Central West Greenland Supraglacial Lake Drainage Classification Dataset (2018-2019)

Englacial drainage of supraglacial lakes on the Greenland Ice Sheet serve as a full thickness surface-to-bed meltwater delivery system. Downstream impacts of these meltwater transport events on ice stability are not well understood, and there does not yet exist comprehensive identification of these events, which limits inference and stability modeling. To close this gap, we have compiled this dataset of lake drainage events, comprising labeled machine learning-ready imagery stacks for supraglacial lakes from the 2018 and 2019 melt seasons in central-west Greenland. Each lake is labeled by its drainage mechanism into one of five classes: no drainage (ND), hydrofracture (HF), moulin drainage (MD), lateral drainage (LD), or crevasse drainage (CD). Time series satellite (Copernicus Sentinel-2) imagery stacks for each lake, along with their drainage mechanism labels, are contained in the per-lake NetCDF files. This dataset builds upon the foundational work of Dunmire et al., 2025 to enable deep learning computer vision model training for classification of lake drainages based on transient physical spatial features. The dataset has an accompanying open source codebase: https://github.com/jharlanr/sat-tile-stack.