Published July 11, 2023
| Version 1.0
Model
Open
METAR cloud cover over weather stations in Sentinel-2 satellite images - Machine Learning models
Authors/Creators
Description
This upload contains the Machine Learning models trained on Sentinel-2 satellite images of level L1C, of the project MSCC (also known as AIMLSSE) for the ordinal classification of cloud cover in the METAR format as commonly used by weather stations.
The dataset for these models can be found here: https://doi.org/10.5281/zenodo.14691474
The model itself is a Convolutional Neural Network (CNN), built on the EfficientNetV2 Model and adapted to the new task using Transfer Learning.
There are two models provided:
| efficientnet_v2_s_final_16km_300_ndsi_ord_regr_automatic_labels.pt | Model trained on the automatically gathered observation data from available weather stations in the METAR cloud cover format. |
| efficientnet_v2_s_final_16km_300_ndsi_ord_regr_manual_labels.pt | Model trained on manually labeled images in the METAR cloud cover format. |
Files
Files
(163.8 MB)
| Name | Size | Download all |
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md5:0cda54060439d9358918a802d0eace91
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81.9 MB | Download |
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md5:7bd96cff4fa1c8ef60e04a1a3c940cb2
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81.9 MB | Download |
Additional details
Related works
- Is part of
- Thesis: 10.5445/IR/1000161948 (DOI)
Dates
- Submitted
-
2023-07-11
Software
- Repository URL
- https://github.com/ErikWessel/MSCC
- Programming language
- Python
- Development Status
- Inactive