metadata

license: etalab-2.0
pipeline_tag: image-segmentation
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:
          - type: mIoU
            value: 64.162
            name: mIoU
          - type: OA
            value: 77.166
            name: Overall Accuracy
          - type: IoU
            value: 84.153
            name: IoU building
          - type: IoU
            value: 76.218
            name: IoU greenhouse
          - type: IoU
            value: 61.59
            name: IoU swimming pool
          - type: IoU
            value: 75.239
            name: IoU impervious surface
          - type: IoU
            value: 56.174
            name: IoU pervious surface
          - type: IoU
            value: 63.016
            name: IoU bare soil
          - type: IoU
            value: 88.96
            name: IoU water
          - type: IoU
            value: 72.539
            name: IoU snow
          - type: IoU
            value: 54.219
            name: IoU herbaceous vegetation
          - type: IoU
            value: 57.088
            name: IoU agricultural land
          - type: IoU
            value: 36.271
            name: IoU plowed land
          - type: IoU
            value: 77.468
            name: IoU vineyard
          - type: IoU
            value: 71.327
            name: IoU deciduous
          - type: IoU
            value: 60.427
            name: IoU coniferous
          - type: IoU
            value: 29.305
            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.
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: unet
Metrics:

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

Params.: 92.8

General Informations

Contact: [email protected]
Code repository: https://github.com/IGNF/FLAIR-HUB
Paper: https://arxiv.org/abs/2506.07080
Project page: https://ignf.github.io/FLAIR/FLAIR-HUB/flairhub
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-unet
- 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 = {10.48550/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