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🌱 PASTIS-HD 🌿 Panoptic Agricultural Satellite TIme Series : optical time series, radar time series and very high resolution image

PASTIS 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 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.


  • The Sentinel imagery used in PASTIS was retrieved from THEIA: "Value-added data processed by the CNES for the Theia 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 produced by IGN.

  • The SPOT images are opendata thanks to the Dataterra Dinamis initiative in the case of the "Couverture France DINAMIS" program.


If you use PASTIS please cite the related paper:

  title={Panoptic Segmentation of Satellite Image Time Series
with Convolutional Temporal Attention Networks},
  author={Sainte Fare Garnot, Vivien  and Landrieu, Loic },

For the PASTIS-R optical-radar fusion dataset, please also cite this paper:

  title    = {Multi-modal temporal attention models for crop mapping from satellite time series},
  journal  = {ISPRS Journal of Photogrammetry and Remote Sensing},
  year     = {2022},
  doi      = {},
  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:

  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},
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