metadata

license: etalab-2.0
pipeline_tag: image-segmentation
tags:
  - semantic segmentation
  - pytorch
  - landcover
model-index:
  - name: FLAIR-HUB_LPIS-I_swinbase-upernet
    results:
      - task:
          type: semantic-segmentation
        dataset:
          name: IGNF/FLAIR-HUB/
          type: earth-observation-dataset
        metrics:
          - type: mIoU
            value: 35.76
            name: mIoU
          - type: OA
            value: 87.189
            name: Overall Accuracy
          - type: IoU
            value: 83.86
            name: IoU building
          - type: IoU
            value: 78.38
            name: IoU greenhouse
          - type: IoU
            value: 61.59
            name: IoU swimming pool
          - type: IoU
            value: 61.59
            name: IoU impervious surface
          - type: IoU
            value: 57.17
            name: IoU pervious surface
          - type: IoU
            value: 62.94
            name: IoU bare soil
          - type: IoU
            value: 90.35
            name: IoU water
          - type: IoU
            value: 63.38
            name: IoU snow
          - type: IoU
            value: 54.34
            name: IoU herbaceous vegetation
          - type: IoU
            value: 57.14
            name: IoU agricultural land
          - type: IoU
            value: 34.85
            name: IoU plowed land
          - type: IoU
            value: 33.017
            name: IoU vineyard
          - type: IoU
            value: 71.73
            name: IoU deciduous
          - type: IoU
            value: 62.6
            name: IoU coniferous
          - type: IoU
            value: 30.19
            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_LPIS-I_swinbase-upernet

Encoder: swin_base_patch4_window12_384
Decoder: upernet
Metrics:

mIoU	O.A.	F-score	Precision	Recall
35.76%	87.19%	46.66%	52.77%	44.45%

Params.: 97.5

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:
    - 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):
    - SPOT_RGBI:
        mean: [1137.03, 433.26, 508.75]
        std:  [543.11, 312.76, 284.61]

Training Data

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

Training Logging

Metrics

Metric	Value
mIoU	35.76%
Overall Accuracy	87.19%
F-score	46.66%
Precision	52.77%
Recall	44.45%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
grasses	47.65	64.54	68.36	61.13
wheat	65.72	79.32	76.87	81.93
barley	45.99	63.00	69.21	57.82
maize	74.46	85.36	79.16	92.61
other cereals	13.98	24.54	26.33	22.97
rice	0.00	0.00	0.00	0.00
flax/hemp/tobacco	56.98	72.59	85.52	63.06
sunflower	44.07	61.17	62.25	60.14
rapeseed	81.60	89.87	86.69	93.29
other oilseed crops	0.00	0.00	0.00	0.00
soy	51.80	68.24	75.15	62.50
other protein crops	8.65	15.93	18.03	14.26
fodder legumes	28.25	44.05	50.58	39.01
beetroots	75.18	85.83	91.19	81.07
potatoes	7.18	13.41	51.09	7.71
other arable crops	22.77	37.10	32.97	42.41
vineyard	33.02	49.64	58.03	43.37
olive groves	14.16	24.80	25.63	24.02
fruit orchards	27.82	43.53	49.41	38.90
nut orchards	29.83	45.95	68.55	34.56
other permanent crops	0.27	0.53	20.92	0.27
mixed crops	5.49	10.42	25.67	6.53
background	87.62	93.40	92.01	94.84

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