  Global Navigation Satellite Systems typically perform poorly in urban
environments, where the likelihood of line-of-sight conditions between devices
and satellites is low. Therefore, alternative location methods are required to
achieve good accuracy. We present LocUNet: A convolutional, end-to-end trained
neural network (NN) for the localization task, which is able to estimate the
position of a user from the received signal strength (RSS) of a small number of
Base Stations (BS). Using estimations of pathloss radio maps of the BSs and the
RSS measurements of the users to be localized, LocUNet can localize users with
state-of-the-art accuracy and enjoys high robustness to inaccuracies in the
estimations of radio maps. The proposed method does not require generating RSS
fingerprints of each specific area where the localization task is performed and
is suitable for real-time applications. Moreover, two novel datasets that allow
for numerical evaluations of RSS and ToA methods in realistic urban
environments are presented and made publicly available for the research
community. By using these datasets, we also provide a fair comparison of
state-of-the-art RSS and ToA-based methods in the dense urban scenario and show
numerically that LocUNet outperforms all the compared methods.
