- Dataset presentation
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 labelfor each pixel). Each patch is a Sentinel-2 multispectral image time series of variable lentgh.
We propose an official 5 fold split provided in the dataset's metadata, and evaluated several of the top-performing image time series networks. You are welcome to use our numbers and to submit your own entries to the leaderboard!
- Dataset in numbers
- Download
The dataset can be downloaded from zenodo.
- Dataloader
This repository also contains a PyTorch dataset class in code/dataloader.py
that can be readily used to load data for training.
- Metrics
A PyTorch implementation is also given in code/panoptic_metrics.py to compute
the panoptic metrics. In order to use these metrics, the model's output should contain an instance prediction
and a semantic prediction. The first one allocates an instance id to each pixel of the image,
and the latter a semantic label.
Please open an issue to submit new entries. Do mention if the work has been published and wether the code accessible for reproducibility. We require that at least a preprint is available to present the method used.
| Model name | #Params | OA | mIoU | Published |
|---|---|---|---|---|
| U-TAE | 1.1M | 83.2% | 63.1% | ✔️ link |
| Unet-3d* | 1.6M | 81.3% | 58.4% | ✔️ link |
| Unet-ConvLSTM* | 1.5M | 82.1% | 57.8% | ✔️ link |
| FPN-ConvLSTM* | 1.3M | 81.6% | 57.1% | ✔️ link |
Models that we re-implemented ourselves are denoted with a star (*).
| Model name | SQ | RQ | PQ |
|---|---|---|---|
| U-TAE + PaPs | 81.3 | 49.2 | 40.4 |
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.
If you use PASTIS please cite the related paper:
@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}
}
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The satellite imagery used in PASTIS was retrieved from THEIA: "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. "
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The annotations used in PASTIS stem from the French land parcel identification system produced by IGN, the French mapping agency.
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This work was partly supported by ASP, the French Payment Agency.