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--- |
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license: etalab-2.0 |
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task_categories: |
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- image-classification |
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- image-segmentation |
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tags: |
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- remote sensing |
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- Agricultural |
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size_categories: |
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- 1K<n<10K |
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--- |
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# 🌱 PASTIS-HD 🌿 Panoptic Agricultural Satellite TIme Series : optical time series, radar time series and very high resolution image |
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[PASTIS](https://github.com/VSainteuf/pastis-benchmark) is a benchmark dataset for panoptic and semantic segmentation of agricultural parcels from satellite time series. |
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It contains 2,433 patches within the French metropolitan territory with panoptic annotations (instance index + semantic label for each pixel). |
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Each patch is a Sentinel-2 multispectral image time series of variable lentgh. |
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This dataset have been extended in 2021 with aligned radar Sentinel-1 observations for all 2433 patches. |
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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. |
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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. |
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The image are resampled to a 1m resolution and converted to 8 bits. |
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This enhancement significantly improves the dataset's spatial content, providing more granular information for agricultural parcel segmentation. |
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**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. |
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## Dataset in numbers |
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🛰️ Sentinel 2 | 🛰️ Sentinel 1 | 🛰️ **SPOT 6-7 VHR** | 🗻 Annotations |
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:-------------------------------------------- | :-------------------------------------------------- | :------------------------------| :------------------------------ |
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➡️ 2,433 time series | ➡️ 2 time 2,433 time series | ➡️ **2,433 images** | 124,422 individual parcels |
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➡️ 10m / pixel | ➡️ 10m / pixel | ➡️ **1.5m / pixel** | covers ~4,000 km² |
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➡️ 128x128 pixels / images | ➡️ 128x128 pixels / images | ➡️ **1280x1280 pixels / images** | over 2B pixels |
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➡️ 38-61 acquisitions / series | ➡️ ~ 70 acquisitions / series | ➡️ **One observation** | 18 crop types |
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➡️ 10 spectral bands |➡️ 2 spectral bands | ➡️ **3 spectral bands** | |
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⚠️ The **SPOT data are natively 1.5m resolution**, but we over-sampled them at 1m to align them pixel-perfect with Sentinel data. |
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## Data loading |
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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. |
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The time series contained in PASTIS have variable lengths. |
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The Sentinel 1 and 2 time series are stored in numpy array. The SPOT images are in TIFF format. |
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The annotations are in numpy array too. |
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### Remark about the folder names |
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⚠️ The **DATA_S1A** folder contains the Sentinel-1 **ascendent** images whereas the **DATA_S1D** folder contains the Sentinel-1 **descendant** images. |
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## Ground Truth Annotations |
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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. |
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Additional information about the dataset can be found in the documentation/pastis-documentation.pdf document. |
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## Credits |
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- The Sentinel imagery used in PASTIS was retrieved from [THEIA](www.theia.land.fr): |
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"Value-added data processed by the CNES for the Theia www.theia.land.fr data cluster using Copernicus data. |
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The treatments use algorithms developed by Theia’s Scientific Expertise Centres. " |
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- 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 |
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by IGN. |
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- 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 |
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If you use PASTIS please cite the [related paper](https://arxiv.org/abs/2107.07933): |
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``` |
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@article{garnot2021panoptic, |
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title={Panoptic Segmentation of Satellite Image Time Series |
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with Convolutional Temporal Attention Networks}, |
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author={Sainte Fare Garnot, Vivien and Landrieu, Loic}, |
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journal={ICCV}, |
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year={2021} |
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} |
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``` |
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For the PASTIS-R optical-radar fusion dataset, please also cite [this paper](https://arxiv.org/abs/2112.07558v1): |
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``` |
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@article{garnot2021mmfusion, |
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title = {Multi-modal temporal attention models for crop mapping from satellite time series}, |
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journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, |
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year = {2022}, |
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doi = {https://doi.org/10.1016/j.isprsjprs.2022.03.012}, |
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author = {Vivien {Sainte Fare Garnot} and Loic Landrieu and Nesrine Chehata}, |
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} |
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``` |
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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): |
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``` |
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@article{astruc2024omnisat, |
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title={Omni{S}at: {S}elf-Supervised Modality Fusion for {E}arth Observation}, |
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author={Astruc, Guillaume and Gonthier, Nicolas and Mallet, Clement and Landrieu, Loic}, |
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journal={ECCV}, |
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year={2024} |
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} |
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``` |
