|
--- |
|
license: cc-by-4.0 |
|
--- |
|
|
|
# TreeSatAI-Time-Series |
|
**** |
|
|
|
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, |
|
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. |
|
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. |
|
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. |
|
|
|
<div style="border:0px; padding:25px; background-color:#F8F5F5; padding-top:10px; padding-bottom:1px;"> |
|
The hereby proposed dataset is an <b>extension of the existing dataset TreeSatAI by Ahlswede et al.</b><br> |
|
While the original dataset only grants access to a single Sentinel-1 & -2 image for each patch, this new dataset compiles <b>all available Sentinel-1 & -2 data spanning a year</b>. |
|
This integration of temporal information assists in distinguishing between different tree species. |
|
Notably, we aligned the year of the Sentinel Time Series with that of the aerial patch if it was 2017 or later. |
|
For preceding years, considering minimal changes in the forest and the need for sufficient temporal context, we specifically chose the year 2017. |
|
|
|
</div> |
|
|
|
**** |
|
|
|
<img src="TreesatAI-TS-fig.png" alt="TreesatAI-TS-fig" style="width: 100%; display: block; margin: 0 auto;"/> |
|
|
|
|
|
**** |
|
The dataset covers 50 381 patches of 60mx60m located through Germany. <br> |
|
|
|
The following zip files and folders are available :<br> |
|
📦 **aerial** (from the original dataset): aerial acquisitions at 0.2m spatial resolution with RGB and Infrared bands.<br> |
|
📦 **sentinel** (from the original dataset): the single acquisition of Sentinel-1 & -2 covering the patch extent (60m) or a wider area (200m)<br> |
|
📦 **sentinel-ts**: the yearly time series of Sentinel-1 & -2.<br> |
|
📦 **labels** (from the original dataset): patchwise labels of present tree species and proprotion.<br> |
|
📦 **geojson** (from the original dataset): vector file providing geographical location of the patches.<br> |
|
📦 **split** (from the original dataset): train, val and tests patches split.<br> |
|
|
|
Sentinel product names follow the official naming convention from the European Space Agency. |
|
**** |
|
|
|
The Sentinel Time Series are provided for each patch in HDF format (.h5) with several datasets : |
|
|
|
`sen-1-asc-data` : Sentinel-1 ascending orbit backscattering coefficient data (Tx2x6x6) | Channels: VV, VH <br> |
|
`sen-1-asc-products` : Sentinel-1 ascending orbit product names (T) <br> |
|
|
|
`sen-1-des-data`: Sentinel-1 descending orbit backscattering coefficient data (Tx2x6x6) | Channels: VV, VH <br> |
|
`sen-1-des-data` : Sentinel-1 ascending orbit product names (T) <br> |
|
|
|
`sen-2-data` : Sentinel-2 Level-2 BOA reflectances (Tx10x6x6) | Channels: B02,B03,B04,B05,B06,B07,B08,B8A,B11,B12 <br> |
|
`sen-2-masks` : Sentinel-2 cloud cover masks (Tx2x6x6) | Channels: snow probability, cloud probability <br> |
|
`sen-2-products` : Sentinel-2 product names (T) <br> |
|
**** |
|
|
|
To access the Sentinel Time Series data in python you can use : |
|
|
|
``` |
|
import h5py |
|
with h5py.File(path/to/h5/file, 'r') as h5file: |
|
sen_1_asc_data = h5file['sen-1-asc-data'][:] |
|
sen_1_asc_products = h5file['sen-1-asc-products'][:] |
|
sen_1_des_data = h5file['sen-1-des-data'][:] |
|
sen_1_des_products = h5file['sen-1-des-products'][:] |
|
sen_2_data = h5file['sen-2-data'][:] |
|
sen_2_products = h5file['sen-2-products'][:] |
|
sen_2_masks = h5file['sen-2-masks'][:] |
|
|
|
``` |
|
**** |
|
|
|
### Licence |
|
This dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |
|
|
|
### Contact |
|
If you have any questions, issues or feedback, you can contact us at: ai-challenge@ign.fr |
|
|
