---
license: etalab-2.0
pipeline_tag: image-segmentation
tags:
- semantic segmentation
- pytorch
- landcover
model-index:
- name: FLAIR-HUB_LC-A_convnextv2base-unet
results:
- task:
type: semantic-segmentation
dataset:
name: IGNF/FLAIR-HUB/
type: earth-observation-dataset
metrics:
- type: mIoU
value: 64.162
name: mIoU
- type: OA
value: 77.166
name: Overall Accuracy
- type: IoU
value: 84.153
name: IoU building
- type: IoU
value: 76.218
name: IoU greenhouse
- type: IoU
value: 61.59
name: IoU swimming pool
- type: IoU
value: 75.239
name: IoU impervious surface
- type: IoU
value: 56.174
name: IoU pervious surface
- type: IoU
value: 63.016
name: IoU bare soil
- type: IoU
value: 88.96
name: IoU water
- type: IoU
value: 72.539
name: IoU snow
- type: IoU
value: 54.219
name: IoU herbaceous vegetation
- type: IoU
value: 57.088
name: IoU agricultural land
- type: IoU
value: 36.271
name: IoU plowed land
- type: IoU
value: 77.468
name: IoU vineyard
- type: IoU
value: 71.327
name: IoU deciduous
- type: IoU
value: 60.427
name: IoU coniferous
- type: IoU
value: 29.305
name: IoU brushwood
library_name: pytorch
---
π FLAIR-HUB Model Collection
-
Trained on: FLAIR-HUB dataset
π
-
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
π
Model ID |
πΊοΈ
Land-cover |
πΎ
Crop-types |
π©οΈ
Aerial |
β°οΈ
Elevation |
π°οΈ
SPOT |
π°οΈ
S2 t.s. |
π°οΈ
S1 t.s. |
π©οΈ
Historical |
| LC-A |
β |
|
β |
|
|
|
|
|
| LC-D |
β |
|
β |
|
|
β |
|
|
| LC-F |
β |
|
β |
|
|
β |
β |
|
| LC-G |
β |
|
|
|
|
β |
|
|
| LC-I |
β |
|
|
|
β |
|
|
|
| LC-L |
β |
|
β |
β |
β |
β |
β |
|
| LPIS-A |
|
β |
β |
|
|
|
|
|
| LPIS-F |
|
β |
|
|
|
β |
|
|
| LPIS-I |
|
β |
|
|
β |
β |
β |
|
| LPIS-J |
|
β |
β |
|
β |
β |
β |
|
π Model: FLAIR-HUB_LC-A_convnextv2base-unet
-
Encoder: convnextv2_base
-
Decoder: unet
-
Metrics:
| mIoU |
O.A. |
F-score |
Precision |
Recall |
| 64.16% |
77.17% |
76.92% |
77.58% |
76.59% |
-
Params.: 92.8
---
## General Informations
- **Contact:** flair@ign.fr
- **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
```yaml
- Model architecture: convnextv2_base-unet
- 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] β 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 | 64.13% |
| Overall Accuracy | 77.45% |
| F-score | 76.88% |
| Precision | 77.36% |
| Recall | 76.89% |
| Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) |
| --------------------- | ------- | ----------- | ------------- | ---------- |
| building | 84.15 | 91.39 | 91.15 | 91.64 |
| greenhouse | 76.22 | 86.50 | 84.11 | 89.04 |
| swimming pool | 60.03 | 75.02 | 76.08 | 73.99 |
| impervious surface | 75.24 | 85.87 | 86.75 | 85.01 |
| pervious surface | 56.17 | 71.94 | 69.87 | 74.14 |
| bare soil | 63.02 | 77.31 | 74.19 | 80.71 |
| water | 88.96 | 94.16 | 94.98 | 93.35 |
| snow | 72.54 | 84.08 | 97.77 | 73.76 |
| herbaceous vegetation | 54.22 | 70.31 | 71.67 | 69.01 |
| agricultural land | 57.09 | 72.68 | 69.75 | 75.87 |
| plowed land | 36.27 | 53.23 | 52.71 | 53.77 |
| vineyard | 77.47 | 87.30 | 85.34 | 89.36 |
| deciduous | 71.33 | 83.26 | 81.90 | 84.67 |
| coniferous | 60.43 | 75.33 | 80.13 | 71.08 |
| brushwood | 29.30 | 45.33 | 47.34 | 43.48 |
---
## 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, SΓ©bastien 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, SΓ©bastien 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
```
