--- license: etalab-2.0 pipeline_tag: image-segmentation library_name: pytorch tags: - semantic segmentation - pytorch - landcover model-index: - name: FLAIR-HUB_LPIS-F_utae results: - task: type: semantic-segmentation dataset: name: IGNF/FLAIR-HUB/ type: earth-observation-dataset metrics: - type: mIoU value: 21.755 name: mIoU - type: OA value: 85.282 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: 24.517 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 ---

🌐 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_LPIS-F_utae

--- ## 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: UTAE - 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: - SENTINEL2_TS : [1,2,3,4,5,6,7,8,9,10] ``` --- ### Training Data ```yaml - Train patches: 152225 - Validation patches: 38175 - Test patches: 50700 ```
Classes distribution.
--- ### Training Logging
Training logging.
--- ## Metrics | Metric | Value | | ---------------- | ------ | | mIoU | 21.75% | | Overall Accuracy | 85.28% | | F-score | 28.74% | | Precision | 28.98% | | Recall | 31.90% | | Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) | | --------------------- | ------- | ----------- | ------------- | ---------- | | grasses | 43.13 | 60.27 | 64.86 | 56.28 | | wheat | 59.61 | 74.70 | 66.33 | 85.47 | | barley | 48.82 | 65.61 | 73.01 | 59.57 | | maize | 68.82 | 81.53 | 73.99 | 90.78 | | other cereals | 2.60 | 5.08 | 15.86 | 3.02 | | rice | 0.00 | 0.00 | 0.00 | 0.00 | | flax/hemp/tobacco | 0.00 | 0.00 | 0.00 | 0.00 | | sunflower | 27.98 | 43.73 | 48.70 | 39.68 | | rapeseed | 70.93 | 82.99 | 76.64 | 90.49 | | other oilseed crops | 0.00 | 0.00 | 0.00 | 0.00 | | soy | 12.37 | 22.02 | 14.49 | 45.84 | | other protein crops | 20.86 | 34.52 | 27.86 | 45.35 | | fodder legumes | 22.85 | 37.20 | 28.70 | 52.83 | | beetroots | 1.51 | 2.98 | 17.46 | 1.63 | | potatoes | 0.00 | 0.00 | 0.00 | 0.00 | | other arable crops | 10.06 | 18.28 | 13.58 | 27.97 | | vineyard | 24.52 | 39.38 | 37.92 | 40.96 | | olive groves | 0.00 | 0.00 | 0.00 | 0.00 | | fruits orchards | 0.00 | 0.00 | 0.00 | 0.00 | | nut orchards | 0.00 | 0.00 | 0.00 | 0.00 | | other permanent crops | 0.00 | 0.00 | 0.00 | 0.00 | | mixed crops | 0.03 | 0.05 | 15.72 | 0.03 | | background | 86.27 | 92.63 | 91.41 | 93.88 | --- ## Inference

Aerial ROI

AERIAL

Inference ROI

INFERENCE
--- ## Cite **BibTeX:** ``` @article{ign2025flairhub, doi = {10.48550/arXiv.2506.07080}, url = {https://arxiv.org/abs/