| --- |
| license: etalab-2.0 |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - image-classification |
| - image-segmentation |
| tags: |
| - remote sensing |
| - Agricultural |
| --- |
| |
| # 🌱 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. Each each Sentinel1 observation is assembled into a 3-channel image: vertical polarization (VV), horizontal polarisation (VH), and the ratio vertical over horizontal polarization (VV/VH). 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. |
|
|
| This dataset is used in the paper [MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data](https://huggingface.co/papers/2508.10894). |
| The code for MAESTRO is available at: [https://github.com/ignf/maestro](https://github.com/ignf/maestro) |
|
|
| ## 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. |
|
|
| ⚠️ The S2 and S1 folders contains more than 2433 files on the contrary to the labels folder. Some patches are not labelled and not used for training. |
| The relevant information can be find in the metadata.geojson file (with 2433 entries), which is used as an index by the dataloader. |
|
|
| ### 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} |
| } |
| ``` |
| If you use the MAESTRO model or find its evaluation on this dataset useful, please cite: |
| ```bibtex |
| @article{labatie2025maestro, |
| title={MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data}, |
| author={Labatie, Antoine and Vaccaro, Michael and Lardiere, Nina and Garioud, Anatol and Gonthier, Nicolas}, |
| journal={arXiv preprint arXiv:2508.10894}, |
| year={2025} |
| } |
| ``` |
