  Many phenomena taking place in the solar photosphere are controlled by plasma
motions. Although the line-of-sight component of the velocity can be estimated
using the Doppler effect, we do not have direct spectroscopic access to the
components that are perpendicular to the line-of-sight. These components are
typically estimated using methods based on local correlation tracking. We have
designed DeepVel, an end-to-end deep neural network that produces an estimation
of the velocity at every single pixel and at every time step and at three
different heights in the atmosphere from just two consecutive continuum images.
We confront DeepVel with local correlation tracking, pointing out that they
give very similar results in the time- and spatially-averaged cases. We use the
network to study the evolution in height of the horizontal velocity field in
fragmenting granules, supporting the buoyancy-braking mechanism for the
formation of integranular lanes in these granules. We also show that DeepVel
can capture very small vortices, so that we can potentially expand the scaling
cascade of vortices to very small sizes and durations.
