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FLAIR-HUB_LC-F_swinbase-upernet

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metadata
license: etalab-2.0
pipeline_tag: image-segmentation
tags:
  - semantic segmentation
  - pytorch
  - landcover
library_name: pytorch
model-index:
  - name: FLAIR-HUB_LC-F_swinbase-upernet
    results:
      - task:
          type: semantic-segmentation
        dataset:
          name: IGNF/FLAIR-HUB/
          type: earth-observation-dataset
        metrics:
          - type: mIoU
            value: 64.868
            name: mIoU
          - type: OA
            value: 77.685
            name: Overall Accuracy
          - type: IoU
            value: 84.002
            name: IoU building
          - type: IoU
            value: 79.283
            name: IoU greenhouse
          - type: IoU
            value: 61.59
            name: IoU swimming pool
          - type: IoU
            value: 75.611
            name: IoU impervious surface
          - type: IoU
            value: 57.694
            name: IoU pervious surface
          - type: IoU
            value: 63.826
            name: IoU bare soil
          - type: IoU
            value: 90.461
            name: IoU water
          - type: IoU
            value: 68.142
            name: IoU snow
          - type: IoU
            value: 54.871
            name: IoU herbaceous vegetation
          - type: IoU
            value: 56.893
            name: IoU agricultural land
          - type: IoU
            value: 37.914
            name: IoU plowed land
          - type: IoU
            value: 78.144
            name: IoU vineyard
          - type: IoU
            value: 71.731
            name: IoU deciduous
          - type: IoU
            value: 63.69
            name: IoU coniferous
          - type: IoU
            value: 29.627
            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.		 🛩️
Historical
LC-A ✓ ✓
LC-D ✓ ✓ ✓
LC-F ✓ ✓ ✓ ✓
LC-G ✓ ✓
LC-I ✓ ✓
LC-L ✓ ✓ ✓ ✓ ✓ ✓
LPIS-A ✓ ✓
LPIS-F ✓ ✓
LPIS-I ✓ ✓ ✓ ✓
LPIS-J ✓ ✓ ✓ ✓ ✓

🔍 Model: FLAIR-HUB_LC-F_swinbase-upernet

  • Encoder: swin_base_patch4_window12_384
  • Decoder: upernet
  • Metrics:
    • mIoU O.A. F-score Precision Recall
    64.87% 77.69% 77.47% 77.69% 77.73%
  • Params.: 97.7

General Informations

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:
    - AERIAL_RGBI : [4,1,2]
    - SENTINEL2_TS: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    - SENTINEL1-ASC_TS: [1, 2]
    - SENTINEL1-DESC_TS: [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
Classes distribution.

Training Logging

Training logging.

Metrics

Metric Value
mIoU 64.87%
Overall Accuracy 77.69%
F-score 77.47%
Precision 77.69%
Recall 77.73%
Class IoU (%) F-score (%) Precision (%) Recall (%)
building 84.00 91.31 90.86 91.76
greenhouse 79.28 88.44 85.63 91.45
swimming pool 61.13 75.88 73.91 77.95
impervious surface 75.61 86.11 87.53 84.74
pervious surface 57.69 73.17 71.90 74.49
bare soil 63.83 77.92 73.15 83.35
water 90.46 94.99 95.79 94.20
snow 68.14 81.05 96.94 69.64
herbaceous vegetation 54.87 70.86 71.99 69.77
agricultural land 56.89 72.52 70.36 74.82
plowed land 37.91 54.98 50.82 59.88
vineyard 78.14 87.73 85.52 90.05
deciduous 71.73 83.54 83.31 83.77
coniferous 63.69 77.82 77.95 77.68
brushwood 29.63 45.71 49.63 42.36

Inference

Aerial ROI

AERIAL

Inference ROI

INFERENCE

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