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