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IGNF/
FLAIR-HUB_LC-G_utae

Name: FLAIR-HUB_LC-G_utae
Availability: InStock

semantic segmentation

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🌐 FLAIR-HUB Model Collection

Trained on: FLAIR-HUB dataset 🔗
Available modalities: Aerial images, SPOT images, Topographic info, Sentinel-2 yearly time-series, Sentinel-1 yearly time-series, Historical aerial images
Encoders: ConvNeXTV2, Swin (Tiny, Small, Base, Large)
Decoders: UNet, UPerNet
Tasks: Land-cover mapping (LC), Crop-type mapping (LPIS)
Class nomenclature: 15 classes for LC, 23 classes for LPIS

	Model task	Model inputs
🆔 Model ID	🗺️ Land-cover	🌾 Crop-types	🛩️ Aerial	⛰️ Elevation	🛰️ SPOT	🛰️ S2 t.s.	🛰️ S1 t.s.	🛩️ Historical
LC-A	✓		✓
LC-B	✓		✓	✓
LC-D	✓		✓			✓
LC-F	✓		✓			✓	✓
LC-G	✓					✓
LC-I	✓				✓
LC-L	✓		✓	✓	✓	✓	✓
LPIS-A		✓	✓
LPIS-F		✓				✓
LPIS-I		✓			✓	✓	✓
LPIS-J		✓	✓		✓	✓	✓

🔍 Model: FLAIR-HUB_LC-G_utae

Encoder: UTAE
Decoder: UTAE
Metrics:

mIoU	O.A.	F-score	Precision	Recall
34.24%	57.83%	47.30%	48.12%	47.59%

Params.: 0.9

General Informations

Contact: flair@ign.fr
Code repository: https://github.com/IGNF/FLAIR-HUB
Paper: https://arxiv.org/abs/2506.07080
Project Page https://ignf.github.io/FLAIR/FLAIR-HUB/flairhub
Developed by: IGN
Compute infrastructure:
- software: python, pytorch-lightning
- hardware: HPC/AI resources provided by GENCI-IDRIS
License: Etalab 2.0

Training Config Hyperparameters

yaml

- Model architecture: UTAE
- Optimizer: AdamW (betas=[0.9, 0.999], weight_decay=0.01)
- Learning rate: 5e-5
- Scheduler: one_cycle_lr (warmup_fraction=0.2)
- Epochs: 150
- Batch size: 5
- Seed: 2025
- Early stopping: patience 20, monitor val_miou (mode=max)
- Class weights:
    - default: 1.0
    - masked classes: [clear cut, ligneous, mixed, other] → weight = 0
- Input channels:
    - SENTINEL2_TS : [1,2,3,4,5,6,7,8,9,10]

Training Data

yaml

- Train patches: 152225
- Validation patches: 38175
- Test patches: 50700

Training Logging

Metrics

Metric	Value
mIoU	34.24%
Overall Accuracy	57.83%
F-score	47.30%
Precision	48.12%
Recall	47.59%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
building	34.91	51.75	54.37	49.37
greenhouse	0.00	0.00	0.00	0.00
swimming pool	0.00	0.00	0.00	0.00
impervious surface	38.27	55.35	51.43	59.92
pervious surface	27.43	43.05	51.24	37.12
bare soil	33.59	50.29	56.33	45.42
water	65.32	79.02	71.19	88.79
snow	67.54	80.63	69.71	95.61
herbaceous vegetation	34.44	51.23	51.81	50.66
agricultural land	42.08	59.24	57.01	61.65
plowed land	10.23	18.56	19.29	17.88
vineyard	41.10	58.26	67.59	51.20
deciduous	55.99	71.79	67.97	76.06
coniferous	48.22	65.06	77.39	56.12
brushwood	14.46	25.27	26.54	24.12
Selection deleted

Inference

Aerial ROI

Inference ROI

Cite

BibTeX:

@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},
}

APA:

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

IGNF/FLAIR-HUB_LC-G_utae

🌐 FLAIR-HUB Model Collection

🔍 Model: FLAIR-HUB_LC-G_utae

General Informations

Training Config Hyperparameters

Training Data

Training Logging

Metrics

Inference

Cite

IGNF/
FLAIR-HUB_LC-G_utae