--- license: etalab-2.0 pipeline_tag: image-segmentation library_name: pytorch tags: - semantic segmentation - pytorch - landcover model-index: - name: FLAIR-HUB_LC-A_RVB_swinbase-upernet results: - task: type: semantic-segmentation dataset: name: IGNF/FLAIR-HUB/ type: earth-observation-dataset metrics: - type: mIoU value: 64.054 name: mIoU - type: OA value: 76.784 name: Overall Accuracy - type: IoU value: 83.769 name: IoU building - type: IoU value: 77.891 name: IoU greenhouse - type: IoU value: 61.59 name: IoU swimming pool - type: IoU value: 75.029 name: IoU impervious surface - type: IoU value: 56.972 name: IoU pervious surface - type: IoU value: 65.214 name: IoU bare soil - type: IoU value: 90.08 name: IoU water - type: IoU value: 67.767 name: IoU snow - type: IoU value: 52.851 name: IoU herbaceous vegetation - type: IoU value: 56.529 name: IoU agricultural land - type: IoU value: 37.34 name: IoU plowed land - type: IoU value: 78.876 name: IoU vineyard - type: IoU value: 70.071 name: IoU deciduous - type: IoU value: 58.948 name: IoU coniferous - type: IoU value: 30.973 name: IoU brushwood ---

🌐 FLAIR-HUB Model Collection

πŸ†”
Model ID
πŸ—ΊοΈ
Land-cover
🌾
Crop-types
πŸ›©οΈ
Aerial
⛰️
Elevation
πŸ›°οΈ
SPOT
πŸ›°οΈ
S2 t.s.
πŸ›°οΈ
S1 t.s.
πŸ›©οΈ
Historical
LC-A βœ“ βœ“
LC-D βœ“ βœ“ βœ“
LC-F βœ“ βœ“ βœ“ βœ“
LC-G βœ“ βœ“
LC-I βœ“ βœ“
LC-L βœ“ βœ“ βœ“ βœ“ βœ“ βœ“
LPIS-A βœ“ βœ“
LPIS-F βœ“ βœ“
LPIS-I βœ“ βœ“ βœ“ βœ“
LPIS-J βœ“ βœ“ βœ“ βœ“ βœ“

πŸ” Model: FLAIR-HUB_LC-A_RVB_swinbase-upernet

--- ## General Informations - **Contact:** flair@ign.fr - **Code repository:** https://github.com/IGNF/FLAIR-HUB - **Paper:** https://huggingface.co/papers/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_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: - 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 ```
Classes distribution.
--- ### Training Logging
Training logging.
--- ## Metrics | Metric | Value | | ---------------- | ------ | | mIoU | 64.05% | | Overall Accuracy | 76.78% | | F-score | 76.88% | | Precision | 77.71% | | Recall | 76.59% | | Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) | | --------------------- | ------- | ----------- | ------------- | ---------- | | building | 83.77 | 91.17 | 91.42 | 90.92 | | greenhouse | 77.89 | 87.57 | 85.28 | 89.99 | | swimming pool | 58.50 | 73.82 | 77.36 | 70.58 | | impervious surface | 75.03 | 85.73 | 87.13 | 84.38 | | pervious surface | 56.97 | 72.59 | 70.18 | 75.17 | | bare soil | 65.21 | 78.94 | 74.64 | 83.78 | | water | 90.08 | 94.78 | 95.00 | 94.57 | | snow | 67.77 | 80.79 | 97.53 | 68.95 | | herbaceous vegetation | 52.85 | 69.15 | 71.87 | 66.64 | | agricultural land | 56.53 | 72.23 | 68.13 | 76.85 | | plowed land | 37.34 | 54.38 | 51.25 | 57.91 | | vineyard | 78.88 | 88.19 | 86.89 | 89.53 | | deciduous | 70.07 | 82.40 | 81.00 | 83.85 | | coniferous | 58.95 | 74.17 | 79.78 | 69.30 | | brushwood | 30.97 | 47.30 | 48.20 | 46.43 | --- ## Inference

Aerial ROI

AERIAL

Inference ROI

INFERENCE
--- ## 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 ```