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  license: cc-by-4.0
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  ---
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- This dataset deals with the mapping of forest species using multi-modal Earth Observation data.
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- It is based on the existing dataset TreeSatAI by Ahlswede et al1.
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  While the original dataset only provides access to one Sentinel-1 & -2 image for each patch, this new dataset gathers all the available Sentinel-1 & -2 data spanning the same year for each patch.
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  Ahlswede et al. (https://essd.copernicus.org/articles/15/681/2023/) introduced the TreeSatAI Benchmark Archive, a new dataset for tree species classification in Central Europe based on multi-sensor data from aerial,
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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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- While the original dataset only provides access to one Sentinel-1 & -2 image for each patch, this time-series dataset collects all the available Sentinel-1 & -2 data spanning the same year for each aerial patch.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: cc-by-4.0
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  ---
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+ This dataset deals with the mapping of forest species using multi-modal Earth Observation data.<br>
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+ It is an extension of the <b>existing dataset TreeSatAI by Ahlswede et al.</b>
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  While the original dataset only provides access to one Sentinel-1 & -2 image for each patch, this new dataset gathers all the available Sentinel-1 & -2 data spanning the same year for each patch.
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  Ahlswede et al. (https://essd.copernicus.org/articles/15/681/2023/) introduced the TreeSatAI Benchmark Archive, a new dataset for tree species classification in Central Europe based on multi-sensor data from aerial,
 
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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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+
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+ - `sen-1-asc-data` : Sentinel-1 ascending orbit backscattering coefficient data (Tx2x6x6)
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+ - `sen-1-asc-products` : Sentinel-1 ascending orbit product names (T)
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+
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+ - `sen-1-des-data` : Sentinel-1 descending orbit backscattering coefficient data (Tx2x6x6)
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+ - `sen-1-des-data` : Sentinel-1 ascending orbit product names (T)
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+
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+ - `sen-2-data` : Sentinel-2 Level-2 BOA reflectances (Tx10x6x6)
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+ - `sen-2-masks` : Sentinel-2 cloud cover masks (Tx2x6x6)
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+ - `sen-2-products` : Sentinel-2 product names (T)
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+
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+
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+ To access the data in python you can use :
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+
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+ ```
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+ import h5py
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+
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+ with h5py.File(path/to/h5/file, 'r') as h5:
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+
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+ sen_1_asc_data = f['sen-1-asc-data'][:]
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+ sen_1_asc_products = f['sen-1-asc-products'][:]
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+
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+ sen_1_des_data = f['sen-1-des-data'][:]
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+ sen_1_des_products = f['sen-1-des-products'][:]
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+
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+ sen_2_data = f['sen-2-data'][:]
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+ sen_2_products = f['sen-2-products'][:]
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+ sen_2_masks = f['sen-2-masks'][:]
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+
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+ ```
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+