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| Model task | Model inputs | |||||||
|---|---|---|---|---|---|---|---|---|
| 🆔 Model ID | 🗺️ Land-cover | 🌾 Crop-types | 🛩️ Aerial | ⛰️ Elevation | 🛰️ SPOT | 🛰️ S2 t.s. | 🛰️ S1 t.s. | 🛩️ Historical |
| LC-A | ✓ | ✓ | ||||||
| LC-B | ✓ | ✓ | ✓ | |||||
| LC-D | ✓ | ✓ | ✓ | |||||
| LC-F | ✓ | ✓ | ✓ | ✓ | ||||
| LC-G | ✓ | ✓ | ||||||
| LC-I | ✓ | ✓ | ||||||
| LC-L | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||
| LPIS-A | ✓ | ✓ | ||||||
| LPIS-F | ✓ | ✓ | ||||||
| LPIS-I | ✓ | ✓ | ✓ | ✓ | ||||
| LPIS-J | ✓ | ✓ | ✓ | ✓ | ✓ |
| mIoU | O.A. | F-score | Precision | Recall |
|---|---|---|---|---|
| 22.30% | 86.63% | 31.21% | 37.26% | 31.06% |
1- Model architecture: swin_base_patch4_window12_384-upernet
2- Optimizer: AdamW (betas=[0.9, 0.999], weight_decay=0.01)
3- Learning rate: 5e-5
4- Scheduler: one_cycle_lr (warmup_fraction=0.2)
5- Epochs: 150
6- Batch size: 5
7- Seed: 2025
8- Early stopping: patience 20, monitor val_miou (mode=max)
9- Class weights:
10 - default: 1.0
11- Input channels:
12 - AERIAL_RGBI : [4,1,2]
13- Input normalization (custom):
14 - AERIAL_RGBI:
15 mean: [106.59, 105.66, 111.35]
16 std: [39.78, 52.23, 45.62]1- Train patches: 152225
2- Validation patches: 38175
3- Test patches: 50700
| 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 |
@article{GARIOUD2026271,
title = {FLAIR-HUB: Large-scale multimodal dataset for land cover and crop mapping},
author = {Anatol Garioud and Sébastien Giordano and Nicolas David and Nicolas Gonthier},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {237},
pages = {271-300},
year = {2026},
issn = {0924-2716},
doi = {https://doi.org/10.1016/j.isprsjprs.2026.04.017},
url = {https://www.sciencedirect.com/science/article/pii/S0924271626001899},
}Anatol Garioud, Sébastien Giordano, Nicolas David, Nicolas Gonthier.
FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping.
ISPRS Journal of Photogrammetry and Remote Sensing, Volume 237, 2026.
DOI: https://doi.org/10.1016/j.isprsjprs.2026.04.017