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Skydingos/
FLAIR-HUB_LC-A_IR_swintiny-upernet

Name: FLAIR-HUB_LC-A_IR_swintiny-upernet
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_LC-A_swintiny-upernet

Encoder: swin_tiny_patch4_window7_224
Decoder: upernet
Metrics:

mIoU	O.A.	F-score	Precision	Recall
62.16%	76.24%	75.33%	75.71%	75.31%

Params.: 29.4

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: swin_tiny_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]

Training Data

yaml

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

Training Logging

Metrics

Metric	Value
mIoU	62.16%
Overall Accuracy	76.24%
F-score	75.33%
Precision	75.71%
Recall	75.31%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
building	82.37	90.33	90.46	90.20
greenhouse	72.11	83.80	79.52	88.57
swimming pool	58.68	73.96	71.85	76.19
impervious surface	74.27	85.23	85.56	84.92
pervious surface	55.75	71.59	70.49	72.73
bare soil	60.93	75.72	74.02	77.51
water	88.45	93.87	95.17	92.61
snow	64.39	78.34	91.63	68.41
herbaceous vegetation	52.59	68.93	70.88	67.09
agricultural land	55.37	71.27	67.69	75.25
plowed land	30.81	47.10	45.73	48.56
vineyard	76.44	86.65	84.87	88.50
deciduous	70.95	83.00	81.34	84.73
coniferous	60.39	75.31	80.00	71.13
brushwood	28.89	44.83	46.43	43.33

Inference

Aerial ROI

Inference ROI

Cite

BibTeX:

@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}
}

APA:

Anatol Garioud, Sébastien Giordano, Nicolas David, Nicolas Gonthier. 
FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping. (2025). 
DOI: https://doi.org/10.48550/arXiv.2506.07080

Skydingos/FLAIR-HUB_LC-A_IR_swintiny-upernet

🌐 FLAIR-HUB Model Collection

🔍 Model: FLAIR-HUB_LC-A_swintiny-upernet

General Informations

Training Config Hyperparameters

Training Data

Training Logging

Metrics

Inference

Cite

Skydingos/
FLAIR-HUB_LC-A_IR_swintiny-upernet