| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| language: |
| - en |
| tags: |
| - sentinel-2 |
| - remote-sensing |
| - earth-observation |
| - zarr |
| - geospatial |
| pretty_name: Sentinel-2 Land Cover Dataset (Zarr Format) |
| --- |
| # Sentinel-2 Land Cover Dataset (Zarr Format) |
|
|
| <!-- Provide a quick summary of the dataset. --> |
| ## Dataset Summary |
| This dataset contains land-cover labels derived from the [AI4LCC benchmark dataset](https://doi.theia.data-terra.org/ai4lcc/?lang=en) and corresponding Sentinel-2 satellite imagery. |
| The original labels and Sentinel-2 were converted into Zarr format to enable efficient storage and scalable access for large geospatial datasets. Sentinel-2 image tiles corresponding to the label locations were added to create a paired dataset suitable for machine learning tasks such as land-cover classification and semantic segmentation. |
| The dataset is intended for research and development in remote sensing, Earth observation, and machine learning. |
|
|
| <!--It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/hug<>gingface_hub/templates/datasetcard_template.md?plain=1).--> |
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|
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| ## Dataset Details |
|
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| ### Dataset Source |
| The label data originates from the AI4LCC benchmark dataset: [https://doi.theia.data-terra.org/ai4lcc/?lang=en](https://doi.theia.data-terra.org/ai4lcc/?lang=en) |
| The Sentinel-2 imagery was retrieved from Planetary Computer for the same spatial locations and time periods (along 2020) corresponding to the labels. |
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|
|
| ### Supported Tasks |
| The dataset can be used for: |
| - Land-cover classification |
| - Semantic segmentation |
| - Remote sensing representation learning |
| - Geospatial deep learning research |
|
|
| ### Dataset Structure |
| The dataset is stored in **Zarr format**, where each `.zarr` folder contains a single tile with all Sentinel-2 spectral bands, the corresponding label, and spatial coordinates. This format allows efficient storage and scalable access for large geospatial datasets.<br> |
| Current available granules: |
| - 30TXN, 30TXP, 30TXQ, 30TXR, 30TXS, |
| - 30TXT, 30TYN, 30TYP, 30TYQ, 30TYR, |
| - 30TYT, 31TCJ, 31TCK, 31TCL, 31TCM, |
| - 31TCN, 31TDK, 31TDL, 31TDM, 31TFN, |
| - 31UEP, 31UEQ |
| <!-- |
| Example repository structure: |
| dataset/ <br> |
| ├sentinel2_{granule}_reduced2021_withlabelclass1.zarr/ <br> |
| │ └── B01 <br> |
| │ └── B02 <br> |
| │ └── B03 <br> |
| │ └── B04 <br> |
| │ └── B05 <br> |
| │ └── B06 <br> |
| │ └── B07 <br> |
| │ └── B08 <br> |
| │ └── B8A <br> |
| │ └── B09 <br> |
| │ └── B11 <br> |
| │ └── B12 <br> |
| │ └── label <br> |
| │ └── spatial_ref <br> |
| │ └── x <br> |
| │ └── y <br> |
| ├── data1.zarr/ <br> |
| --> |
|
|
| ### Dataset Creation |
| - Retrieving Sentinel-2 imagery (2020) within ground-truth bounding boxes using the Planetary Computer API (sentinel-2-l2a) and odc-stac |
| - Generating annual composites using median reduction with Xarray |
| - Simplifying the original 14 land-cover classes into 5 classes as in Table 1 ref [Wenger et al., 2022](https://www.germain-forestier.info/publis/isprs2022.pdf) |
| - Combining imagery and labels |
| <p align="center"> |
| <img src="images/typology_AI4LCC.png" alt="typology" width="400"> |
| </p> |
|
|
| ### Intended Use |
| This dataset is intended for: |
| - machine learning research |
| - benchmarking remote sensing models |
| - land-cover classification studies |
| - geospatial AI experimentation |
| It may also serve as a benchmark dataset for scalable data pipelines using Zarr. |
|
|
| ### Limitations |
| Potential limitations include: |
| - Temporal mismatch between label creation and satellite acquisition |
| - Spatial resolution differences between label sources and imagery |
| - Class imbalance in land-cover categories |
| Users should validate dataset suitability for their specific application. |
|
|
| ### Licensing and Attribution |
| The label data originates from the AI4LCC benchmark dataset. |
| Users must comply with the licensing terms of the original dataset. |
| If using this dataset, please also cite the original AI4LCC dataset: [https://doi.theia.data-terra.org/ai4lcc/?lang=en](https://doi.theia.data-terra.org/ai4lcc/?lang=en) |
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| ## Acknowledgements |
| This dataset builds upon the AI4LCC benchmark dataset and publicly available Sentinel-2 satellite imagery. |
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