metadata

license: etalab-2.0
pipeline_tag: image-segmentation
tags:
  - semantic segmentation
  - pytorch
  - landcover
library_name: pytorch
model-index:
  - name: FLAIR-HUB_LPIS-A_swinbase-upernet
    results:
      - task:
          type: semantic-segmentation
        dataset:
          name: IGNF/FLAIR-HUB/
          type: earth-observation-dataset
        metrics:
          - type: mIoU
            value: 22.303
            name: mIoU
          - type: OA
            value: 86.634
            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: 43.419
            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

🌐 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-A_swinbase-upernet

Encoder: swin_base_patch4_window12_384
Decoder: upernet
Metrics:

mIoU	O.A.	F-score	Precision	Recall
22.30%	86.63%	31.21%	37.26%	31.06%

Params.: 89.4

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
- 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	22.30%
Overall Accuracy	86.63%
F-score	31.21%
Precision	37.26%
Recall	31.06%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
grasses	49.37	66.10	72.82	60.53
wheat	34.23	51.00	41.11	67.15
barley	13.13	23.21	40.73	16.23
maize	60.50	75.39	77.30	73.57
other cereals	3.49	6.74	8.51	5.57
rice	0.00	0.00	0.00	0.00
flax/hemp/tobacco	2.71	5.27	63.81	2.75
sunflower	12.59	22.36	17.40	31.26
rapeseed	37.98	55.05	61.15	50.06
other oilseed crops	0.00	0.00	0.00	0.00
soy	0.00	0.00	0.00	0.00
other protein crops	3.05	5.93	6.82	5.24
fodder legumes	13.26	23.41	33.03	18.14
beetroots	53.90	70.04	64.80	76.20
potatoes	7.48	13.92	11.05	18.81
other arable crops	19.74	32.97	33.93	32.07
vineyard	43.42	60.55	55.72	66.29
olive groves	13.55	23.87	42.01	16.67
fruits orchards	36.82	53.82	51.31	56.60
nut orchards	2.87	5.59	10.36	3.83
other permanent crops	14.78	25.75	66.07	15.99
mixed crops	1.49	2.93	6.75	1.87
background	88.61	93.96	92.41	95.56

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