LSD4WSD : An Open Dataset for Wet Snow Detection with SAR Data and Physical Labelling

LSD4WSD: Learning SAR Dataset for Wet Snow Detection.

The aim of this dataset is to provide a basis for automatic learning to detect wet snow.
It is based on Sentinel-1 SAR satellite images acquired between August 2020 and August 2021 over the French Alps. It consists of 487157 samples of size 16 by 16 by 9 for training and 3668 for testing. For each sample, the associated label is obtained using the Crocus physical model.

The 9 channels are in the following order:

• Sentinel-1 polarimetric channels: VV, VH and the combination C: VV/VH,
• Topographical features: altitude, orientation, slope
• Polarimetric ratio with a reference summer image: VV/VVref, VH/VHref, C/Cref

The utils.py file provides the function for opening the hdf5 file and displays the dataset characteristics.

Below is the associated command line:

python3 utils.py --dirpath ./

The complete processing chain will be added in future versions at the following Github address.

The authors would like to acknowledge the support from the National Centre for Space Studies (CNES) in providing computing facilities and access to SAR images via the PEPS platform.

The authors would like to deeply thank Mathieu Fructus for running the Crocus simulations