--- license: etalab-2.0 pipeline_tag: image-segmentation tags: - semantic segmentation - pytorch - landcover library_name: pytorch model-index: - name: FLAIR-HUB_LC-F_swinbase-upernet results: - task: type: semantic-segmentation dataset: name: IGNF/FLAIR-HUB/ type: earth-observation-dataset metrics: - type: mIoU value: 64.868 name: mIoU - type: OA value: 77.685 name: Overall Accuracy - type: IoU value: 84.002 name: IoU building - type: IoU value: 79.283 name: IoU greenhouse - type: IoU value: 61.59 name: IoU swimming pool - type: IoU value: 75.611 name: IoU impervious surface - type: IoU value: 57.694 name: IoU pervious surface - type: IoU value: 63.826 name: IoU bare soil - type: IoU value: 90.461 name: IoU water - type: IoU value: 68.142 name: IoU snow - type: IoU value: 54.871 name: IoU herbaceous vegetation - type: IoU value: 56.893 name: IoU agricultural land - type: IoU value: 37.914 name: IoU plowed land - type: IoU value: 78.144 name: IoU vineyard - type: IoU value: 71.731 name: IoU deciduous - type: IoU value: 63.69 name: IoU coniferous - type: IoU value: 29.627 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-F_swinbase-upernet

--- ## 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_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 : [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): - 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 ```
Classes distribution.
--- ### Training Logging
Training logging.
--- ## Metrics | Metric | Value | | ---------------- | ------ | | mIoU | 64.87% | | Overall Accuracy | 77.69% | | F-score | 77.47% | | Precision | 77.69% | | Recall | 77.73% | | Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) | | --------------------- | ------- | ----------- | ------------- | ---------- | | building | 84.00 | 91.31 | 90.86 | 91.76 | | greenhouse | 79.28 | 88.44 | 85.63 | 91.45 | | swimming pool | 61.13 | 75.88 | 73.91 | 77.95 | | impervious surface | 75.61 | 86.11 | 87.53 | 84.74 | | pervious surface | 57.69 | 73.17 | 71.90 | 74.49 | | bare soil | 63.83 | 77.92 | 73.15 | 83.35 | | water | 90.46 | 94.99 | 95.79 | 94.20 | | snow | 68.14 | 81.05 | 96.94 | 69.64 | | herbaceous vegetation | 54.87 | 70.86 | 71.99 | 69.77 | | agricultural land | 56.89 | 72.52 | 70.36 | 74.82 | | plowed land | 37.91 | 54.98 | 50.82 | 59.88 | | vineyard | 78.14 | 87.73 | 85.52 | 90.05 | | deciduous | 71.73 | 83.54 | 83.31 | 83.77 | | coniferous | 63.69 | 77.82 | 77.95 | 77.68 | | brushwood | 29.63 | 45.71 | 49.63 | 42.36 | --- ## 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 ```