Abstract: | This thesis describes a classifier of objects on a real world cadastre map within the relational
machine learning framework.
The first part of the work aims to introduce and compare relational probabilistic reasoners
that allows us to create a model over the data that would be interpretable and corresponding
to the human description of the objects on the map.
For the implementation part it is necessary to execute some basics image processing to obtain
some probabilistic facts and allow us to create a model of the data.
There are several state of the art classifier allowing to classify object recognized by computer
vision. We tried to create one that takes into account reasoning the relations between the
object than rather handling them as independent.
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