Update README.md

README.md

@@ -1,3 +1,65 @@
 ---
 license: etalab-2.0
+task_categories:
+- image-classification
+- image-segmentation
+tags:
+- climate
+- remote sensing
+- Agricultural
+size_categories:
+- 1K<n<10K
 ---
+
+
+# 🌱 PASTIS-HD 🌿 Panoptic Agricultural Satellite TIme Series : optical time series, radar time series and very high resolution image
+
+[PASTIS](https://github.com/VSainteuf/pastis-benchmark) is a benchmark dataset for panoptic and semantic segmentation of agricultural parcels from satellite time series. 
+It contains 2,433 patches within the French metropolitan territory with panoptic annotations (instance index + semantic label for each pixel). 
+Each patch is a Sentinel-2 multispectral image time series of variable lentgh. 
+
+This dataset have been extended in 2021 with aligned radar Sentinel-1 observations for all 2433 patches. 
+For each patch, it constains approximately 70 observations of Sentinel-1 in ascending orbit, and 70 observations in descending orbit. 
+
+We extend PASTIS with aligned very high resolution satellite images from SPOT 6-7 constellation for all 2433 patches in addition to the Sentinel-1 and 2 time series. 
+The image are resampled to a 1m resolution and converted to 8 bits. 
+This enhancement significantly improves the dataset's spatial content, providing more granular information for agricultural parcel segmentation.
+PASTIS-HD can be used to evaluate multi-modal fusion methods (with optical time series, radar time series and VHR images) for parcel-based classification, semantic segmentation, and panoptic segmentation.  
+
+The SPOT images are opendata thanks to the Dataterra Dinamis initiative in the case of the ["Couverture France DINAMIS"](https://dinamis.data-terra.org/opendata/) program.
+
+
+- **Dataset in numbers**
+
+🛰️ Sentinel 2                                    | 🛰️ Sentinel 1                                          | 🛰️ **SPOT 6-7 VHR**    | 🗻 Annotations
+:-------------------------------------------- | :-------------------------------------------------- | :------------------------------| :------------------------------
+➡️ 2,433 time series          | ➡️ 2 time 2,433 time series | ➡️ **2,433 images** | 124,422 individual parcels 
+➡️ 10m / pixel | ➡️ 10m / pixel | ➡️ **1m / pixel** | covers ~4,000 km²
+➡️ 128x128 pixels / images | ➡️ 128x128 pixels / images | ➡️ **1280x1280 pixels / images** | over 2B pixels 
+➡️ 38-61 acquisitions / series | ➡️ ~ 70 acquisitions / series | ➡️ **One observation** | 18 crop types   
+➡️  10 spectral bands      |➡️  2 spectral bands | ➡️  **3 spectral bands** | 
+
+
+## References
+If you use PASTIS please cite the [related paper](https://arxiv.org/abs/2107.07933):
+```
+@article{garnot2021panoptic,
+  title={Panoptic Segmentation of Satellite Image Time Series
+with Convolutional Temporal Attention Networks},
+  author={Sainte Fare Garnot, Vivien  and Landrieu, Loic },
+  journal={ICCV},
+  year={2021}
+}
+```
+
+For the PASTIS-R optical-radar fusion dataset, please also cite [this paper](https://arxiv.org/abs/2112.07558v1):
+```
+@article{garnot2021mmfusion,
+  title    = {Multi-modal temporal attention models for crop mapping from satellite time series},
+  journal  = {ISPRS Journal of Photogrammetry and Remote Sensing},
+  year     = {2022},
+  doi      = {https://doi.org/10.1016/j.isprsjprs.2022.03.012},
+  author   = {Vivien {Sainte Fare Garnot} and Loic Landrieu and Nesrine Chehata},
+}
+```
+