Glacial-Lake-Bench: A Global Multi-Sensor Benchmark Dataset for Evaluating Deep Learning Models for Glacial Lake Mapping

Abstract. Glacial lakes are among the most sensitive indicators of climate change, closely linked to natural hazards and are natural reservoirs of freshwater resources. Thus, automated mapping and monitoring of glacial lakes is imperative. However, most automated approaches either remain regional in scope or show limited performance under challenging conditions such as cloud cover, shadows, and spatially small or frozen lakes. The global scale analysis and comparative evaluation of deep learning models is primarily hindered by the lack of readily available datasets for training data. To address this gap, we present Glacial Lake-Bench (GLB), a multisource remote sensing dataset comprising Sentinel-2, Sentinel-1, and Copernicus DEM-derived terrain (11 channels in total). GLB consists of 19,115 image-label pairs (256x256x11) spanning all Randolph Glacier Inventory (RGI) regions except Antarctica, providing the first-ever global, multi-sensor dataset for glacial lake segmentation. In addition, we compiled Glacial Lake-Bench-Challenge (GLBC), a curated subset of 1,105 image-label pairs representing scenes with cloud cover, shadow, frozen lake surfaces, and small lakes to establish a community standard for evaluating model robustness under difficult conditions. Labels are derived from Zhang et al. (2024); we independently quantify label quality through stratified sampling of 50 image-label pairs from each RGI region, amounting to 900 chips in total. Our quality assessment reveals a mean Intersection over Union (mIoU) of 0.95, precision of 0.99, recall of 0.96, and per-region agreement that is consistent with the known difficulty of small, turbid, shadowed lakes in high-mountain terrain. To demonstrate that the dataset is usable, well-posed, and appropriately challenging, we provide reference baselines from two convolutional networks (U-Net, DeepLabv3+) and two Geo-Foundation Models (GFMs) (DOFA, Prithvi-EO-2.0), evaluated with a recommended leave-one-region-out (LORO) protocol that minimizes spatial autocorrelation, alongside a random split and the GLBC subset. Baseline mIoU reaches 0.80–0.85 on GLB and drops to 0.74–0.79 on GLBC, confirming that the challenge subset isolates genuinely difficult conditions. The GLB dataset is available at https://zenodo.org/records/17917359 (Kaushik, 2026)