---
license: etalab-2.0
task_categories:
- image-classification
- image-segmentation
tags:
- 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. This extension is named PASTIS-R.

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.  

- **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** | 

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6582b7dd75754a803e484487/sxmnCAGs0p2u_PALLsqyN.jpeg)


## Credits

- The Sentinel imagery used in PASTIS was retrieved from [THEIA](www.theia.land.fr): 
"Value-added data processed by the CNES for the Theia www.theia.land.fr data cluster using Copernicus data.
The treatments use algorithms developed by Theia’s Scientific Expertise Centres. "

- The annotations used in PASTIS stem from the French [land parcel identification system](https://www.data.gouv.fr/en/datasets/registre-parcellaire-graphique-rpg-contours-des-parcelles-et-ilots-culturaux-et-leur-groupe-de-cultures-majoritaire/) produced
 by IGN.

- 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.


## 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},
}
```

For the PASTIS-HD with the 3 modality optical-radar time series plus VHR images dataset, please also cite this paper:
```
@article{astruc2024omnisat,
  title={Omni{S}at: {S}elf-Supervised Modality Fusion for {E}arth Observation},
  author={Astruc, Guillaume and Gonthier, Nicolas and Mallet, Clement and Landrieu, Loic},
  journal={arXiv preprint arXiv:2404.08351},
  year={2024}
}
```