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---
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 | ➡️ **1.5m / 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** |
⚠️ The **SPOT data are natively 1.5m resolution**, but we over-sampled them at 1m to align them pixel-perfect with Sentinel data.
## Data loading
The Github repository associated to this dataset contains a PyTorch dataset class of [the OmniSat repository](https://github.com/gastruc/OmniSat/blob/main/src/data/Pastis.py) that can be readily used to load data for training models on PASTIS-HD.
The time series contained in PASTIS have variable lengths.
The Sentinel 1 and 2 time series are stored in numpy array. The SPOT images are in TIFF format.
The annotations are in numpy array too.
### Remark about the folder names
⚠️ The **DATA_S1A** folder contains the Sentinel-1 **ascendent** images whereas the **DATA_S1D** folder contains the Sentinel-1 **descendant** images.
## Ground Truth Annotations
The agricultural parcels are grouped into 18 different crop classes as shown in the table below. The backgroud class corresponds to non-agricultural land, and the void label for parcels that are mostly outside their patch.
Additional information about the dataset can be found in the documentation/pastis-documentation.pdf document.
## 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 modalities optical-radar time series plus VHR images dataset, please also cite [this paper](https://arxiv.org/abs/2404.08351):
```
@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={ECCV},
year={2024}
}
``` |