work 
on building footprint detection methods and building change 
detection the reader is referred to the recently published article 
by Matikainen et al. (2010). 
3. TEST SITE AND DATA SETS 
3.1 Test site 
The test site covers the major part of the city centre of 
Innsbruck (Austria). It comprises a densely built-up area with 
varying building types (multi-story block buildings, single 
family houses with gardens, large industrial buildings), 
agricultural land, and forested areas. The data sets also contain 
temporary objects such as cars, trains, market booths, etc. 
which appear as changes in the data (Fig. 2). 
3.2 Data sets 
The ALS data were acquired as pilot surveys for the laser 
scanning project Tyrol, Austria (Anegg, 2007). The two data 
sets overlap the major part of the city of Innsbruck, the city 
centre and parts to the west including the airport (Fig. 1). The 
overlapping area represents the border of two larger ALS 
campaigns in the north (summer scan) and south (autumn scan). 
Both flights were acquired with an Optech ALTM 2050. The 
average point density of both flights is around 4 pts/sqm. The 
test site covers an area of 10 km 2 and contains 2441 building 
footprints in the summer data set. 
flight showing the test site of Innsbruck (Austria) 
where multi-temporal airborne laser scanning data is 
available 
3.3 Change detection 
As a manual reference a difference raster (diffDSM) of both 
DSMs was calculated, which visualizes all differences in 
elevation, which might disturb the building change detection 
procedure. Figure 2 shows in green areas, where the elevation 
decreased and in red, where the elevation increased. 
First of all, building edges show increases and decreases, 
indicating here a shift to north west, which can be caused by (i) 
insufficient registration of the data, (ii) difference of scan angle 
in both scans and therefore different amount of echoes on 
building walls, and (iii) different echo distribution and local 
point density which effects the aggregation of points to raster 
cells when calculating the DSM. Further decreases are reasoned 
by parking cars, umbrellas in front of restaurants in the inner 
city, which were removed in the autumn scan (Fig. 2, lower 
arrow), maize fields, which were harvested, and deciduous 
trees, which lost their leafs. While in the summer scan the laser 
beam was reflected on the tree canopy, in the autumn scan the 
laser beam was reflected on the branches or even on the ground, 
which leads to negative heights in the diffDSM. An increase of 
elevation can be found in the city centre where the Christmas 
tree for the Christmas market was already installed (Fig. 2, 
upper arrow). 
Fig. 2. Different aspects on elevation changes in the difference 
digital surface model from the city centre 
4. METHOD 
The workflow of the proposed building change detection 
comprises two major steps, which are firstly the object-based 
building footprint detection (Sect. 4.1), which is applied for 
each laser scan independently and secondly the change 
detection procedure (Sect. 4.2). 
4.1 Extraction of building footprints 
In a first step a first-last-echo difference model (FLDM) is 
calculated, by subtracting the last reflection (lowest elevation) 
from the first reflection with the highest elevation in order to 
derive a vegetation mask. Elevation differences of reflections 
within a single laser beam mainly occur at the canopy of high 
vegetation and building edges. Hence, the difference model is 
further enhanced by applying a filter for removing long thin 
structures representing building edges and small areas (Fig. 3). 
The areas covered by the vegetation mask are set to “no data” 
and are not considered any more in the building detection 
process.