  In this article, we present a collection of radio map datasets in dense urban
setting, which we generated and made publicly available. The datasets include
simulated pathloss/received signal strength (RSS) and time of arrival (ToA)
radio maps over a large collection of realistic dense urban setting in real
city maps. The two main applications of the presented dataset are 1) learning
methods that predict the pathloss from input city maps (namely, deep
learning-based simulations), and, 2) wireless localization. The fact that the
RSS and ToA maps are computed by the same simulations over the same city maps
allows for a fair comparison of the RSS and ToA-based localization methods.
