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IGNF/
FLAIR-HUB_LC-I_swinbase-upernet

Name: FLAIR-HUB_LC-I_swinbase-upernet
Availability: InStock

pytorch-lightning

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-I_swinbase-upernet

Encoder: swin_base_patch4_window12_384
Decoder: upernet
Metrics:

mIoU	O.A.	F-score	Precision	Recall
43.49%	64.13%	58.09%	59.34%	58.11%

Params.: 89.2

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: swin_base_patch4_window12_384-upernet
- 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:
    - SPOT_RGBI : [4,1,2]
- Input normalization (custom):
    - SPOT_RGBI:
        mean: [1137.09, 433.26, 508.75]
        std:  [543.11, 312.76, 284.61]

Training Data

yaml

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

Training Logging

Metrics

Metric	Value
mIoU	43.49%
Overall Accuracy	64.13%
F-score	58.09%
Precision	59.34%
Recall	58.11%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
building	57.23	72.79	72.23	73.37
greenhouse	49.83	66.52	64.05	69.18
swimming pool	13.76	24.20	39.65	17.41
impervious surface	53.18	69.43	68.13	70.78
pervious surface	39.98	57.12	56.88	57.37
bare soil	43.96	61.07	63.87	58.51
water	71.02	83.06	80.41	85.88
snow	61.98	76.53	69.44	85.23
herbaceous vegetation	36.86	53.87	56.88	51.16
agricultural land	48.21	65.05	57.30	75.23
plowed land	4.56	8.72	12.45	6.71
vineyard	42.06	59.21	69.71	51.47
deciduous	58.67	73.95	73.26	74.66
coniferous	52.95	69.23	71.47	67.14
brushwood	18.07	30.61	34.41	27.56

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-I_swinbase-upernet

🌐 FLAIR-HUB Model Collection

🔍 Model: FLAIR-HUB_LC-I_swinbase-upernet

General Informations

Training Config Hyperparameters

Training Data

Training Logging

Metrics

Inference

Cite

IGNF/
FLAIR-HUB_LC-I_swinbase-upernet