---
license: etalab-2.0
pipeline_tag: image-segmentation
tags:
- semantic segmentation
- pytorch
- landcover
library_name: pytorch
model-index:
- name: FLAIR-HUB_LC-A_swinbase-unet
results:
- task:
type: semantic-segmentation
dataset:
name: IGNF/FLAIR-HUB/
type: earth-observation-dataset
metrics:
- type: mIoU
value: 64.803
name: mIoU
- type: OA
value: 77.93
name: Overall Accuracy
- type: IoU
value: 84.7
name: IoU building
- type: IoU
value: 79.029
name: IoU greenhouse
- type: IoU
value: 61.59
name: IoU swimming pool
- type: IoU
value: 76.228
name: IoU impervious surface
- type: IoU
value: 57.509
name: IoU pervious surface
- type: IoU
value: 64.232
name: IoU bare soil
- type: IoU
value: 90.6
name: IoU water
- type: IoU
value: 63.761
name: IoU snow
- type: IoU
value: 54.897
name: IoU herbaceous vegetation
- type: IoU
value: 58.304
name: IoU agricultural land
- type: IoU
value: 37.635
name: IoU plowed land
- type: IoU
value: 78.314
name: IoU vineyard
- type: IoU
value: 72.073
name: IoU deciduous
- type: IoU
value: 62.519
name: IoU coniferous
- type: IoU
value: 30.084
name: IoU brushwood
---
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_swinbase-unet
-
Encoder: swin_base_patch4_window12_384
-
Decoder: unet
-
Metrics:
| mIoU |
O.A. |
F-score |
Precision |
Recall |
| 64.80% |
77.93% |
77.43% |
78.16% |
77.17% |
-
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: swin_base_patch4_window12_384-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 | 64.80% |
| Overall Accuracy | 77.93% |
| F-score | 77.43% |
| Precision | 78.16% |
| Recall | 77.17% |
| Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) |
| --------------------- | ------- | ----------- | ------------- | ---------- |
| building | 84.70 | 91.72 | 91.98 | 91.46 |
| greenhouse | 79.03 | 88.29 | 85.94 | 90.77 |
| swimming pool | 62.16 | 76.67 | 76.55 | 76.79 |
| impervious surface | 76.23 | 86.51 | 86.75 | 86.28 |
| pervious surface | 57.51 | 73.02 | 70.90 | 75.28 |
| bare soil | 64.23 | 78.22 | 74.68 | 82.12 |
| water | 90.60 | 95.07 | 95.95 | 94.20 |
| snow | 63.76 | 77.87 | 94.88 | 66.03 |
| herbaceous vegetation | 54.90 | 70.88 | 73.05 | 68.84 |
| agricultural land | 58.30 | 73.66 | 70.66 | 76.93 |
| plowed land | 37.64 | 54.69 | 53.87 | 55.53 |
| vineyard | 78.31 | 87.84 | 85.25 | 90.59 |
| deciduous | 72.07 | 83.77 | 81.89 | 85.74 |
| coniferous | 62.52 | 76.94 | 80.55 | 73.64 |
| brushwood | 30.08 | 46.25 | 49.53 | 43.39 |
---
## 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
```
