IGNF/FLAIR-HUB_LC-A_IR_swinsmall-upernet

🌐 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-A_swinsmall-upernet

• Encoder: swin_small_patch4_window7_224
• Decoder: upernet
• Metrics:

mIoU	O.A.	F-score	Precision	Recall
63.18%	76.91%	76.20%	76.87%	76.28%

• Params.: 50.7

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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

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Training Config Hyperparameters

- Model architecture: swin_small_patch4_window7_224-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:
    - AERIAL_RGBI : [4,1,2]
- Input normalization (custom):
    - AERIAL_RGBI:
        mean: [106.59, 105.66, 111.35]
        std:  [39.78, 52.23, 45.62]

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Training Data

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

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Training Logging

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Metrics

Metric	Value
mIoU	63.18%
Overall Accuracy	76.91%
F-score	76.20%
Precision	76.87%
Recall	76.28%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
building	83.45	90.98	90.83	91.13
greenhouse	77.03	87.03	83.66	90.67
swimming pool	60.78	75.61	74.79	76.44
impervious surface	74.99	85.71	86.52	84.91
pervious surface	56.36	72.09	69.89	74.44
bare soil	61.39	76.07	71.62	81.12
water	89.39	94.40	95.29	93.52
snow	56.85	72.49	94.54	58.78
herbaceous vegetation	53.53	69.73	72.02	67.59
agricultural land	57.10	72.69	69.68	75.98
plowed land	37.71	54.76	50.79	59.41
vineyard	77.63	87.40	85.05	89.89
deciduous	70.95	83.01	81.86	84.18
coniferous	61.86	76.43	79.51	73.59
brushwood	28.72	44.62	46.93	42.52

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Inference

Aerial ROI

Inference ROI

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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