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license: etalab-2.0
pipeline_tag: image-segmentation
tags:
  - semantic segmentation
  - pytorch
  - landcover
library_name: pytorch
model-index:
  - name: FLAIR-HUB_LPIS-J_swinbase-upernet
    results:
      - task:
          type: semantic-segmentation
        dataset:
          name: IGNF/FLAIR-HUB/
          type: earth-observation-dataset
        metrics:
          - type: mIoU
            value: 32.349
            name: mIoU
          - type: OA
            value: 87.967
            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: 44.552
            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 task		Model inputs
🆔 Model ID	🗺️ Land-cover	🌾 Crop-types	🛩️ Aerial	⛰️ Elevation	🛰️ SPOT	🛰️ S2 t.s.	🛰️ S1 t.s.	🛩️ Historical
LC-A	✓		✓
LC-B	✓		✓	✓
LC-D	✓		✓			✓
LC-F	✓		✓			✓	✓
LC-G	✓					✓
LC-I	✓				✓
LC-L	✓		✓	✓	✓	✓	✓
LPIS-A		✓	✓
LPIS-F		✓				✓
LPIS-I		✓			✓	✓	✓
LPIS-J		✓	✓		✓	✓	✓

🔍 Model: FLAIR-HUB_LPIS-J_swinbase-upernet

Encoder: swin_base_patch4_window12_384
Decoder: upernet
Metrics:

mIoU	O.A.	F-score	Precision	Recall
32.35%	87.97%	43.04%	50.96%	42.60%

Params.: 186.9
Code: GitHub

General Informations

Contact: flair@ign.fr
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: 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]

Training Data

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

Training Logging

Metrics

Metric	Value
mIoU	32.35%
Overall Accuracy	87.97%
F-score	43.04%
Precision	50.96%
Recall	42.60%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
grasses	52.02	68.44	73.20	64.26
wheat	57.45	72.97	64.92	83.30
barley	30.96	47.29	72.74	35.03
maize	78.30	87.83	84.77	91.11
other cereals	8.15	15.08	18.59	12.69
rice	0.00	0.00	0.00	0.00
flax/hemp/tobacco	10.61	19.19	88.66	10.76
sunflower	45.82	62.85	53.61	75.92
rapeseed	71.89	83.64	82.53	84.78
other oilseed crops	0.00	0.00	0.00	0.00
soy	33.68	50.38	62.54	42.19
other protein crops	8.93	16.39	18.95	14.44
fodder legumes	27.19	42.76	44.10	41.50
beetroots	75.31	85.91	84.14	87.77
potatoes	14.37	25.13	19.45	35.48
other arable crops	22.10	36.20	39.78	33.22
vineyard	44.55	61.64	56.82	67.36
olive groves	16.38	28.14	55.59	18.84
fruits orchards	36.57	53.55	47.56	61.27
nut orchards	6.60	12.38	19.69	9.03
other permanent crops	12.12	21.62	84.16	12.40
mixed crops	2.30	4.50	7.39	3.24
background	88.73	94.03	92.86	95.22

Inference

Aerial ROI

Inference ROI

Cite

BibTeX:

@article{GARIOUD2026271,
title = {FLAIR-HUB: Large-scale multimodal dataset for land cover and crop mapping},
author = {Anatol Garioud and Sébastien Giordano and Nicolas David and Nicolas Gonthier},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {237},
pages = {271-300},
year = {2026},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2026.04.017},
url = {https://www.sciencedirect.com/science/article/pii/S0924271626001899},
}

APA:

Anatol Garioud, Sébastien Giordano, Nicolas David, Nicolas Gonthier. 
FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping.
ISPRS Journal of Photogrammetry and Remote Sensing, Volume 237, 2026.
DOI: https://doi.org/10.1016/j.isprsjprs.2026.04.017