--- license: etalab-2.0 tags: - semantic segmentation - pytorch - landcover model-index: - name: FLAIR-HUB_LC-G_utae results: - task: type: semantic-segmentation dataset: name: IGNF/FLAIR-HUB/ type: earth-observation-dataset metrics: - name: mIoU type: mIoU value: 34.239 - name: Overall Accuracy type: OA value: 57.826 - name: IoU building type: IoU value: 34.908 - name: IoU greenhouse type: IoU value: 0.0 - name: IoU swimming pool type: IoU value: 61.59 - name: IoU impervious surface type: IoU value: 38.267 - name: IoU pervious surface type: IoU value: 27.432 - name: IoU bare soil type: IoU value: 33.594 - name: IoU water type: IoU value: 65.32 - name: IoU snow type: IoU value: 67.543 - name: IoU herbaceous vegetation type: IoU value: 34.435 - name: IoU agricultural land type: IoU value: 42.083 - name: IoU plowed land type: IoU value: 10.228 - name: IoU vineyard type: IoU value: 41.105 - name: IoU deciduous type: IoU value: 55.992 - name: IoU coniferous type: IoU value: 48.219 - name: IoU brushwood type: IoU value: 14.462 pipeline_tag: image-segmentation ---

🌐 FLAIR-HUB Model Collection

Trained on: FLAIR-HUB dataset 🔗
Available modalities: Aerial images, SPOT images, Topographic info, Sentinel-2 yearly time-series, Sentinel-1 yearly time-series, Historical aerial images
Encoders: ConvNeXTV2, Swin (Tiny, Small, Base, Large)
Decoders: UNet, UPerNet
Tasks: Land-cover mapping (LC), Crop-type mapping (LPIS)
Class nomenclature: 15 classes for LC, 23 classes for LPIS

🆔 Model ID	🗺️ Land-cover	🌾 Crop-types	🛩️ Aerial	⛰️ Elevation	🛰️ SPOT	🛰️ S2 t.s.	🛰️ S1 t.s.
LC-A	✓		✓
LC-D	✓		✓			✓
LC-F	✓		✓			✓	✓
LC-G	✓					✓
LC-I	✓				✓
LC-L	✓		✓	✓	✓	✓	✓
LPIS-A		✓	✓
LPIS-F		✓				✓
LPIS-I		✓			✓	✓	✓
LPIS-J		✓	✓		✓	✓	✓

🔍 Model: FLAIR-HUB_LC-G_utae

Encoder: UTAE
Decoder: UTAE
Metrics:

mIoU	O.A.	F-score	Precision	Recall
34.24%	57.83%	47.30%	48.12%	47.59%

Params.: 0.9

--- ## 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: 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] → 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 ```

--- ### Training Logging

--- ## Metrics | Metric | Value | | ---------------- | ------ | | mIoU | 34.24% | | Overall Accuracy | 57.83% | | F-score | 47.30% | | Precision | 48.12% | | Recall | 47.59% | | Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) | | --------------------- | ------- | ----------- | ------------- | ---------- | | building | 34.91 | 51.75 | 54.37 | 49.37 | | greenhouse | 0.00 | 0.00 | 0.00 | 0.00 | | swimming pool | 0.00 | 0.00 | 0.00 | 0.00 | | impervious surface | 38.27 | 55.35 | 51.43 | 59.92 | | pervious surface | 27.43 | 43.05 | 51.24 | 37.12 | | bare soil | 33.59 | 50.29 | 56.33 | 45.42 | | water | 65.32 | 79.02 | 71.19 | 88.79 | | snow | 67.54 | 80.63 | 69.71 | 95.61 | | herbaceous vegetation | 34.44 | 51.23 | 51.81 | 50.66 | | agricultural land | 42.08 | 59.24 | 57.01 | 61.65 | | plowed land | 10.23 | 18.56 | 19.29 | 17.88 | | vineyard | 41.10 | 58.26 | 67.59 | 51.20 | | deciduous | 55.99 | 71.79 | 67.97 | 76.06 | | coniferous | 48.22 | 65.06 | 77.39 | 56.12 | | brushwood | 14.46 | 25.27 | 26.54 | 24.12 | Selection deleted --- ## Inference

Aerial ROI

Inference ROI

--- ## Cite **BibTeX:** ``` @article{ign2025flairhub, doi = {10.48550/arXiv.2506.07080}, url = {https://arxiv.org/abs/2506.07080}, author = {Garioud, Anatol and Giordano, Sébastien 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, Sébastien 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 ```