Continental-scale land cover information is essential to furthering our understanding of the terrestrial environment, atmosphere and climate change. Several global land cover products have been released in recent years but they typically do not include Antarctica. The lack of land cover data in Antarctica is concerning because mountain glaciers and icecaps there have been losing mass at a rate well above the global average, leading to expansion of proglacial regions. Proglacial regions comprise transient land cover types with high rates of geomorphological activity that delivers sediment into the Southern Ocean and supports its rich biodiversity. With Antarctic mountain glaciers and icecaps projected to lose more mass in the coming decades, and with permafrost projected to degrade too, it is timely to establish a baseline land cover dataset for Antarctica with which future classifications can be compared. Here, we present the code used to classify Landsat-8 Operational Land Imager (OLI) images in six proglacial regions of Antarctica at 30 m resolution, with an overall accuracy of 77.0 % for proglacial land classes. The code to the accuracy assessment is also available. We conducted this classification using an unsupervised K-means clustering approach, which circumvented the need for training data and was highly effective at picking up key land classes, such as vegetation, water, and different sedimentary surfaces.