  This master thesis addresses the subject of automatically generating a
dataset for image recognition, which takes a lot of time when being done
manually. As the thesis was written with motivation from the context of the
biodiversity workgroup at the City University of Applied Sciences Bremen, the
classification of taxonomic entries was chosen as an exemplary use case. In
order to automate the dataset creation, a prototype was conceptualized and
implemented after working out knowledge basics and analyzing requirements for
it. It makes use of an pre-trained abstract artificial intelligence which is
able to sort out images that do not contain the desired content. Subsequent to
the implementation and the automated dataset creation resulting from it, an
evaluation was performed. Other, manually collected datasets were compared to
the one the prototype produced in means of specifications and accuracy. The
results were more than satisfactory and showed that automatically generating a
dataset for image recognition is not only possible, but also might be a decent
alternative to spending time and money in doing this task manually. At the very
end of this work, an idea of how to use the principle of employing abstract
artificial intelligences for step-by-step classification of deeper taxonomic
layers in a productive system is presented and discussed.
