  In Spectrum cartography (SC), the generation of exposure maps for radio
frequency electromagnetic fields (RF-EMF) spans dimensions of frequency, space,
and time, which relies on a sparse collection of sensor data, posing a
challenging ill-posed inverse problem. Cartography methods based on models
integrate designed priors, such as sparsity and low-rank structures, to refine
the solution of this inverse problem. In our previous work, EMF exposure map
reconstruction was achieved by Generative Adversarial Networks (GANs) where
physical laws or structural constraints were employed as a prior, but they
require a large amount of labeled data or simulated full maps for training to
produce efficient results. In this paper, we present a method to reconstruct
EMF exposure maps using only the generator network in GANs which does not
require explicit training, thus overcoming the limitations of GANs, such as
using reference full exposure maps. This approach uses a prior from sensor data
as Local Image Prior (LIP) captured by deep convolutional generative networks
independent of learning the network parameters from images in an urban
environment. Experimental results show that, even when only sparse sensor data
are available, our method can produce accurate estimates.
