FLAIR -- Datasets
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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.
🔗 Links
📄 Dataset Preprint
📁 Toy dataset (~750MB) -direct download-
💻 Source Code (GitHub)
🏠 FLAIR datasets page
✉️ Contact Us – [email protected] – Questions or collaboration inquiries welcome!
🎯 Key Figures
| 🗺️ | ROI / Area Covered | ➡️ 2,822 ROIs / 2,528 km² |
| 🧠 | Modalities | ➡️ 6 modalities |
| 🏛️ | Departments (France) | ➡️ 74 |
| 🧩 | AI Patches (512×512 px @ 0.2m) | ➡️ 241,100 |
| 🖼️ | Annotated Pixels | ➡️ 63.2 billion |
| 🛰️ | Sentinel-2 Acquisitions | ➡️ 256,221 |
| 📡 | Sentinel-1 Acquisitions | ➡️ 532,696 |
| 📁 | Total Files | ➡️ ~2.5 million |
| 💾 | Total Dataset Size | ➡️ ~750 GB |
🗃️ 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
| Modality | Description | Resolution / Format | Metadata |
|---|---|---|---|
| BD ORTHO (AERIAL_RGBI) | Orthorectified aerial images with 4 bands (R, G, B, NIR). | 20 cm, 8-bit unsigned | Radiometric stats, acquisition dates/cameras |
| BD ORTHO HISTORIQUE (AERIAL-RLT_PAN) | Historical panchromatic aerial images (1947–1965), resampled. | ~40 cm, real: 0.4–1.2 m, 8-bit | Dates, original image references |
| ELEVATION (DEM_ELEV) | Elevation data with DSM (surface) and DTM (terrain) channels. | DSM: 20 cm, DTM: 1 m, Float32 | Object heights via DSM–DTM difference |
| SPOT (SPOT_RGBI) | SPOT 6-7 satellite images, 4 bands, calibrated reflectance. | 1.6 m (resampled) | Acquisition dates, radiometric stats |
| SENTINEL-2 (SENTINEL2_TS) | Annual time series with 10 spectral bands, calibrated reflectance. | 10.24 m (resampled) | Dates, radiometric stats, cloud/snow masks |
| SENTINEL-1 ASC/DESC (SENTINEL1-XXX_TS) | Radar time series (VV, VH), SAR backscatter (σ0). | 10.24 m (resampled) | Stats per ascending/descending series |
| LABELS CoSIA (AERIAL_LABEL-COSIA) | Land cover labels from aerial RGBI photo-interpretation. | 20 cm, 15–19 classes | Aligned with BD ORTHO, patch statistics |
| LABELS LPIS (ALL_LABEL-LPIS) | Crop type data from CAP declarations, hierarchical class structure. | 20 cm | Aligned with BD ORTHO, may differ from CoSIA |
🏷️ 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.
🌍 Spatial partition
FLAIR-HUB uses an official split for benchmarking, corresponding to the split_1 fold.
| TRAIN / VALIDATION | D004, D005, D006, D007, D008, D009, D010, D011, D013, D014, D016, D017, D018, D020, D021, D023, D024047, D025039, D029, D030, D031, D032, D033, D034, D035, D037, D038, D040, D041, D044, D045, D046, D049, D051, D052, D054057, D055, D056, D058, D059062, D060, D063, D065, D066, D067, D070, D072, D074, D077, D078, D080, D081, D086, D091 |
|---|---|
| TEST | D012, D015, D022, D026, D036, D061, D064, D068, D069, D071, D073, D075, D076, D083, D084, D085 |
🏆 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:
| Task | Model ID | mIoU | O.A. |
|---|---|---|---|
| 🗺️ Land-cover | LC-L | 65.8 | 78.2 |
| 🌾 Crop-types | LPIS-I | 39.2 | 87.2 |
The Model ID can be used to retrieve the corresponding pre-trained model from the FLAIR-HUB-MODELS collection.
