  New advancements in radio data post-processing are underway within the SKA
precursor community, aiming to facilitate the extraction of scientific results
from survey images through a semi-automated approach. Several of these
developments leverage deep learning (DL) methodologies for diverse tasks,
including source detection, object or morphology classification, and anomaly
detection. Despite substantial progress, the full potential of these methods
often remains untapped due to challenges associated with training large
supervised models, particularly in the presence of small and class-unbalanced
labelled datasets. Self-supervised learning has recently established itself as
a powerful methodology to deal with some of the aforementioned challenges, by
directly learning a lower-dimensional representation from large samples of
unlabelled data. The resulting model and data representation can then be used
for data inspection and various downstream tasks if a small subset of labelled
data is available. In this work, we explored contrastive learning methods to
learn suitable radio data representation from unlabelled images taken from the
ASKAP EMU and SARAO MeerKAT GPS surveys. We evaluated trained models and the
obtained data representation over smaller labelled datasets, also taken from
different radio surveys, in selected analysis tasks: source detection and
classification, and search for objects with peculiar morphology. For all
explored downstream tasks, we reported and discussed the benefits brought by
self-supervised foundational models built on radio data.
