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Enhance FLAIR-HUB dataset card with MAESTRO paper, code, and sample usage (#1)

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- Enhance FLAIR-HUB dataset card with MAESTRO paper, code, and sample usage (544a2c03b80c601f1bd6453b9586bebe59e113f5)


Co-authored-by: Niels Rogge <[email protected]>

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  1. README.md +26 -10
README.md CHANGED
@@ -1,5 +1,10 @@
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  ---
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  license: etalab-2.0
 
 
 
 
 
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  tags:
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  - Multimodal
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  - Earth Observation
@@ -9,11 +14,6 @@ tags:
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  - Environement
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  - LandCover
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  - Agriculture
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- task_categories:
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- - image-segmentation
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- pretty_name: FLAIR-HUB
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- size_categories:
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- - 100K<n<1M
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  ---
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  # FLAIR-HUB : Large-scale Multimodal Dataset for Land Cover and Crop Mapping
@@ -32,8 +32,10 @@ learning methods, and will continue to grow with new modalities and annotations.
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  ## 🔗 Links
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  📄 <a href="https://arxiv.org/abs/2506.07080" target="_blank"><b>Dataset Preprint</b></a><br>
 
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  📁 <a href="https://storage.gra.cloud.ovh.net/v1/AUTH_366279ce616242ebb14161b7991a8461/defi-ia/flair_hub/FLAIR-HUB_TOY_DATASET.zip" target="_blank"><b>Toy dataset (~750MB) -direct download-</b></a><br>
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  💻 <a href="https://github.com/IGNF/FLAIR-HUB" target="_blank"><b>Source Code (GitHub)</b></a><br>
 
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  🏠 <a href="https://ignf.github.io/FLAIR/" target="_blank"><b>FLAIR datasets page </b></a><br>
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  ✉️ <a href="mailto:[email protected]"><b>Contact Us</b></a> – [email protected] – Questions or collaboration inquiries welcome!<br>
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  <hr>
@@ -183,8 +185,6 @@ FLAIR-HUB uses an <b>official split for benchmarking, corresponding to the split
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  <p align="center"><img src="datacard_imgs/FLAIR-HUB_splits_oneline.png" alt="" style="width:80%;max-width:1300px;" /></p>
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  <hr>
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-
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-
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  ## 🏆 Bechmark scores
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@@ -237,8 +237,25 @@ The **Model ID** can be used to retrieve the corresponding pre-trained model fro
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  | LPIS-J | ✓ | ✓ | ✓ | ✓ | 186.9 | 53 | **88.0** | 35.4 |
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  | LPIS-K | | ✓ | | | 89.2 | 14 | 84.5 | 15.1 |
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-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## 📚 How to Cite
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@@ -263,5 +280,4 @@ DOI: https://doi.org/10.48550/arXiv.2506.07080
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  ## ⚙️ Acknowledgement
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- Experiments have been conducted using HPC/AI resources provided by GENCI-IDRIS (Grant 2024-A0161013803, 2024-AD011014286R2 and 2025-A0181013803).
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-
 
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  ---
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  license: etalab-2.0
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+ size_categories:
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+ - 100K<n<1M
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+ task_categories:
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+ - image-segmentation
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+ pretty_name: FLAIR-HUB
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  tags:
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  - Multimodal
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  - Earth Observation
 
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  - Environement
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  - LandCover
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  - Agriculture
 
 
 
 
 
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  ---
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  # FLAIR-HUB : Large-scale Multimodal Dataset for Land Cover and Crop Mapping
 
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  ## 🔗 Links
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  📄 <a href="https://arxiv.org/abs/2506.07080" target="_blank"><b>Dataset Preprint</b></a><br>
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+ 📄 <a href="https://huggingface.co/papers/2508.10894" target="_blank"><b>MAESTRO Paper (using this dataset)</b></a><br>
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  📁 <a href="https://storage.gra.cloud.ovh.net/v1/AUTH_366279ce616242ebb14161b7991a8461/defi-ia/flair_hub/FLAIR-HUB_TOY_DATASET.zip" target="_blank"><b>Toy dataset (~750MB) -direct download-</b></a><br>
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  💻 <a href="https://github.com/IGNF/FLAIR-HUB" target="_blank"><b>Source Code (GitHub)</b></a><br>
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+ 💻 <a href="https://github.com/ignf/maestro" target="_blank"><b>MAESTRO Code (GitHub, uses this dataset)</b></a><br>
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  🏠 <a href="https://ignf.github.io/FLAIR/" target="_blank"><b>FLAIR datasets page </b></a><br>
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  ✉️ <a href="mailto:[email protected]"><b>Contact Us</b></a> – [email protected] – Questions or collaboration inquiries welcome!<br>
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  <hr>
 
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  <p align="center"><img src="datacard_imgs/FLAIR-HUB_splits_oneline.png" alt="" style="width:80%;max-width:1300px;" /></p>
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  <hr>
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  ## 🏆 Bechmark scores
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  | LPIS-J | ✓ | ✓ | ✓ | ✓ | 186.9 | 53 | **88.0** | 35.4 |
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  | LPIS-K | | ✓ | | | 89.2 | 14 | 84.5 | 15.1 |
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+ <hr>
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+ ## ✨ Sample Usage
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+
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+ This dataset is extensively used by the [MAESTRO model](https://huggingface.co/papers/2508.10894) for masked autoencoding on multimodal Earth observation data. You can find the MAESTRO model's code on its [GitHub repository](https://github.com/ignf/maestro).
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+
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+ A minimal example for using FLAIR-HUB with the MAESTRO framework:
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+ ```bash
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+ poetry run python main.py \
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+ model.model=mae \
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+ model.model_size=medium \
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+ run.exp_name=mae-m_flair \
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+ run.exp_dir=/path/to/experiments/dir \
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+ datasets.root_dir=/path/to/dataset/dir \
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+ datasets.flair.rel_dir=FLAIR-HUB \
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+ datasets.filter_pretrain=[flair] \
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+ datasets.filter_finetune=[flair]
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+ ```
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+ <hr>
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  ## 📚 How to Cite
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  ## ⚙️ Acknowledgement
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+ Experiments have been conducted using HPC/AI resources provided by GENCI-IDRIS (Grant 2024-A0161013803, 2024-AD011014286R2 and 2025-A0181013803).