---
license: etalab-2.0
pipeline_tag: image-segmentation
tags:
- semantic segmentation
- pytorch
- landcover
model-index:
- name: FLAIR-HUB_LPIS-I_swinbase-upernet
results:
- task:
type: semantic-segmentation
dataset:
name: IGNF/FLAIR-HUB/
type: earth-observation-dataset
metrics:
- type: mIoU
value: 35.76
name: mIoU
- type: OA
value: 87.189
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: 33.017
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
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_LPIS-I_swinbase-upernet
-
Encoder: swin_base_patch4_window12_384
-
Decoder: upernet
-
Metrics:
| mIoU |
O.A. |
F-score |
Precision |
Recall |
| 35.76% |
87.19% |
46.66% |
52.77% |
44.45% |
-
Params.: 97.5
---
## 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-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:
- 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):
- SPOT_RGBI:
mean: [1137.03, 433.26, 508.75]
std: [543.11, 312.76, 284.61]
```
---
### Training Data
```yaml
- Train patches: 152225
- Validation patches: 38175
- Test patches: 50700
```
---
### Training Logging
---
## Metrics
| Metric | Value |
| ---------------- | ------ |
| mIoU | 35.76% |
| Overall Accuracy | 87.19% |
| F-score | 46.66% |
| Precision | 52.77% |
| Recall | 44.45% |
| Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) |
| --------------------- | ------- | ----------- | ------------- | ---------- |
| grasses | 47.65 | 64.54 | 68.36 | 61.13 |
| wheat | 65.72 | 79.32 | 76.87 | 81.93 |
| barley | 45.99 | 63.00 | 69.21 | 57.82 |
| maize | 74.46 | 85.36 | 79.16 | 92.61 |
| other cereals | 13.98 | 24.54 | 26.33 | 22.97 |
| rice | 0.00 | 0.00 | 0.00 | 0.00 |
| flax/hemp/tobacco | 56.98 | 72.59 | 85.52 | 63.06 |
| sunflower | 44.07 | 61.17 | 62.25 | 60.14 |
| rapeseed | 81.60 | 89.87 | 86.69 | 93.29 |
| other oilseed crops | 0.00 | 0.00 | 0.00 | 0.00 |
| soy | 51.80 | 68.24 | 75.15 | 62.50 |
| other protein crops | 8.65 | 15.93 | 18.03 | 14.26 |
| fodder legumes | 28.25 | 44.05 | 50.58 | 39.01 |
| beetroots | 75.18 | 85.83 | 91.19 | 81.07 |
| potatoes | 7.18 | 13.41 | 51.09 | 7.71 |
| other arable crops | 22.77 | 37.10 | 32.97 | 42.41 |
| vineyard | 33.02 | 49.64 | 58.03 | 43.37 |
| olive groves | 14.16 | 24.80 | 25.63 | 24.02 |
| fruit orchards | 27.82 | 43.53 | 49.41 | 38.90 |
| nut orchards | 29.83 | 45.95 | 68.55 | 34.56 |
| other permanent crops | 0.27 | 0.53 | 20.92 | 0.27 |
| mixed crops | 5.49 | 10.42 | 25.67 | 6.53 |
| background | 87.62 | 93.40 | 92.01 | 94.84 |
---
## 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
```
