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@@ -14,6 +14,23 @@ Sentinel-1 and Sentinel-2 1. The dataset contains labels of 20 European tree spe
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  The authors propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data.
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  Finally, they provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods.
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  The Sentinel Time Series are provided for each patch in HDF format (.h5) with several datasets :
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  - **`sen-1-asc-data`** : Sentinel-1 ascending orbit backscattering coefficient data (Tx2x6x6) | Channels: VV, VH
 
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  The authors propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data.
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  Finally, they provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods.
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+ The dataset covers 50 381 patches of 60mx60m located through Germany. <br>
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+ The following folders are available :<br>
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+ 📁 **aerial** (from the original dataset): aerial acquisitions at 0.2m spatial resolution with RGB and Infrared bands.<br>
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+ 📁 **sentinel** (from the original dataset): the single acquisition of Sentinel-1 & -2 covering the patch extent (60m) or a wider area (200m)<br>
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+ 📁 **sentinel-ts**: the yearly time series of Sentinel-1 & -2.<br>
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+ 📁 **geojson** (from the original dataset): vector file providing geographical location of the patches.<br>
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+ 📁 **split** (from the original dataset): train, val and tests patches split.<br>
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  The Sentinel Time Series are provided for each patch in HDF format (.h5) with several datasets :
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  - **`sen-1-asc-data`** : Sentinel-1 ascending orbit backscattering coefficient data (Tx2x6x6) | Channels: VV, VH