  (abridged) Star formation in the outer Galaxy, i.e., outside of the Solar
circle, has been lightly studied in part due to low CO brightness of molecular
clouds linked with the negative metallicity gradient. Recent infrared surveys
provide an overview of dust emission in large sections of the Galaxy, but
suffer from cloud confusion and poor spatial resolution at far-infrared
wavelengths. We aim to develop a methodology to identify and classify young
stellar objects (YSOs) in star-forming regions in the outer Galaxy, and use it
to solve a long-standing confusion with the distance and evolutionary status of
IRAS 22147+5948. We use Support Vector Machine learning algorithm to complement
standard color-color and color-magnitude diagrams in search for YSOs in the
IRAS 22147 region using publicly available data from the `Spitzer Mapping of
the Outer Galaxy' survey. The agglomerative hierarchical clustering algorithm
is used to identify clusters, along with the Robitaille et al. (2017) code to
calculate physical properties of individual YSOs. We identify 13 Class I and 13
Class II YSO candidates using the color-color diagrams, and additional 2 and 21
sources, respectively, using the machine learning techniques. Spectral energy
distributions of 23 sources are modelled with a star and a passive disk,
corresponding to Class II objects. Models of 3 sources include envelopes
typical for Class I objects. The objects are grouped in 2 clusters located at
the distance of ~2.2 kpc, and 5 clusters at ~5.6 kpc. The spatial extent of CO,
radio continuum, and dust emission confirms the origin of YSOs in two distinct
star-forming regions along a similar line-of-sight. The outer Galaxy might
serve as a unique laboratory of star formation across environments on condition
that complementary methods and ancillary data are used to properly account for
cloud confusion and distance uncertainties.
