--- license: etalab-2.0 pipeline_tag: image-segmentation tags: - semantic segmentation - pytorch - landcover library_name: pytorch model-index: - name: FLAIR-HUB_LPIS-A_swinbase-upernet results: - task: type: semantic-segmentation dataset: name: IGNF/FLAIR-HUB/ type: earth-observation-dataset metrics: - type: mIoU value: 22.303 name: mIoU - type: OA value: 86.634 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: 43.419 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-A_swinbase-upernet

--- ## General Informations - **Contact:** flair@ign.fr - **Code repository:** https://github.com/IGNF/FLAIR-HUB - **Paper:** https://arxiv.org/abs/2506.07080 - **Project page:** https://ignf.github.io/FLAIR/FLAIR-HUB/flairhub - **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 - 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 | 22.30% | | Overall Accuracy | 86.63% | | F-score | 31.21% | | Precision | 37.26% | | Recall | 31.06% | | Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) | | --------------------- | ------- | ----------- | ------------- | ---------- | | grasses | 49.37 | 66.10 | 72.82 | 60.53 | | wheat | 34.23 | 51.00 | 41.11 | 67.15 | | barley | 13.13 | 23.21 | 40.73 | 16.23 | | maize | 60.50 | 75.39 | 77.30 | 73.57 | | other cereals | 3.49 | 6.74 | 8.51 | 5.57 | | rice | 0.00 | 0.00 | 0.00 | 0.00 | | flax/hemp/tobacco | 2.71 | 5.27 | 63.81 | 2.75 | | sunflower | 12.59 | 22.36 | 17.40 | 31.26 | | rapeseed | 37.98 | 55.05 | 61.15 | 50.06 | | other oilseed crops | 0.00 | 0.00 | 0.00 | 0.00 | | soy | 0.00 | 0.00 | 0.00 | 0.00 | | other protein crops | 3.05 | 5.93 | 6.82 | 5.24 | | fodder legumes | 13.26 | 23.41 | 33.03 | 18.14 | | beetroots | 53.90 | 70.04 | 64.80 | 76.20 | | potatoes | 7.48 | 13.92 | 11.05 | 18.81 | | other arable crops | 19.74 | 32.97 | 33.93 | 32.07 | | vineyard | 43.42 | 60.55 | 55.72 | 66.29 | | olive groves | 13.55 | 23.87 | 42.01 | 16.67 | | fruits orchards | 36.82 | 53.82 | 51.31 | 56.60 | | nut orchards | 2.87 | 5.59 | 10.36 | 3.83 | | other permanent crops | 14.78 | 25.75 | 66.07 | 15.99 | | mixed crops | 1.49 | 2.93 | 6.75 | 1.87 | | background | 88.61 | 93.96 | 92.41 | 95.56 | --- ## 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 ```