LSD4WSD : An Open Dataset for Wet Snow Detection with SAR Data and Physical Labelling
- Citation Author(s):
-
Matthieu Gallet
(LISTIC, University Savoie Mont Blanc)
Abdourrahmane Atto
(LISTIC, University Savoie Mont Blanc)
Fatima Karbou
(CEN, Centre National de Recherches Météorologiques)
Emmanuel Trouvé
(LISTIC, University Savoie Mont Blanc)
- Submitted by:
- matthieu gallet
- Last updated:
- DOI:
- 10.21227/b7eb-pj47
- Data Format:
- Research Article Link:
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Abstract
LSD4WSD: Learning SAR Dataset for Wet Snow Detection.
The dataset can be found at : https://zenodo.org/record/8111485
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
Instructions:
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.
