  The ultraviolet (UV) to sub-millimetre (submm) spectral energy distribution
of galaxies can be roughly divided into two sections: the stellar emission
(attenuated by dust) at UV to near-infrared wavelengths and dust emission at
longer wavelengths. In Dobbels et al. (2020), we show that these two sections
are strongly related, and we can predict the global dust properties from the
integrated UV to mid-infrared emission with the help of machine learning
techniques. We investigate if these machine learning techniques can also be
extended to resolved scales. Our aim is to predict resolved maps of the
specific dust luminosity, specific dust mass, and dust temperature starting
from a set of surface brightness images from UV to mid-infrared wavelengths. We
used a selection of nearby galaxies retrieved from the DustPedia sample, in
addition to M31 and M33. These were convolved and resampled to a range of pixel
sizes, ranging from 150 pc to 3 kpc. We trained a random forest model which
considers each pixel individually. We find that the predictions work well on
resolved scales, with the dust mass and temperature having a similar root mean
square error as on global scales (0.32 dex and 3.15 K on 18" scales
respectively), and the dust luminosity being noticeably better (0.11 dex). We
find no significant dependence on the pixel scale. Predictions on individual
galaxies can be biased, and we find that about two-thirds of the scatter can be
attributed to scatter between galaxies (rather than within galaxies). A machine
learning approach can be used to create dust maps, with its resolution being
only limited to the input bands, thus achieving a higher resolution than
Herschel. These dust maps can be used to improve global estimates of dust
properties, they can lead to a better estimate of dust attenuation, and they
can be used as a constraint on cosmological simulations that trace dust.
