metadata

license: etalab-2.0
pipeline_tag: image-segmentation
tags:
  - semantic segmentation
  - pytorch
  - landcover
library_name: pytorch
model-index:
  - name: FLAIR-HUB_LC-A_convnextv2base-upernet
    results:
      - task:
          type: semantic-segmentation
        dataset:
          name: IGNF/FLAIR-HUB/
          type: earth-observation-dataset
        metrics:
          - type: mIoU
            value: 63.771
            name: mIoU
          - type: OA
            value: 77.031
            name: Overall Accuracy
          - type: IoU
            value: 83.5
            name: IoU building
          - type: IoU
            value: 76.548
            name: IoU greenhouse
          - type: IoU
            value: 59.37
            name: IoU swimming pool
          - type: IoU
            value: 74.837
            name: IoU impervious surface
          - type: IoU
            value: 56.544
            name: IoU pervious surface
          - type: IoU
            value: 63.005
            name: IoU bare soil
          - type: IoU
            value: 89.533
            name: IoU water
          - type: IoU
            value: 67.806
            name: IoU snow
          - type: IoU
            value: 53.766
            name: IoU herbaceous vegetation
          - type: IoU
            value: 57.318
            name: IoU agricultural land
          - type: IoU
            value: 34.667
            name: IoU plowed land
          - type: IoU
            value: 78.533
            name: IoU vineyard
          - type: IoU
            value: 70.751
            name: IoU deciduous
          - type: IoU
            value: 61.192
            name: IoU coniferous
          - type: IoU
            value: 29.189
            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_convnextv2base-unet

Encoder: convnextv2_base
Decoder: upernet
Metrics:

mIoU	O.A.	F-score	Precision	Recall
63.77%	77.03%	76.60%	76.94%	76.67%

Params.: 90.2

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: convnextv2_base-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	63.77%
Overall Accuracy	77.03%
F-score	76.60%
Precision	76.94%
Recall	76.67%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
building	83.50	91.01	92.26	89.79
greenhouse	76.55	86.72	82.49	91.40
swimming pool	59.37	74.51	74.21	74.81
impervious surface	74.84	85.61	85.22	86.00
pervious surface	56.54	72.24	70.62	73.94
bare soil	63.00	77.30	73.17	81.93
water	89.53	94.48	95.19	93.78
snow	67.81	80.81	96.07	69.74
herbaceous vegetation	53.77	69.93	71.69	68.26
agricultural land	57.32	72.87	70.08	75.89
plowed land	34.67	51.49	50.81	52.18
vineyard	78.53	87.98	84.50	91.75
deciduous	70.75	82.87	82.33	83.42
coniferous	61.19	75.92	77.47	74.44
brushwood	29.19	45.19	47.94	42.74

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