--- license: etalab-2.0 pipeline_tag: image-segmentation tags: - semantic segmentation - pytorch - landcover library_name: pytorch model-index: - name: FLAIR-HUB_LPIS-J_swinbase-upernet results: - task: type: semantic-segmentation dataset: name: IGNF/FLAIR-HUB/ type: earth-observation-dataset metrics: - type: mIoU value: 32.349 name: mIoU - type: OA value: 87.967 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: 44.552 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-J_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] - 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): - AERIAL_RGBI: mean: [106.59, 105.66, 111.35] std: [39.78, 52.23, 45.62] - 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 ```
Classes distribution.
--- ### Training Logging
Training logging.
--- ## Metrics | Metric | Value | | ---------------- | ------ | | mIoU | 32.35% | | Overall Accuracy | 87.97% | | F-score | 43.04% | | Precision | 50.96% | | Recall | 42.60% | | Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) | | --------------------- | ------- | ----------- | ------------- | ---------- | | grasses | 52.02 | 68.44 | 73.20 | 64.26 | | wheat | 57.45 | 72.97 | 64.92 | 83.30 | | barley | 30.96 | 47.29 | 72.74 | 35.03 | | maize | 78.30 | 87.83 | 84.77 | 91.11 | | other cereals | 8.15 | 15.08 | 18.59 | 12.69 | | rice | 0.00 | 0.00 | 0.00 | 0.00 | | flax/hemp/tobacco | 10.61 | 19.19 | 88.66 | 10.76 | | sunflower | 45.82 | 62.85 | 53.61 | 75.92 | | rapeseed | 71.89 | 83.64 | 82.53 | 84.78 | | other oilseed crops | 0.00 | 0.00 | 0.00 | 0.00 | | soy | 33.68 | 50.38 | 62.54 | 42.19 | | other protein crops | 8.93 | 16.39 | 18.95 | 14.44 | | fodder legumes | 27.19 | 42.76 | 44.10 | 41.50 | | beetroots | 75.31 | 85.91 | 84.14 | 87.77 | | potatoes | 14.37 | 25.13 | 19.45 | 35.48 | | other arable crops | 22.10 | 36.20 | 39.78 | 33.22 | | vineyard | 44.55 | 61.64 | 56.82 | 67.36 | | olive groves | 16.38 | 28.14 | 55.59 | 18.84 | | fruits orchards | 36.57 | 53.55 | 47.56 | 61.27 | | nut orchards | 6.60 | 12.38 | 19.69 | 9.03 | | other permanent crops | 12.12 | 21.62 | 84.16 | 12.40 | | mixed crops | 2.30 | 4.50 | 7.39 | 3.24 | | background | 88.73 | 94.03 | 92.86 | 95.22 | --- ## 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 ```