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The Antarctic Ice Sheet is the largest potential contributor to future global sea level, with a maximum contribution of approximately 58 m. The flow of grounded ice towards the ocean is largely restricted by floating ice shelves, which fringe ~75% of the Antarctic coastline. This buttressing effect can be diminished or lost entirely following partial or complete ice-shelf disintegration events, leading to an acceleration of ice discharge. The vulnerability of ice shelves to fracture and disintegration is influenced by a range of factors, including the ponding of surface and subsurface meltwater, which can induce flexural stresses and promote fracture through the ice-shelf column.While the widespread extent of surface meltwater systems across numerous Antarctic ice shelves during the austral summer is now well documented, meltwater storage within the ice-shelf subsurface remains poorly understood. Liquid water can persist perennially beneath the ice-shelf surface if sufficiently insulated by surrounding and overlying layers of firn, snow, and/or ice. Due to their year-round persistence, buried meltwater lakes introduce a potential mechanism for hydrofracture outside of the melt season, with important implications for ice-shelf stability.In situ surveys in Antarctica are logistically challenging and limited in spatial extent, rendering spaceborne remote sensing an indispensable tool for monitoring ice-shelf processes at a continental scale. However, the growing volume of satellite observation poses data-analysis challenges typical of the ‘Big Data’ era; remote-sensing datasets are often high-dimensional, unstructured, and large in sample size, with rapidly increasing spatiotemporal coverage and low-cost availability. These characteristics limit the scalability of manual interpretation or traditional thresholding approaches for pan-Antarctic applications. In contrast, machine-learning methods are ideal for extracting patterns and features from large databases of satellite imagery. In light of this, machine learning offers great potential for the detection of subsurface meltwater across Antarctic ice shelves at a continental scale, if challenges relating to the sampling and labelling of training data, as well as class imbalance, are addressed carefully. An active learning strategy can help to reduce data redundancy and labelling requirements in deep learning, while also improving model performance in the presence of class imbalance. Here, we present a systematic training-data sampling strategy applied to all major Antarctic ice shelves. We employ a stratified random sampling approach to mitigate strong regional imbalances in data availability and create a curated training data subset that combines an equal number of random and expert-selected samples. This dataset is used to initialise an active learning framework for training a deep-learning model to detect subsurface lakes in Antarctica. We evaluate model performance across multiple configurations and present fine-tuning of model hyperparameters.