Automated Analysis of Snowpack Stratigraphy NIR Images Using Deep Learning

Snowpacks are important elements of the Earth’s cryosphere and are composed of layers with unique physical properties. Snow stratigraphy, the study of distinct snow layers and their properties, provides essential data for climate modeling, water resource management, and avalanche prediction. However, existing methods for characterizing snowpacks with near-infrared (NIR) photography are based on manually segmenting layers from images, which is a laborious and time-consuming task. In this work, we develop an approach to automate snowpack layer segmentation based on fine-tuning Segment Anything (SAM), a state-of-the-art deep learning segmentation model. We use a small set of expert-labeled NIR snowpack images and explore different task representations. We approach the problem through the lens of 1) edge detection, which focuses on detecting snowpack layer boundaries and 2) region detection, which focuses on predicting the area occupied by the layers.  Our results indicate that deep learning segmentation is promising for automating the segmentation of snowpacks. This ultimately leads to facilitating snow stratigraphy analysis to improve applications such as avalanche forecasting and snowpack modeling.