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(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.