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metadata
license: etalab-2.0
pipeline_tag: image-segmentation
tags:
  - semantic segmentation
  - pytorch
  - landcover
model-index:
  - name: FLAIR-HUB_LC-A_RVB_swinlarge-upernet
    results:
      - task:
          type: semantic-segmentation
        dataset:
          name: IGNF/FLAIR-HUB/
          type: earth-observation-dataset
        metrics:
          - type: mIoU
            value: 63.356
            name: mIoU
          - type: OA
            value: 76.954
            name: Overall Accuracy
          - type: IoU
            value: 83.972
            name: IoU building
          - type: IoU
            value: 77.247
            name: IoU greenhouse
          - type: IoU
            value: 61.59
            name: IoU swimming pool
          - type: IoU
            value: 75.642
            name: IoU impervious surface
          - type: IoU
            value: 57.941
            name: IoU pervious surface
          - type: IoU
            value: 63.61
            name: IoU bare soil
          - type: IoU
            value: 90.07
            name: IoU water
          - type: IoU
            value: 54.777
            name: IoU snow
          - type: IoU
            value: 53.235
            name: IoU herbaceous vegetation
          - type: IoU
            value: 57.935
            name: IoU agricultural land
          - type: IoU
            value: 38.391
            name: IoU plowed land
          - type: IoU
            value: 78.814
            name: IoU vineyard
          - type: IoU
            value: 69.909
            name: IoU deciduous
          - type: IoU
            value: 59.468
            name: IoU coniferous
          - type: IoU
            value: 30.173
            name: IoU brushwood
library_name: pytorch

🌐 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-A_RVB_swinlarge-upernet

  • Encoder: swin_large_patch4_window12_384
  • Decoder: upernet
  • Metrics:
  • mIoU O.A. F-score Precision Recall
    63.36% 76.95% 76.35% 77.04% 76.37%
  • Params.: 199.4

General Informations


Training Config Hyperparameters

- Model architecture: swin_large_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 : [1,2,3]
- Input normalization (custom):
    - AERIAL_RGBI:
        mean: [105.66, 111.35, 102.18]
        std:  [52.23, 45.62, 44.30]

Training Data

- Train patches: 152225
- Validation patches: 38175
- Test patches: 50700
Classes distribution.

Training Logging

Training logging.

Metrics

Metric Value
mIoU 63.36%
Overall Accuracy 76.95%
F-score 76.35%
Precision 77.04%
Recall 76.37%
Class IoU (%) F-score (%) Precision (%) Recall (%)
building 83.97 91.29 91.49 91.08
greenhouse 77.25 87.16 84.38 90.14
swimming pool 59.15 74.33 73.53 75.15
impervious surface 75.64 86.13 86.24 86.02
pervious surface 57.94 73.37 71.93 74.87
bare soil 63.61 77.76 73.29 82.81
water 90.07 94.78 94.50 95.05
snow 54.78 70.78 92.39 57.37
herbaceous vegetation 53.23 69.48 72.51 66.69
agricultural land 57.93 73.37 69.54 77.64
plowed land 38.39 55.48 53.90 57.16
vineyard 78.81 88.15 85.33 91.17
deciduous 69.91 82.29 81.36 83.24
coniferous 59.47 74.58 78.84 70.76
brushwood 30.17 46.36 46.41 46.31

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