  Sensing capability is one of the most highlighted new feature of future 6G
wireless networks. This paper addresses the sensing potential of Large
Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the
attention received by LIS in terms of communication aspects, it can offer a
high-resolution rendering of the propagation environment. This is because, in
an indoor setting, it can be placed in proximity to the sensed phenomena, while
the high resolution is offered by densely spaced tiny antennas deployed over a
large area. By treating an LIS as a radio image of the environment relying on
the received signal power, we develop techniques to sense the environment, by
leveraging the tools of image processing and machine learning. Once a
holographic image is obtained, a Denoising Autoencoder (DAE) network can be
used for constructing a super-resolution image leading to sensing advantages
not available in traditional sensing systems. Also, we derive a statistical
test based on the Generalized Likelihood Ratio (GLRT) as a benchmark for the
machine learning solution. We test these methods for a scenario where we need
to detect whether an industrial robot deviates from a predefined route. The
results show that the LIS-based sensing offers high precision and has a high
application potential in indoor industrial environments.
