  The automated identification of extragalactic objects in large surveys
provides reliable and reproducible samples of galaxies in less time than
procedures involving human interaction. However, regions near the Galactic disc
are more challenging due to the dust extinction. We present the methodology for
the automatic classification of galaxies and non-galaxies at low Galactic
latitude regions using both images and, photometric and morphological near-IR
data from the VVVX survey. Using the VVV-NIRGC, we analyse by statistical
methods the most relevant features for galaxy identification. This catalogue
was used to train a CNN with image data and an XGBoost model with both
photometric and morphological data and then to generate a dataset of
extragalactic candidates. This allows us to derive probability catalogues used
to analyse the completeness and purity as a function of the configuration
parameters and to explore the best combinations of the models. As a test case,
we apply this methodology to the Northern disc region of the VVVX survey,
obtaining 172,396 extragalatic candidates with probabilities of being galaxies.
We analyse the performance of our methodology in the VVV disc, reaching an
F1-score of 0.67, a 65 per cent purity and a 69 per cent completeness. We
present the VVV-NIR Galaxy Catalogue: Northern part of the Galactic disc
comprising 1,003 new galaxies, with probabilities greater than 0.6 for either
model, with visual inspection and with only 2 previously identified galaxies.
In the future, we intend to apply this methodology to other areas of the VVVX
survey.
