---
license: etalab-2.0
pipeline_tag: image-segmentation
tags:
- semantic segmentation
- pytorch
- landcover
library_name: pytorch
model-index:
- name: FLAIR-HUB_LC-A_swinlarge-upernet
results:
- task:
type: semantic-segmentation
dataset:
name: IGNF/FLAIR-HUB/
type: earth-observation-dataset
metrics:
- type: mIoU
value: 61.868
name: mIoU
- type: OA
value: 76.067
name: Overall Accuracy
- type: IoU
value: 83.423
name: IoU building
- type: IoU
value: 75.669
name: IoU greenhouse
- type: IoU
value: 61.59
name: IoU swimming pool
- type: IoU
value: 75.15
name: IoU impervious surface
- type: IoU
value: 56.467
name: IoU pervious surface
- type: IoU
value: 62.006
name: IoU bare soil
- type: IoU
value: 88.373
name: IoU water
- type: IoU
value: 51.815
name: IoU snow
- type: IoU
value: 52.26
name: IoU herbaceous vegetation
- type: IoU
value: 56.434
name: IoU agricultural land
- type: IoU
value: 34.136
name: IoU plowed land
- type: IoU
value: 77.787
name: IoU vineyard
- type: IoU
value: 69.129
name: IoU deciduous
- type: IoU
value: 57.445
name: IoU coniferous
- type: IoU
value: 28.85
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_RVB_swinsmall-upernet
-
Encoder: swin_small_patch4_window7_224
-
Decoder: upernet
-
Metrics:
| mIoU |
O.A. |
F-score |
Precision |
Recall |
| 61.87% |
76.07% |
75.11% |
76.13% |
74.97% |
-
Params.: 50.7
---
## 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: swin_small_patch4_window7_224-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] β weight = 0
- Input channels:
- AERIAL_RGBI : [1,2,3]
- Input normalization (custom):
- AERIAL_RGBI:
mean: [105.66, 111.35, 102.18]
std: [52.23, 45.62, 44.30]
```
---
### Training Data
```yaml
- Train patches: 152225
- Validation patches: 38175
- Test patches: 50700
```
---
### Training Logging
---
## Metrics
| Metric | Value |
| ---------------- | ------ |
| mIoU | 61.87% |
| Overall Accuracy | 76.07% |
| F-score | 75.11% |
| Precision | 76.13% |
| Recall | 74.97% |
| Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) |
| --------------------- | ------- | ----------- | ------------- | ---------- |
| building | 83.42 | 90.96 | 91.14 | 90.79 |
| greenhouse | 75.67 | 86.15 | 84.00 | 88.42 |
| swimming pool | 59.08 | 74.28 | 73.34 | 75.24 |
| impervious surface | 75.15 | 85.81 | 85.80 | 85.82 |
| pervious surface | 56.47 | 72.18 | 71.49 | 72.88 |
| bare soil | 62.01 | 76.55 | 72.14 | 81.53 |
| water | 88.37 | 93.83 | 92.76 | 94.92 |
| snow | 51.82 | 68.26 | 92.98 | 53.93 |
| herbaceous vegetation | 52.26 | 68.65 | 72.10 | 65.51 |
| agricultural land | 56.43 | 72.15 | 67.57 | 77.40 |
| plowed land | 34.14 | 50.90 | 49.04 | 52.90 |
| vineyard | 77.79 | 87.51 | 85.54 | 89.56 |
| deciduous | 69.13 | 81.75 | 80.27 | 83.28 |
| coniferous | 57.44 | 72.97 | 77.92 | 68.61 |
| brushwood | 28.85 | 44.78 | 45.88 | 43.73 |
---
## 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
```
