Automated Analysis of Snowpack Stratigraphy NIR Images Using Deep Learning
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".