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README.md
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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 <b>extension of the existing dataset TreeSatAI by Ahlswede et al.</b><br>
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While the original dataset only grants access to a single Sentinel-1 & -2 image for each patch, this new dataset compiles all available Sentinel-1 & -2 data spanning a year.
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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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📁 **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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To access the data in python you can use :
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```
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license: cc-by-4.0
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---
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# TreeSatAI-Time-Series
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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 <b>extension of the existing dataset TreeSatAI by Ahlswede et al.</b><br>
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While the original dataset only grants access to a single Sentinel-1 & -2 image for each patch, this new dataset compiles all available Sentinel-1 & -2 data spanning a year.
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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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****
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The dataset covers 50 381 patches of 60mx60m located through Germany. <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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📁 **labels** (from the original dataset): patchwise labels of present tree species and proprotion.<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 <br>
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`sen-1-asc-products` : Sentinel-1 ascending orbit product names (T) <br>
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`sen-1-des-data`: Sentinel-1 descending orbit backscattering coefficient data (Tx2x6x6) | Channels: VV, VH <br>
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`sen-1-des-data` : Sentinel-1 ascending orbit product names (T) <br>
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`sen-2-data` : Sentinel-2 Level-2 BOA reflectances (Tx10x6x6) | Channels: B02,B03,B04,B05,B06,B07,B08,B8A,B11,B12 <br>
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`sen-2-masks` : Sentinel-2 cloud cover masks (Tx2x6x6) | Channels: snow probability, cloud probability <br>
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`sen-2-products` : Sentinel-2 product names (T) <br>
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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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### Licence
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This dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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### Contact
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