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IGNF/
FLAIR-HUB_LC-A_IR_convnextv2base-upernet

Name: FLAIR-HUB_LC-A_IR_convnextv2base-upernet
Availability: InStock

pytorch

semantic segmentation

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🌐 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_LC-A_convnextv2base-unet

Encoder: convnextv2_base
Decoder: upernet
Metrics:

mIoU	O.A.	F-score	Precision	Recall
63.77%	77.03%	76.60%	76.94%	76.67%

Params.: 90.2

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

yaml

- Model architecture: convnextv2_base-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]
- Input normalization (custom):
    - AERIAL_RGBI:
        mean: [106.59, 105.66, 111.35]
        std:  [39.78, 52.23, 45.62]

Training Data

yaml

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

Training Logging

Metrics

Metric	Value
mIoU	63.77%
Overall Accuracy	77.03%
F-score	76.60%
Precision	76.94%
Recall	76.67%

Class	IoU (%)	F-score (%)	Precision (%)	Recall (%)
building	83.50	91.01	92.26	89.79
greenhouse	76.55	86.72	82.49	91.40
swimming pool	59.37	74.51	74.21	74.81
impervious surface	74.84	85.61	85.22	86.00
pervious surface	56.54	72.24	70.62	73.94
bare soil	63.00	77.30	73.17	81.93
water	89.53	94.48	95.19	93.78
snow	67.81	80.81	96.07	69.74
herbaceous vegetation	53.77	69.93	71.69	68.26
agricultural land	57.32	72.87	70.08	75.89
plowed land	34.67	51.49	50.81	52.18
vineyard	78.53	87.98	84.50	91.75
deciduous	70.75	82.87	82.33	83.42
coniferous	61.19	75.92	77.47	74.44
brushwood	29.19	45.19	47.94	42.74

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

IGNF/FLAIR-HUB_LC-A_IR_convnextv2base-upernet

🌐 FLAIR-HUB Model Collection

🔍 Model: FLAIR-HUB_LC-A_convnextv2base-unet

General Informations

Training Config Hyperparameters

Training Data

Training Logging

Metrics

Inference

Cite

IGNF/
FLAIR-HUB_LC-A_IR_convnextv2base-upernet