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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+demo/dem_SA_SA5120_E1223N0237.tif filter=lfs diff=lfs merge=lfs -text
+demo/demo_p509.tif filter=lfs diff=lfs merge=lfs -text
+test/test_2000_high.mlstac filter=lfs diff=lfs merge=lfs -text
+test/test_509_high.mlstac filter=lfs diff=lfs merge=lfs -text
+test/test_509_scribble.mlstac filter=lfs diff=lfs merge=lfs -text

README.md

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----
-license: cc0-1.0
----
+license: cc0-1.0
+task_categories:
+- image-segmentation
+language:
+- en
+tags:
+  - earth-observation
+  - remote-sensing
+  - sentinel-2
+  - multi-spectral
+  - satellite
+  - geospatial
+pretty_name: cloudsen12
+size_categories:
+  - 100K<n<1M
+---
+
+
+<img src="cloudsen12.gif" alt="drawing" width="20%"/>
+
+
+**CloudSEN12+** is a significant extension of the [CloudSEN12](https://cloudsen12.github.io/) dataset, which doubles the number of expert-reviewed labels, making it, by 
+a large margin, the largest cloud detection dataset to date for Sentinel-2. All labels from the previous version have been curated and refined, enhancing the 
+dataset's trustworthiness. This new release is licensed **under CC0**, which puts it in the public domain and allows anyone to use, modify, and distribute 
+it without permission or attribution.
+
+## Data Folder order
+
+
+The CloudSEN12+ dataset is organized into `train`, `val`, and `test` splits. The images have
+been padded from 509x509 to 512x512 and 2000x2000 to 2048x2048 to ensure that the patches are divisible by 32. The padding is filled with zeros in the left and bottom sides of the image. For
+those who prefer traditional storage formats, GeoTIFF files are available in our [ScienceDataBank](https://www.scidb.cn/en/detail?dataSetId=2036f4657b094edfbb099053d6024b08&version=V1) repository.
+
+<center>
+<img src="https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/9UA4U3WObVeq7BAcf37-C.png" alt="drawing" width="50%"/>
+</center>
+
+*CloudSEN12+ spatial coverage. The terms p509 and p2000 denote the patch size 509 × 509 and 2000 × 2000, respectively. ‘high’, ‘scribble’, and ‘nolabel’ refer to the types of expert-labeled annotations*
+
+## Data Structure
+
+We use `.mlstac` format to store the data in HugginFace and GeoTIFF for ScienceDataBank.
+
+
+Index  | Name | Scale  | Wavelength                      | Description              |
+|------|------|--------|---------------------------------|--------------------------|
+|  0  | B1   | 0.0001 | 443.9 nm (S2A)/442.3 nm (S2B)   | Aerosols.                |
+|  1  | B2   | 0.0001 | 496.6 nm (S2A)/492.1 nm (S2B)   | Blue.                    |
+|  2  | B3   | 0.0001 | 560 nm (S2A)/559 nm (S2B)       | Green.                   |
+|  3  | B4   | 0.0001 | 664.5 nm (S2A)/665 nm (S2B)     | Red.                     |
+|  4  | B5   | 0.0001 | 703.9 nm (S2A)/703.8 nm (S2B)   | Red Edge 1.              |
+|  5  | B6   | 0.0001 | 740.2 nm (S2A)/739.1 nm (S2B)   | Red Edge 2.              |
+|  6  | B7   | 0.0001 | 782.5 nm (S2A)/779.7 nm (S2B)   | Red Edge 3.              |
+|  7  | B8   | 0.0001 | 835.1 nm (S2A)/833 nm (S2B)     | NIR.                     |
+|  8  | B8A  | 0.0001 | 864.8 nm (S2A)/864 nm (S2B)     | Red Edge 4.              |
+|  9  | B9   | 0.0001 | 945 nm (S2A)/943.2 nm (S2B)     | Water vapor.             |
+| 10  | B10  | 0.0001 | 1373.5 nm (S2A)/1376.9 nm (S2B) | Cirrus.                  |
+| 11  | B11  | 0.0001 | 1613.7 nm (S2A)/1610.4 nm (S2B) | SWIR 1.                  |
+| 12  | B12  | 0.0001 | 2202.4 nm (S2A)/2185.7 nm (S2B) | SWIR 2.                  |
+| 13  | CM1  | 1      | -                               | Expert-labeled image.    |
+| 14  | CM2  | 1      | -                               | UnetMobV2-labeled image. |
+
+
+## Folder Structure
+
+
+The **fixed/** folder contains high and scribble labels, which have been improved in this new version. These changes have already been integrated.
+
+The **demo/** folder contains examples illustrating how to utilize the models trained with CLoudSEN12 to estimate the hardness and trustworthiness indices.
+
+The **images/** folder contains the CloudSEN12+ imagery
+
+## Download
+
+
+## Citation
+
+Cooming soon!

demo/demo.py

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+import segmentation_models_pytorch as smp
+import sklearn.metrics
+import torch
+import timm
+
+# -- Replace with your data --
+x = torch.randn(1, 13, 512, 512) # S2 L1C image
+y = torch.randint(0, 4, (1, 512, 512)).numpy() # Target
+
+
+# -- Load the segmentation model - UNetMobV2 --
+segmodel = smp.Unet(
+    encoder_name="mobilenet_v2",
+    encoder_weights=None,
+    in_channels=13,
+    classes=4
+)
+segmodel.load_state_dict(torch.load("models/UNetMobV2.pt"))
+segmodel.eval()
+
+
+# -- Predict the cloud mask --
+with torch.no_grad():    
+    yhat = segmodel(x)
+    cloudmask = torch.argmax(yhat, dim=1).cpu().numpy().squeeze()
+
+
+# -- Predict the trustworthiness index (TI) --
+ti_index = sklearn.metrics.fbeta_score(
+    y_true=y.flatten(),
+    y_pred=cloudmask.flatten(),
+    beta=2.0,
+    average="macro"
+)
+
+
+# -- Load the hardness index (HI) model --
+hi_model = timm.create_model(
+    model_name="resnet10t",
+    pretrained=True,
+    num_classes=1,
+    in_chans=13
+)
+hi_model.load_state_dict(torch.load("models/resnet10.pt"))
+hi_model.eval()
+
+
+# -- Estimate the hardness index (HI) --
+with torch.no_grad():
+    y = hi_model(x)
+    hi_index = torch.sigmoid(y).cpu().numpy().squeeze().item()
+
+
+# -- Decision making --
+if (ti_index < 0.3) & (hi_index > 0.5):
+    perror = 1
+else:
+    perror = 0

demo/models/L2A_EfficientNetUnet.pt

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