  Reconfigurable Intelligent Surfaces (RISs) promise improved, secure and more
efficient wireless communications. We propose and demonstrate how to exploit
the diversity offered by RISs to generate and select easily differentiable
radio maps for use in wireless fingerprinting localization applications.
Further, we apply machine learning feature selection methods to prune the large
state space of the RIS, thus reducing complexity and enhancing localization
accuracy and position acquisition time. We evaluate our proposed approach by
generation of radio maps with a novel radio propagation modelling and
simulations.
