--- license: etalab-2.0 pipeline_tag: image-segmentation tags: - semantic segmentation - pytorch - landcover library_name: pytorch model-index: - name: FLAIR-HUB_LC-L_swinbase-upernet results: - task: type: semantic-segmentation dataset: name: IGNF/FLAIR-HUB/ type: earth-observation-dataset metrics: - type: mIoU value: 65.777 name: mIoU - type: OA value: 78.238 name: Overall Accuracy - type: IoU value: 85.306 name: IoU building - type: IoU value: 79.091 name: IoU greenhouse - type: IoU value: 61.59 name: IoU swimming pool - type: IoU value: 76.579 name: IoU impervious surface - type: IoU value: 58.249 name: IoU pervious surface - type: IoU value: 64.664 name: IoU bare soil - type: IoU value: 90.521 name: IoU water - type: IoU value: 73.412 name: IoU snow - type: IoU value: 55.133 name: IoU herbaceous vegetation - type: IoU value: 58.63 name: IoU agricultural land - type: IoU value: 37.548 name: IoU plowed land - type: IoU value: 78.56 name: IoU vineyard - type: IoU value: 72.26 name: IoU deciduous - type: IoU value: 63.548 name: IoU coniferous - type: IoU value: 31.121 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-L_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 - 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] - DEM_ELEV: means: [311.06, 311.06] std: [537.55, 537.55] ``` --- ### Training Data ```yaml - Train patches: 152225 - Validation patches: 38175 - Test patches: 50700 ```
Classes distribution.
--- ### Training Logging
Training logging.
--- ## Metrics | Metric | Value | | ---------------- | ------ | | mIoU | 65.78% | | Overall Accuracy | 78.24% | | F-score | 78.15% | | Precision | 78.28% | | Recall | 78.30% | | Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) | | --------------------- | ------- | ----------- | ------------- | ---------- | | building | 85.31 | 92.07 | 92.12 | 92.02 | | greenhouse | 79.09 | 88.32 | 84.61 | 92.38 | | swimming pool | 62.04 | 76.57 | 74.86 | 78.36 | | impervious surface | 76.58 | 86.74 | 86.65 | 86.82 | | pervious surface | 58.25 | 73.62 | 72.56 | 74.71 | | bare soil | 64.66 | 78.54 | 75.42 | 81.92 | | water | 90.52 | 95.02 | 95.87 | 94.20 | | snow | 73.41 | 84.67 | 96.42 | 75.47 | | herbaceous vegetation | 55.13 | 71.08 | 72.57 | 69.64 | | agricultural land | 58.63 | 73.92 | 70.75 | 77.39 | | plowed land | 37.55 | 54.60 | 53.74 | 55.48 | | vineyard | 78.56 | 87.99 | 85.42 | 90.72 | | deciduous | 72.26 | 83.90 | 83.69 | 84.10 | | coniferous | 63.55 | 77.71 | 79.25 | 76.23 | | brushwood | 31.12 | 47.47 | 50.24 | 44.99 | --- ## 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 ```