In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Voi. XXXVIII, Part 7B 
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CHANGE DETECTION OF BUILDING FOOTPRINTS FROM AIRBORNE LASER 
SCANNING ACQUIRED IN SHORT TIME INTERVALS 
M. Rutzinger a ’ *, B. Rtif b,c , B. Hofle d , M. Vetter e 
a ITC - Faculty of Geo-Information Science and Earth Observation of the University of Twente, 7500 AA Enschede, 
The Netherlands - rutzinger@itc.nl 
b alpS-Centre for Natural Hazard Management, 6020 Innsbruck, Austria 
c University of Innsbruck, Department of Geography, 6020 Innsbruck, Austria - benno.ruef@uibk.ac.at 
d University of Heidelberg, Department of Geography, 69120 Heidelberg, Germany - hoefle@uni-heidelberg.de 
e Vienna University of Technology, Institute of Photogrammetry and Remote Sensing, 1040 Vienna, Austria - 
mv@ipf.tuwien.ac.at 
Commission VII 
KEY WORDS: Airborne laser scanning, building segments, classification, object-based change detection, urban areas 
ABSTRACT: 
Several recent studies have shown that airborne laser scanning (ALS) of urban areas delivers valuable information for 3D city 
modelling and map updating. Building footprint detection from multi-temporal ALS lacks in comparability because of changing 
ALS flight parameters, flying season, interpolation settings if digital elevation models are used, and the ability of the used building 
detection method to deal with these influences. So far, less attention has been paid to change detection of buildings within a short 
time span (approx, three months), where major problems are the high variability of vegetation over time and to distinguish 
temporary objects from small changes of buildings, which are currently under construction and demolition, respectively. We 
introduce an object-based workflow to investigate how unchanged objects can be defined, which variability in the object appearance 
is allowed to define an object as unchanged, and at which threshold a change can be indicated. The test site is situated in the city of 
Innsbmck (Austria) where ALS data is available from summer and autumn in 2005. In an initial step building footprints are derived 
by an object-based image analysis (OBIA) detection method for each flight independently. The parameters for building detection are 
derived for a training site in order to automatically derive the rules of the classification tree. Then the object features of buildings 
derived from the different flights are compared to each other and separated into the classes unchanged building, new building, 
demolished building, new building part, and demolished building part. The results are verified by a reference, which was created 
manually by visual inspection of the elevation difference image of both epochs. For new buildings and building parts 90% and for 
demolished buildings and building parts 32% were detected correctly. The detection of demolished buildings is strongly influenced 
by the appearance of high vegetation, which is caused by the decreasing heights of trees by comparing summer (leaf-on) and autumn 
(leaf-off) ALS data. 
1. INTRODUCTION 
Urban areas are highly dynamic landscapes where changes 
occur in different rates and frequencies. Nowadays airborne 
laser scanning (ALS) data is available for several urban areas in 
Europe. The purpose of acquiring multi-temporal ALS data is 
on the one hand to have the most recent surface representation 
of a certain area and on the other hand to be able to perform 
change detection analysis for monitoring purposes. Change 
detection plays a key role in urban planning i.e. monitoring of 
urban sprawl and its dynamics (e.g. Durieux et al, 2008; 
Maktav et al., 2005) and to detect changes after natural hazards 
such as earthquakes (e.g. Vu et al., 2004; Rehor et al., 2008). 
The objective of this paper is to show how changes appear in 
ALS data, caused by either seasonal differences or by urban 
dynamics i.e. construction activities. It is interesting to see how 
these changes are captured in data, which was flown within 
only three months, which is a very short time period for urban 
multi-temporal ALS data sets. The aim is to explore the 
performance of multi-temporal building detection by applying 
the method of Rutzinger et al. (2006). 
2. RELATED WORK 
Champion et al. (2009) test four different building detection 
approaches (Champion, 2007; Matikainen et al., 2007; Olsen 
and Kudsen, 2005; and Rottensteiner, 2008). The input data 
were infrared orthophotos and digital surface models (DSMs) 
from image matching and for one test site from ALS. A 
comprehensive comparison was undertaken in order to 
investigate the impact of input data types, resolution, scene 
complexity and methods. The authors state that high quality 
DSMs are important for reliable building and change detection. 
However, the ALS data available in this study was first echo 
data, which made it difficult to differentiate buildings from 
vegetation. The detection of changing buildings in ALS DSMs 
was already early investigated by Murakami et al. (1999) by 
subtracting two DSMs and filtering the difference image in 
* Corresponding author.