🗺️ Land-cover
| Model ID | Aerial VHR | Elevation | SPOT | S2 t.s. | S1 t.s. | Historical | PARA. | EP. | O.A. | mIoU |
|---|---|---|---|---|---|---|---|---|---|---|
| LC-A | ✓ | 89.4 | 79 | 77.5 | 64.1 | |||||
| LC-B | ✓ | ✓ | 181.4 | 124 | 78.1 | 65.1 | ||||
| LC-C | ✓ | ✓ | ✓ | 270.6 | 129 | 78.2 | 65.2 | |||
| LC-D | ✓ | ✓ | 93.9 | 85 | 77.6 | 64.7 | ||||
| LC-E | ✓ | ✓ | 95.8 | 98 | 77.7 | 64.5 | ||||
| LC-F | ✓ | ✓ | ✓ | 97.7 | 64 | 77.7 | 64.9 | |||
| LC-G | ✓ | 0.9 | 89 | 57.8 | 34.2 | |||||
| LC-H | ✓ | 1.8 | 106 | 54.5 | 28.2 | |||||
| LC-I | ✓ | 89.2 | 94 | 64.1 | 43.5 | |||||
| LC-J | ✓ | 89.4 | 97 | 67.4 | 51.2 | |||||
| LC-K | ✓ | ✓ | 181.4 | 45 | 77.6 | 64.3 | ||||
| LC-L | ✓ | ✓ | ✓ | ✓ | ✓ | 276.4 | 121 | 78.2 | 65.8 | |
| LC-ALL | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 365.8 | 129 | 78.2 | 65.6 |
🌾 Crop-types
| Model ID | Aerial VHR | SPOT | S2 t.s. | S1 t.s. | PARA. | EP. | O.A. | mIoU |
|---|---|---|---|---|---|---|---|---|
| LV.1 - 23 classes (2 classes removed) | ||||||||
| LPIS-A | ✓ | 89.4 | 91 | 86.6 | 24.4 | |||
| LPIS-B | ✓ | ✓ | 181.2 | 99 | 87.1 | 26.1 | ||
| LPIS-C | ✓ | ✓ | 93.9 | 100 | 87.5 | 29.8 | ||
| LPIS-D | ✓ | ✓ | ✓ | 97.7 | 45 | 88.0 | 36.1 | |
| LPIS-E | ✓ | ✓ | ✓ | 183.1 | 46 | 87.6 | 30.3 | |
| LPIS-F | ✓ | 0.9 | 61 | 85.3 | 23.8 | |||
| LPIS-G | ✓ | 1.8 | 77 | 84.5 | 18.1 | |||
| LPIS-H | ✓ | ✓ | 2.8 | 61 | 84.9 | 23.8 | ||
| LPIS-I | ✓ | ✓ | ✓ | 97.5 | 49 | 87.2 | 39.2 | |
| LPIS-J | ✓ | ✓ | ✓ | ✓ | 186.9 | 53 | 88.0 | 35.4 |
| LPIS-K | ✓ | 89.2 | 14 | 84.5 | 15.1 |
📚 How to Cite
Anatol Garioud, Sébastien Giordano, Nicolas David, Nicolas Gonthier.
FLAIR-HUB: semantic segmentation and domain adaptation dataset. (2025).
DOI: https://doi.org/10.48550/arXiv.2506.07080
@article{ign2025flairhub,
doi = {10.48550/arXiv.2506.07080},
url = {https://arxiv.org/abs/2506.07080},
author = {Garioud, Anatol and Giordano, Sébastien and David, Nicolas and Gonthier, Nicolas},
title = {FLAIR-HUB : Large-scale Multimodal Dataset for Land Cover and Crop Mapping},
publisher = {arXiv},
year = {2025}
}
⚙️ Acknowledgement
Experiments have been conducted using HPC/AI resources provided by GENCI-IDRIS (Grant 2024-A0161013803, 2024-AD011014286R2 and 2025-A0181013803).
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