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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 |
|---|---|---|---|---|
| 35.76% | 87.19% | 46.66% | 52.77% | 44.45% |
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 - masked classes: [clear cut, ligneous, mixed, other] → weight = 0
12- Input channels:
13 - SPOT_RGBI: [4, 1, 2]
14 - SENTINEL2_TS: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
15 - SENTINEL1-ASC_TS: [1, 2]
16 - SENTINEL1-DESC_TS: [1, 2]
17- Input normalization (custom):
18 - SPOT_RGBI:
19 mean: [1137.03, 433.26, 508.75]
20 std: [543.11, 312.76, 284.61]1- Train patches: 152225
2- Validation patches: 38175
3- Test patches: 50700
| Metric | Value |
|---|---|
| mIoU | 35.76% |
| Overall Accuracy | 87.19% |
| F-score | 46.66% |
| Precision | 52.77% |
| Recall | 44.45% |
| Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) |
|---|---|---|---|---|
| grasses | 47.65 | 64.54 | 68.36 | 61.13 |
| wheat | 65.72 | 79.32 | 76.87 | 81.93 |
| barley | 45.99 | 63.00 | 69.21 | 57.82 |
| maize | 74.46 | 85.36 | 79.16 | 92.61 |
| other cereals | 13.98 | 24.54 | 26.33 | 22.97 |
| rice | 0.00 | 0.00 | 0.00 | 0.00 |
| flax/hemp/tobacco | 56.98 | 72.59 | 85.52 | 63.06 |
| sunflower | 44.07 | 61.17 | 62.25 | 60.14 |
| rapeseed | 81.60 | 89.87 | 86.69 | 93.29 |
| other oilseed crops | 0.00 | 0.00 | 0.00 | 0.00 |
| soy | 51.80 | 68.24 | 75.15 | 62.50 |
| other protein crops | 8.65 | 15.93 | 18.03 | 14.26 |
| fodder legumes | 28.25 | 44.05 | 50.58 | 39.01 |
| beetroots | 75.18 | 85.83 | 91.19 | 81.07 |
| potatoes | 7.18 | 13.41 | 51.09 | 7.71 |
| other arable crops | 22.77 | 37.10 | 32.97 | 42.41 |
| vineyard | 33.02 | 49.64 | 58.03 | 43.37 |
| olive groves | 14.16 | 24.80 | 25.63 | 24.02 |
| fruit orchards | 27.82 | 43.53 | 49.41 | 38.90 |
| nut orchards | 29.83 | 45.95 | 68.55 | 34.56 |
| other permanent crops | 0.27 | 0.53 | 20.92 | 0.27 |
| mixed crops | 5.49 | 10.42 | 25.67 | 6.53 |
| background | 87.62 | 93.40 | 92.01 | 94.84 |
@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