Digital Image Similarity
for Geo-spatial Knowledge Management

James D. Carswell1, David C. Wilson2, and Michela Bertolotto2

1 Digital Media Centre, Dublin Institute of Technology, Dublin 2, Ireland
jcarswell@dit.ie
2 Smart Media Institute, Department of Computer Science,
University College Dublin, Belfield, Dublin 4, Ireland
{david.wilson,michela.bertolotto}@ucd.ie


Abstract. The amount and availability of high-quality geo-spatial image 
data, such as digital satellite and aerial photographs, is increasing
dramatically. Task-based management of such visual information and
associated knowledge is a central concern for organisations that rely on
digital imagery. We are developing geo-spatial knowledge management
techniques that employ case-based reasoning as the core methodology. In
order to provide effective retrieval of task-based experiences that center
around geo-spatial imagery, we need to forward novel similarity metrics
for directly comparing the image components of experience cases. Based
on work in geo-spatial image database retrieval, we are building an effective 
similarity metric for geo-spatial imagery that makes comparisons
based on derived image features, their shapes, and the spatial relations
between them. This paper gives an overview of the geo-spatial knowledge
management context, describes our image similarity metric, and provides
an initial evaluation of the work.
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