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@@ -37,6 +37,20 @@ This enhancement significantly improves the dataset's spatial content, providing
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  ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6582b7dd75754a803e484487/sxmnCAGs0p2u_PALLsqyN.jpeg)
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  ## Credits
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  ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6582b7dd75754a803e484487/sxmnCAGs0p2u_PALLsqyN.jpeg)
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+ ## Data loading
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+ The Github repository associated to this dataset contains a PyTorch dataset class [here)(https://github.com/gastruc/OmniSat/blob/main/src/data/Pastis.py) that can be readily used to load data for training models on PASTIS-HD.
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+ The time series contained in PASTIS have variable lengths.
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+ The Sentinel 1 and 2 time series are stored in numpy array. The SPOT images are in TIFF format.
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+ The annotations are in numpy array too.
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+ ## Ground Truth Annotations
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+ The agricultural parcels are grouped into 18 different crop classes as shown in the table below. The backgroud class corresponds to non-agricultural land, and the void label for parcels that are mostly outside their patch.
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6582b7dd75754a803e484487/T2AvsxFivkkRfr1Zt8QKR.png)
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+ Additional information about the dataset can be found in the documentation/pastis-documentation.pdf document.
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  ## Credits
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