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metadata

license: etalab-2.0
tags:
  - semantic segmentation
  - pytorch
  - landcover
model-index:
  - name: FLAIR-HUB_LC-A_convnextv2base-unet
    results:
      - task:
          type: semantic-segmentation
        dataset:
          name: IGNF/FLAIR-HUB/
          type: earth-observation-dataset
        metrics:
          - name: mIoU
            type: mIoU
            value: 64.16
          - name: Overall Accuracy
            type: OA
            value: 77.17
          - name: IoU building
            type: IoU
            value: 84.15
          - name: IoU greenhouse
            type: IoU
            value: 76.22
          - name: IoU swimming pool
            type: IoU
            value: 61.59
          - name: IoU impervious surface
            type: IoU
            value: 75.24
          - name: IoU pervious surface
            type: IoU
            value: 56.17
          - name: IoU bare soil
            type: IoU
            value: 63.02
          - name: IoU water
            type: IoU
            value: 88.96
          - name: IoU snow
            type: IoU
            value: 72.54
          - name: IoU herbaceous vegetation
            type: IoU
            value: 54.22
          - name: IoU agricultural land
            type: IoU
            value: 57.09
          - name: IoU plowed land
            type: IoU
            value: 36.27
          - name: IoU vineyard
            type: IoU
            value: 77.47
          - name: IoU deciduous
            type: IoU
            value: 71.33
          - name: IoU coniferous
            type: IoU
            value: 60.43
          - name: IoU brushwood
            type: IoU
            value: 29.3
pipeline_tag: image-segmentation

🌐 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	🛰️ SPOT	🛰️ S2 t.s.	🛰️ S1 t.s.
LC-A	✓		✓
LC-D	✓		✓		✓
LC-F	✓		✓		✓	✓
LC-G	✓				✓
LC-I	✓			✓
LPIS-A		✓	✓
LPIS-F		✓			✓
LPIS-I		✓		✓	✓	✓
LPIS-J		✓	✓	✓	✓	✓

🔍 Model: FLAIR-HUB_LC-A_convnextv2base-unet

Encoder: swin_base_patch4_window12_384
Decoder: upernet
Metrics:

mIoU	O.A.	F-score	Precision	Recall
64.16%	77.17%	76.92%	77.58%	76.59%

Params.: 89.4

General Informations

Contact: [email protected]
Code repository: https://github.com/IGNF/FLAIR-HUB
Paper: https://arxiv.org/pdf/2211.12979.pdf
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:
    - 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	64.13%
Overall Accuracy	77.45%
F-score	76.88%
Precision	77.36%
Recall	76.89%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
building	84.15	91.39	91.15	91.64
greenhouse	76.22	86.50	84.11	89.04
swimming pool	60.03	75.02	76.08	73.99
impervious surface	75.24	85.87	86.75	85.01
pervious surface	56.17	71.94	69.87	74.14
bare soil	63.02	77.31	74.19	80.71
water	88.96	94.16	94.98	93.35
snow	72.54	84.08	97.77	73.76
herbaceous vegetation	54.22	70.31	71.67	69.01
agricultural land	57.09	72.68	69.75	75.87
plowed land	36.27	53.23	52.71	53.77
vineyard	77.47	87.30	85.34	89.36
deciduous	71.33	83.26	81.90	84.67
coniferous	60.43	75.33	80.13	71.08
brushwood	29.30	45.33	47.34	43.48

Inference

Aerial ROI

Inference ROI

Cite

BibTeX:

@article{ign2025flairhub,
  doi = {10.13140/RG.2.2.30183.73128/1},
  url = {https://arxiv.org/pdf/2211.12979.pdf},
  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/10.13140/RG.2.2.30183.73128/1