metadata

license: etalab-2.0
pipeline_tag: image-segmentation
tags:
  - semantic segmentation
  - pytorch
  - landcover
library_name: pytorch
model-index:
  - name: FLAIR-HUB_LC-A_swintiny-upernet
    results:
      - task:
          type: semantic-segmentation
        dataset:
          name: IGNF/FLAIR-HUB/
          type: earth-observation-dataset
        metrics:
          - type: mIoU
            value: 62.159
            name: mIoU
          - type: OA
            value: 76.242
            name: Overall Accuracy
          - type: IoU
            value: 82.367
            name: IoU building
          - type: IoU
            value: 72.114
            name: IoU greenhouse
          - type: IoU
            value: 61.59
            name: IoU swimming pool
          - type: IoU
            value: 74.269
            name: IoU impervious surface
          - type: IoU
            value: 55.749
            name: IoU pervious surface
          - type: IoU
            value: 60.933
            name: IoU bare soil
          - type: IoU
            value: 88.453
            name: IoU water
          - type: IoU
            value: 64.387
            name: IoU snow
          - type: IoU
            value: 52.594
            name: IoU herbaceous vegetation
          - type: IoU
            value: 55.365
            name: IoU agricultural land
          - type: IoU
            value: 30.807
            name: IoU plowed land
          - type: IoU
            value: 76.441
            name: IoU vineyard
          - type: IoU
            value: 70.946
            name: IoU deciduous
          - type: IoU
            value: 60.394
            name: IoU coniferous
          - type: IoU
            value: 28.887
            name: IoU brushwood

🌐 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 ID	🗺️ Land-cover	🌾 Crop-types	🛩️ Aerial	⛰️ Elevation	🛰️ SPOT	🛰️ S2 t.s.	🛰️ S1 t.s.
LC-A	✓		✓
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: [email protected]
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

- 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

- 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