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license: cc-by-4.0 |
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# TreeSatAI-Time-Series |
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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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This integration of temporal information assists in distinguishing between different tree species. |
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Notably, we aligned the year of the Sentinel Time Series with that of the aerial patch if it was 2017 or later. |
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For preceding years, considering minimal changes in the forest and the need for sufficient temporal context, we specifically chose the year 2017. |
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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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Sentinel-1 and Sentinel-2. The dataset contains labels of 20 European tree species (*i.e.*, 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany. |
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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 archive files and 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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📁 **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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Sentinel product names follow the official naming convention from the European SPace Agency. |
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To access the Sentinel Time Series data in python you can use : |
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``` |
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import h5py |
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with h5py.File(path/to/h5/file, 'r') as h5: |
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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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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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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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### 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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If you have any questions, issues or feedback, you can contact us at: ai-challenge@ign.fr |
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