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camilletyriard/
glacier-segmentation-attention-unet

Name: glacier-segmentation-attention-unet
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Attention U-Net — Glacier Extent Segmentation (Swiss Alps)

Trained model weights for binary glacier/non-glacier segmentation from Sentinel-2 multispectral imagery over the Jungfrau–Aletsch region, Switzerland.

Developed as part of a study transferring the deforestation detection architecture of John & Zhang (2022) to cryosphere monitoring.

Full code, notebooks, and documentation: https://github.com/camilletyriard-dev/glacier-segmentation-attention-unet

Model Files

File	Description	Input bands
`glacier_segmentation_6band.keras`	Glacier segmentation — main model	6 (RGB + NIR + SWIR1 + SWIR2)
`deforestation_amazon_4band.keras`	Replication — Amazon deforestation	4 (RGB + NIR)
`deforestation_atlantic_4band.keras`	Replication — Atlantic Forest	4 (RGB + NIR)
`deforestation_amazon_rgb_3band.keras`	Replication — Amazon RGB only	3 (RGB)

Performance

Glacier Segmentation (Swiss Alps — main contribution)

Metric	Score
IoU	0.9839
Precision	0.9919
Recall	0.9915
F1	0.9917

Test set: 101 images, Jungfrau–Aletsch region, summer 2020–2025.

Usage

python

from keras.models import load_model
import numpy as np

# Load model
model = load_model("glacier_segmentation_6band.keras")

# Input shape: (batch, 512, 512, 6) — normalised to [0, 1]
# Output shape: (batch, 512, 512, 1) — binary mask
prediction = model.predict(image[np.newaxis, ...])

Architecture

Attention-gated U-Net (Oktay et al., 2018) adapted from John & Zhang (2022).

Key modifications for glacier segmentation:

6-channel input (RGB + NIR + SWIR1 + SWIR2)
Bottleneck dropout (p = 0.5)
Dice loss (class imbalance: ice ≈ 12% of pixels)
Full fine-tuning of all 2.01M parameters

Citation

bibtex

@misc{tyriard2025glacier,
  author = {Tyriard, Camille},
  title  = {Attention U-Net for Glacier Extent Segmentation in the Swiss Alps},
  year   = {2025},
  url    = {https://github.com/camilletyriard-dev/glacier-segmentation-attention-unet}
}

Based on: