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CHANGE DETECTION FOR UPDATING MEDIUM SCALE MAPS USING LASER 
ALTIMETRY 
G. Vosselman, B.G.H. Gorte, G. Sithole 
Delft University of Technology, Faculty of Aerospace Engineering 
Department of Earth Observation and Satellite Systems 
P.O. Box 5058, 2600 GB Delft, The Netherlands 
{m.g.vosselman, b.g.h.gorte, g.sithole }@lIr.tudelft.nl 
Commission III, WG III/3 
KEY WORDS: Laser scanning, LIDAR, Classification, Change detection, Mapping, Updating 
ABSTRACT: 
To increase the update rates of topographical databases, research is performed to automatically detect changes using airborne laser 
scanning data. After the determination of the bare-Earth points, the remaining points have been classified as either points on 
buildings or points on vegetation. Additional usage was made of registered colour imagery taken during the laser scanning survey. 
The results show that buildings can be detected reliably using laser altimetry data sets. However, they also show that mapping rules 
(which buildings should be in the map and which can be neglected) need to be implemented accurately. Otherwise, the change 
detection procedure would signal a need for map updating for buildings that are not to be mapped. 
1. INTRODUCTION 
To satisfy the demands for more frequent updates of 
topographic databases, mapping agencies are looking into the 
possibilities to partially automate their production processes. 
Automated mapping still seems to be far out of reach. 
However, new technologies like laser scanning can help to 
speed up the production process. When revising a 
topographical database, much time is currently spent on 
checking whether the information is still up to date. 
Significant costs savings can be obtained if one would be 
able to automatically flag the objects in the database that 
need to be updated. In this way an operator would no longer 
have to look at map areas where no changes took place. This 
paper reports about studies on the usage of laser scanning 
data for automated change detection of buildings for the 
purpose of updating a medium scale map (1:10.000 scale). 
In general, change detection can be performed on multi- 
epoch data or by comparing data of a single epoch to a map. 
Surface model differences generated from multi-epoch data 
of laser scanners immediately show newly constructed or 
demolished buildings and roads (Murakami et al. 1998, 
1999). In most cases, such data will, however, not be 
available. Alternatively, one can compare object extracted 
from laser data of a single epoch to the objects of a map. For 
this purpose one first needs to segment the laser data and 
classify the segments. This approach is followed in this 
paper. 
In Section 2 related literature on the classification of laser 
scanning data and the usage of laser scanning data for change 
detection is briefly reviewed. Section 3 discusses the 
segmentation and classification of laser scanner point clouds 
into bare Earth, building, and vegetation segments. Results of 
this classification are presented in section 4. The segments 
classified as building segment are compared to the building 
objects of a topographical database. The purpose of this 
comparison is to detect buildings that are new, changed in 
size or shape, or demolished. For this step to be successful, it 
is important to implement the same object selection rules as 
described in the mapping catalogue used for the production 
of the topographic database. Differences caused by 
generalisation of the building shapes in the database also 
need to be accounted for. The developed procedure for 
change detection is described in Section 5. The results are 
discussed in Section 6. 
2. RELATED LITERATURE 
The classification of laser point clouds into points on the bare 
Earth surface and other points is of large importance for the 
production of digital elevation models with laser scanning. 
Many studies have been devoted to this subject. Sithole and 
Vosselman (2004) provide an overview on these filter 
algorithms together with an experimental comparison. 
For the purpose of change detection it is required to further 
classify the points that do not belong to the bare Earth 
surface. Maas (1999) and Oude Elberink and Maas (2000) 
extract texture measures from height co-occurrence matrix. 
These texture measures, together with differences between 
first and last pulse laser data and the heights of a normalised 
digital surface model are used as the input for an 
unsupervised K-means classification. Depending on the 
number of object classes to be distinguished, 90% to 97% 
correct classifications were obtained. 
Matikainen et al. (2001, 2003) use a bottom up region 
merging algorithm to create segments. For these segments 
attributes like texture measures from a co-occurrence matrix,