Exploring Geo Foundation Models for Glacier Mapping Using Remote Sensing Data

Geo foundation models (GFMs) have recently emerged as a new paradigm in Earth observation (EO). They provide a promising approach for enhancing remote sensing analysis. GFMs enable faster and more generalised applications. They are deep learning models trained on large unlabelled datasets to learn general spatial, spectral, and contextual representations of the Earth’s surface. The datasets used are usually diverse in location, seasons, and even sensors. This diversity ensures that the model learns features that are as general as possible. This is vital because labelled data in remote sensing are limited, while high-quality unlabelled data are widely accessible. As a result, GFMs are increasingly viewed as a promising tool for scalable and robust environmental monitoring.

Among various EO tasks, glacier mapping is particularly relevant in the context of GFMs. Glaciers are located in hardly accessible regions, which makes ground-truth (GT) preparation difficult. Delineation of glaciers is often affected by seasonal snow and regional variability. Moreover, debris-covered and rock glaciers are harder to detect due to their complex landforms and their similarity to surrounding terrain. Accurate glacier delineation is crucial for monitoring cryospheric changes, assessing climate change impacts, managing water resources, and mitigating natural hazards.

In this study, we explore the applicability of GFMs for glacier mapping using multispectral Sentinel-2 imagery. We apply fine-tuning of pre-trained GFMs for glacier delineation, with the aim of assessing their potential in comparison with traditional deep learning approaches.