Datasets:
Enhance FLAIR-HUB dataset card with MAESTRO paper, code, and sample usage (#1)
Browse files- Enhance FLAIR-HUB dataset card with MAESTRO paper, code, and sample usage (544a2c03b80c601f1bd6453b9586bebe59e113f5)
Co-authored-by: Niels Rogge <[email protected]>
README.md
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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
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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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# 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://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>
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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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## 📚 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).
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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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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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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).
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