Global Supraglacial Debris Dataset (GSDD)

We present the Global Supraglacial Debris Dataset (GSDD), developed to support the training, validation, and testing of deep learning models for supraglacial debris mapping.

Baseline results using standard CNNs and a geo-foundational model are reported in our article:
“Improving supraglacial debris mapping using newly annotated multisource remote sensing data and a geo-foundational model” by Kaushik Saurabh¹, Maurya Lalit, Tellman Elizabeth, and Zhang Guoqing.

The dataset comprises Sentinel-2 spectral bands (Blue, Green, Red, NIR, SWIR1, SWIR2), a derived Normalized Difference Debris Index (NDDI), topographic layers (slope and elevation), and glacier velocity data. These layers help distinguish supraglacial debris (on-glacier) from proglacial debris (off-glacier), with minimal manual annotation effort.

GSDD was systematically generated to assist the machine learning and geoscience communities in improving glacier mapping workflows and evaluating the applicability of newly proposed geo-foundational models for downstream cryosphere-related tasks—an area that remains underexplored.

Please cite:
Kaushik Saurabh¹, Maurya Lalit, Tellman Elizabeth, Zhang Guoqing. “Improving supraglacial debris mapping using newly annotated multisource remote sensing data and a geo-foundational model.”