Knowing the internal spatial distribution of cold and temperate ice within polythermal glaciers is essential for understanding and modelling their dynamics. The boundary between both types of ice, termed as cold-temperate transition surface (CTS), is usually determined from ground-penetrating radar (GPR) data, given the permittivity contrast between cold ice and water-rich temperate ice. This task is traditionally manual and time-consuming. We here present an approach based on deep learning for the automatic classification of cold ice, temperate ice and bedrock. We used deep learning algorithms based on convolutional neural networks (CNN), following a feature pyramid network architecture. The training data were collected in Svalbard, using a GPR with central frequency of 20–25 MHz, along various campaigns spanning the period 2008–2022, carried out on glaciers in Sabine Land, Nordenskiöld Land, Wedel Jarlsberg Land and Nordaustlandet. To address data scarcity, we evaluated two dataset expansion strategies: generating synthetic radargrams by forward modelling of electromagnetic wave propagation, and a data augmentation scheme in which each GPR profile is split into 200 m segments treated as independent samples. Starting from a baseline trained on the original GPR dataset, these strategies were applied individually and in combination, defining four experiments. Our results show in general satisfactory values of the performance metrics. For instance, the intersection over union (IoU) metric reaches values of 64.2 ± 2.7 %, 77.0 ± 2.4 % and 98.1 ± 0.2 % for the classes cold ice, temperate ice and bedrock, respectively.