International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
clustering technique. It presents an advantage in the sense that
there is no prior knowledge is needed for such learning
processes, i.e. data samples group themselves in terms of
similarity. We develop an interactive environment integrated a
SOM view, 2D and 3D views of the dataset, thus it facilities
detections of clusters associated with different spatial objects.
Despite the preliminary nature of the case study, it does
illustrate the powerfulness of unsupervised methods in general
and SOM in particular in extracting spatial objects from a laser-
scanning dataset. It is important to note that SOM training
process is much dependent on the parameter settings as reported
in table 1. This issue deserves further research, in particular in
terms of how the parameter settings have impact on the
extraction of spatial objects from a point cloud.
ACKNOWLEDGEMENTS
The author would like to thank Mikael Óstlund from the GIS
institute at the University of Gávle who provides the datasets
for the case study.
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