metadata

license: etalab-2.0
pipeline_tag: image-segmentation
tags:
  - semantic segmentation
  - pytorch
  - landcover
library_name: pytorch
model-index:
  - name: FLAIR-HUB_LC-L_swinbase-upernet
    results:
      - task:
          type: semantic-segmentation
        dataset:
          name: IGNF/FLAIR-HUB/
          type: earth-observation-dataset
        metrics:
          - type: mIoU
            value: 65.777
            name: mIoU
          - type: OA
            value: 78.238
            name: Overall Accuracy
          - type: IoU
            value: 85.306
            name: IoU building
          - type: IoU
            value: 79.091
            name: IoU greenhouse
          - type: IoU
            value: 61.59
            name: IoU swimming pool
          - type: IoU
            value: 76.579
            name: IoU impervious surface
          - type: IoU
            value: 58.249
            name: IoU pervious surface
          - type: IoU
            value: 64.664
            name: IoU bare soil
          - type: IoU
            value: 90.521
            name: IoU water
          - type: IoU
            value: 73.412
            name: IoU snow
          - type: IoU
            value: 55.133
            name: IoU herbaceous vegetation
          - type: IoU
            value: 58.63
            name: IoU agricultural land
          - type: IoU
            value: 37.548
            name: IoU plowed land
          - type: IoU
            value: 78.56
            name: IoU vineyard
          - type: IoU
            value: 72.26
            name: IoU deciduous
          - type: IoU
            value: 63.548
            name: IoU coniferous
          - type: IoU
            value: 31.121
            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-L_swinbase-upernet

Encoder: swin_base_patch4_window12_384
Decoder: upernet
Metrics:

mIoU	O.A.	F-score	Precision	Recall
65.78%	78.24%	78.15%	78.28%	78.30%

Params.: 276.4

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: 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]
    - SPOT_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]
    - SPOT_RGBI:
        mean: [1137.03, 433.26, 508.75]
        std:  [543.11, 312.76, 284.61]
    - DEM_ELEV:
        means: [311.06, 311.06]
        std: [537.55, 537.55]

Training Data

- Train patches: 152225
- Validation patches: 38175
- Test patches: 50700

Training Logging

Metrics

Metric	Value
mIoU	65.78%
Overall Accuracy	78.24%
F-score	78.15%
Precision	78.28%
Recall	78.30%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
building	85.31	92.07	92.12	92.02
greenhouse	79.09	88.32	84.61	92.38
swimming pool	62.04	76.57	74.86	78.36
impervious surface	76.58	86.74	86.65	86.82
pervious surface	58.25	73.62	72.56	74.71
bare soil	64.66	78.54	75.42	81.92
water	90.52	95.02	95.87	94.20
snow	73.41	84.67	96.42	75.47
herbaceous vegetation	55.13	71.08	72.57	69.64
agricultural land	58.63	73.92	70.75	77.39
plowed land	37.55	54.60	53.74	55.48
vineyard	78.56	87.99	85.42	90.72
deciduous	72.26	83.90	83.69	84.10
coniferous	63.55	77.71	79.25	76.23
brushwood	31.12	47.47	50.24	44.99

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