metadata

license: etalab-2.0
pipeline_tag: image-segmentation
tags:
  - semantic segmentation
  - pytorch
  - landcover
model-index:
  - name: FLAIR-HUB_LC-I_swinbase-upernet
    results:
      - task:
          type: semantic-segmentation
        dataset:
          name: IGNF/FLAIR-HUB/
          type: earth-observation-dataset
        metrics:
          - type: mIoU
            value: 43.488
            name: mIoU
          - type: OA
            value: 64.126
            name: Overall Accuracy
          - type: IoU
            value: 57.225
            name: IoU building
          - type: IoU
            value: 49.833
            name: IoU greenhouse
          - type: IoU
            value: 61.59
            name: IoU swimming pool
          - type: IoU
            value: 53.178
            name: IoU impervious surface
          - type: IoU
            value: 39.981
            name: IoU pervious surface
          - type: IoU
            value: 43.96
            name: IoU bare soil
          - type: IoU
            value: 71.021
            name: IoU water
          - type: IoU
            value: 61.981
            name: IoU snow
          - type: IoU
            value: 36.862
            name: IoU herbaceous vegetation
          - type: IoU
            value: 48.208
            name: IoU agricultural land
          - type: IoU
            value: 4.559
            name: IoU plowed land
          - type: IoU
            value: 42.06
            name: IoU vineyard
          - type: IoU
            value: 58.671
            name: IoU deciduous
          - type: IoU
            value: 52.946
            name: IoU coniferous
          - type: IoU
            value: 18.069
            name: IoU brushwood
library_name: pytorch-lightning

🌐 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-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: [email protected]
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

- 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

- 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{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