  Radio maps quantify magnitudes such as the received signal strength at every
location of a geographical region. Although the estimation of radio maps has
attracted widespread interest, the vast majority of works rely on simulated
data and, therefore, cannot establish the effectiveness and relative
performance of existing algorithms in practice. To fill this gap, this paper
presents the first comprehensive and rigorous study of radio map estimation
(RME) in the real world. The main features of the RME problem are analyzed and
the capabilities of existing estimators are compared using large measurement
datasets collected in this work. By studying four performance metrics, recent
theoretical findings are empirically corroborated and a large number of
conclusions are drawn. Remarkably, the estimation error is seen to be
reasonably small even with few measurements, which establishes the viability of
RME in practice. Besides, from extensive comparisons, it is concluded that
estimators based on deep neural networks necessitate large volumes of training
data to exhibit a significant advantage over more traditional methods.
Combining both types of schemes is seen to result in a novel estimator that
features the best performance in most situations. The acquired datasets are
made publicly available to enable further studies.
