FLAIR-HUB : Large-scale Multimodal Dataset for Land Cover and Crop Mapping

FLAIR-HUB builds upon and includes the FLAIR#1 and FLAIR#2 datasets, expanding them into a unified, large-scale, multi-sensor land-cover resource with very-high-resolution annotations. Spanning over 2,500 km² of diverse French ecoclimates and landscapes, it features 63 billion hand-annotated pixels across 19 land-cover and 23 crop type classes.
The dataset integrates complementary data sources including aerial imagery, SPOT and Sentinel satellites, surface models, and historical aerial photos, offering rich spatial, spectral, and temporal diversity. FLAIR-HUB supports the development of semantic segmentation, multimodal fusion, and self-supervised learning methods, and will continue to grow with new modalities and annotations.

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🔗 Links

📄 Dataset Preprint
📄 MAESTRO Paper (using this dataset)
📁 Toy dataset (~750MB) -direct download-
💻 Source Code (GitHub)
💻 MAESTRO Code (GitHub, uses this dataset)
🏠 FLAIR datasets page
✉️ Contact Us – flair@ign.fr – Questions or collaboration inquiries welcome!

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🎯 Key Figures

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🗃️ Dataset Structure

data/
├── DOMAIN_SENSOR_DATATYPE/ 
│   ├── ROI/
│   │   ├── <Patch>.tif # image file 
│   │   ├── <Patch>.tif
|   |   ├── ...
│   └── ...
├── ...
├── DOMAIN_SENSOR_LABEL-XX/
│   ├── ROI/
│   │   ├── <Patch>.tif # supervision file 
│   │   ├── <Patch>.tif
│   └── ...
├── ...
└── GLOBAL_ALL_MTD/
    ├── GLOABAL_SENSOR_MTD.gpkg # metadata file
    ├── GLOABAL_SENSOR_MTD.gpkg
    └── ...   

🗂️ Data Modalities Overview

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🏷️ Supervision

FLAIR-HUB includes two complementary supervision sources: AERIAL_LABEL-COSIA, a high-resolution land cover annotation derived from expert photo-interpretation of RGBI imagery, offering pixel-level precision across 19 classes; and AERIAL_LABEL-LPIS, a crop-type annotation based on farmer-declared parcels from the European Common Agricultural Policy, structured into a three-level taxonomy of up to 46 crop classes. While COSIA reflects actual land cover, LPIS captures declared land use, and the two differ in purpose, precision, and spatial alignment.

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🌍 Spatial partition

FLAIR-HUB uses an official split for benchmarking, corresponding to the split_1 fold.

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🏆 Bechmark scores

Several model configurations were trained (see the accompanying data paper). The best-performing configurations for both land-cover and crop-type classification tasks are summarized below:

The Model ID can be used to retrieve the corresponding pre-trained model from the FLAIR-HUB-MODELS collection.

🗺️ Land-cover

🌾 Crop-types

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🔎 Filter dataset with the FLAIR-HUB Dataset Browser

A small desktop GUI to browse and download subsets of the IGNF/FLAIR-HUB dataset from Hugging Face with filters for: Domain, Year, Modality or Data type.

Requirements:

• Python 3.9+
• Tkinter (usually included; on Linux you may need: sudo apt-get install python3-tk)
• Python packages: pip install huggingface_hub

Run:

1. Download the file flair-hub-HF-dl.py from the Files section of this dataset.
2. In a terminal: pip install huggingface_hub
3. Launch: python flair-hub-HF-dl.py

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✨ MAESTRO basecode

This dataset is extensively used by the MAESTRO model for masked autoencoding on multimodal Earth observation data. You can find the MAESTRO model's code on its GitHub repository.

A minimal example for using FLAIR-HUB with the MAESTRO framework:

poetry run python main.py \
        model.model=mae \
        model.model_size=medium \
        run.exp_name=mae-m_flair \
        run.exp_dir=/path/to/experiments/dir \
        datasets.root_dir=/path/to/dataset/dir \
        datasets.flair.rel_dir=FLAIR-HUB \
        datasets.filter_pretrain=[flair] \
        datasets.filter_finetune=[flair]

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📚 How to Cite

Anatol Garioud, Sébastien Giordano, Nicolas David, Nicolas Gonthier. 
FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping.
ISPRS Journal of Photogrammetry and Remote Sensing, Volume 237, 2026.
DOI: https://doi.org/10.1016/j.isprsjprs.2026.04.017

@article{GARIOUD2026271,
title = {FLAIR-HUB: Large-scale multimodal dataset for land cover and crop mapping},
author = {Anatol Garioud and Sébastien Giordano and Nicolas David and Nicolas Gonthier},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {237},
pages = {271-300},
year = {2026},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2026.04.017},
url = {https://www.sciencedirect.com/science/article/pii/S0924271626001899},
}

⚙️ Acknowledgement

Experiments have been conducted using HPC/AI resources provided by GENCI-IDRIS (Grant 2024-A0161013803, 2024-AD011014286R2 and 2025-A0181013803).