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  license: etalab-2.0
 
 
 
 
 
 
 
 
 
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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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+ - climate
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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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+
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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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+
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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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+
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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.
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+
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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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+
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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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+
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+
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+ - **Dataset in numbers**
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+
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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 | ➡️ **1m / 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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+
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+
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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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+
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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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+