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IGNF/
FLAIR-HUB_LPIS-F_utae

Name: FLAIR-HUB_LPIS-F_utae
Availability: InStock

pytorch

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_LPIS-F_utae

Encoder: UTAE
Decoder: UTAE
Metrics:

mIoU	O.A.	F-score	Precision	Recall
21.75%	85.28%	28.74%	28.98%	31.90%

Params.: 0.9

General Informations

Contact: flair@ign.fr
Code repository: https://github.com/IGNF/FLAIR-HUB
Paper: https://arxiv.org/abs/2506.07080
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	21.75%
Overall Accuracy	85.28%
F-score	28.74%
Precision	28.98%
Recall	31.90%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
grasses	43.13	60.27	64.86	56.28
wheat	59.61	74.70	66.33	85.47
barley	48.82	65.61	73.01	59.57
maize	68.82	81.53	73.99	90.78
other cereals	2.60	5.08	15.86	3.02
rice	0.00	0.00	0.00	0.00
flax/hemp/tobacco	0.00	0.00	0.00	0.00
sunflower	27.98	43.73	48.70	39.68
rapeseed	70.93	82.99	76.64	90.49
other oilseed crops	0.00	0.00	0.00	0.00
soy	12.37	22.02	14.49	45.84
other protein crops	20.86	34.52	27.86	45.35
fodder legumes	22.85	37.20	28.70	52.83
beetroots	1.51	2.98	17.46	1.63
potatoes	0.00	0.00	0.00	0.00
other arable crops	10.06	18.28	13.58	27.97
vineyard	24.52	39.38	37.92	40.96
olive groves	0.00	0.00	0.00	0.00
fruits orchards	0.00	0.00	0.00	0.00
nut orchards	0.00	0.00	0.00	0.00
other permanent crops	0.00	0.00	0.00	0.00
mixed crops	0.03	0.05	15.72	0.03
background	86.27	92.63	91.41	93.88

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_LPIS-F_utae

🌐 FLAIR-HUB Model Collection

🔍 Model: FLAIR-HUB_LPIS-F_utae

General Informations

Training Config Hyperparameters

Training Data

Training Logging

Metrics

Inference

Cite

IGNF/
FLAIR-HUB_LPIS-F_utae