  Aims. In astronomy, machine learning has been successful in various tasks
such as source localisation, classification, anomaly detection, and
segmentation. However, feature regression remains an area with room for
improvement. We aim to design a network that can accurately estimate sources'
features and their uncertainties from single-band image cutouts, given the
approximated locations of the sources provided by the previously developed code
AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a
proof of concept, showing the potential of machine learning in estimating
astronomical features when trained on meticulously crafted synthetic images and
subsequently applied to real astronomical data. Methods. The algorithm
presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band
cutouts of 32x32 pixels around the localised sources to estimate flux,
sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a
two-step mean variance estimation (TS-MVE) approach to first estimate the
features and then their uncertainties without the need for additional
information, for example the point spread function (PSF). For this proof of
concept, we generated a synthetic dataset comprising only point sources
directly derived from real images, ensuring a controlled yet authentic testing
environment. Results.We show that ASID-FE, trained on synthetic images derived
from the MeerLICHT telescope, can predict more accurate features with respect
to similar codes such as SourceExtractor and that the two-step method can
estimate well-calibrated uncertainties that are better behaved compared to
similar methods that use deep ensembles of simple MVE networks. Finally, we
evaluate the model on real images from the MeerLICHT telescope and the Zwicky
Transient Facility (ZTF) to test its transfer learning abilities.
