Deep Learning for Super-resolution of Sea Ice Concentration: Case Study of OSISAF Downscaler

The trained model (unet.pth) is located in the root directory.

Installation

1. Clone the repository:

git clone <repository_url>
cd <repository_name>

2. Create a virtual environment and sync dependencies:

uv sync
source .venv/bin/activate

Usage

Ensure your data is placed in ./data/OSISAF and ./data/MASAM2 or update src/config.py.

Training the Model

To train the U-Net model from scratch:

uv run python src/unet_train.py

Visualizing Results

To generate comparison plots between Low Res, High Res, and Prediction:

uv run python src/visualize_pairs.py

Benchmarking

To evaluate classical interpolation baselines:

uv run python src/interpolation_methods.py