--- license: etalab-2.0 pipeline_tag: image-segmentation tags: - semantic segmentation - pytorch - landcover library_name: pytorch model-index: - name: FLAIR-HUB_LC-A_convnextv2tiny-upernet results: - task: type: semantic-segmentation dataset: name: IGNF/FLAIR-HUB/ type: earth-observation-dataset metrics: - type: mIoU value: 62.725 name: mIoU - type: OA value: 76.434 name: Overall Accuracy - type: IoU value: 82.565 name: IoU building - type: IoU value: 75.292 name: IoU greenhouse - type: IoU value: 61.59 name: IoU swimming pool - type: IoU value: 73.768 name: IoU impervious surface - type: IoU value: 55.136 name: IoU pervious surface - type: IoU value: 60.195 name: IoU bare soil - type: IoU value: 88.596 name: IoU water - type: IoU value: 64.808 name: IoU snow - type: IoU value: 53.2 name: IoU herbaceous vegetation - type: IoU value: 55.828 name: IoU agricultural land - type: IoU value: 35.343 name: IoU plowed land - type: IoU value: 76.054 name: IoU vineyard - type: IoU value: 70.93 name: IoU deciduous - type: IoU value: 60.604 name: IoU coniferous - type: IoU value: 29.504 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_convnextv2tiny-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: convnextv2_tiny-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] - 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 | 62.73% | | Overall Accuracy | 76.43% | | F-score | 75.87% | | Precision | 76.22% | | Recall | 75.72% | | Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) | | --------------------- | ------- | ----------- | ------------- | ---------- | | building | 82.57 | 90.45 | 90.96 | 89.94 | | greenhouse | 75.29 | 85.90 | 83.32 | 88.66 | | swimming pool | 59.06 | 74.26 | 75.33 | 73.23 | | impervious surface | 73.77 | 84.90 | 85.42 | 84.40 | | pervious surface | 55.14 | 71.08 | 70.18 | 72.00 | | bare soil | 60.20 | 75.15 | 72.82 | 77.64 | | water | 88.60 | 93.95 | 95.09 | 92.85 | | snow | 64.81 | 78.65 | 87.46 | 71.45 | | herbaceous vegetation | 53.20 | 69.45 | 70.50 | 68.43 | | agricultural land | 55.83 | 71.65 | 69.34 | 74.12 | | plowed land | 35.34 | 52.23 | 50.89 | 53.63 | | vineyard | 76.05 | 86.40 | 84.28 | 88.62 | | deciduous | 70.93 | 82.99 | 81.83 | 84.19 | | coniferous | 60.60 | 75.47 | 78.67 | 72.52 | | brushwood | 29.50 | 45.56 | 47.15 | 44.09 | --- ## 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 ```