SnowCoverage

Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning

1. Dataset

1.1 The locations of 40 Sentinel-2 L2A scenes across the globe

1.2 Visualization of all 40 scenes via RGB bands

1.3 Labeled classification masks of all 40 collected scenes

2. Install

Python>=3.8.0 is required with all requirements.txt installed including PyTorch>=1.7:

$ git clone https://github.com/yiluyucheng/SnowCoverage
$ cd SnowCoverage
$ pip install -r requirements.txt

Download the model file via Google Drive: unet_4bands.pth

Replace the dummpy model file './models/unet_4bands.pth' with valid model file.

3. How to run:

Use the following code to make classifications:

$ python make_prediction.py ./test_data/20200804T223709_20200804T223712_T59GLM.tif save_output_image.pdf

Prediction result:

4. Citation

If you plan to use this dataset or feel this paper is useful for your publication, please cite the following publication to support the work:

Wang, Y.; Su, J.; Zhai, X.; Meng, F.; Liu, C. Snow Coverage Mapping by Learning from Sentinel-2 Satellite Multispectral Images via Machine Learning Algorithms. Remote Sens. 2022, 14, 782. https://doi.org/10.3390/rs14030782