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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 |
|---|---|---|---|---|
| 62.01% | 75.58% | 75.27% | 76.11% | 75.10% |
1- Model architecture: swin_tiny_patch4_window7_224-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 - AERIAL_RGBI : [1,2,3]
14- Input normalization (custom):
15 - AERIAL_RGBI:
16 mean: [105.66, 111.35, 102.18]
17 std: [52.23, 45.62, 44.30]1- Train patches: 152225
2- Validation patches: 38175
3- Test patches: 50700
| Metric | Value |
|---|---|
| mIoU | 62.01% |
| Overall Accuracy | 75.58% |
| F-score | 75.27% |
| Precision | 76.11% |
| Recall | 75.10% |
| Class | IoU (%) | F-score (%) | Precision (%) | Recall (%) |
|---|---|---|---|---|
| building | 82.44 | 90.37 | 90.51 | 90.24 |
| greenhouse | 76.30 | 86.56 | 84.06 | 89.21 |
| swimming pool | 57.97 | 73.39 | 73.87 | 72.92 |
| impervious surface | 74.24 | 85.21 | 85.38 | 85.05 |
| pervious surface | 55.91 | 71.72 | 69.81 | 73.75 |
| bare soil | 62.43 | 76.87 | 73.25 | 80.86 |
| water | 88.03 | 93.63 | 93.18 | 94.09 |
| snow | 60.68 | 75.53 | 94.83 | 62.76 |
| herbaceous vegetation | 51.07 | 67.61 | 71.85 | 63.84 |
| agricultural land | 56.23 | 71.99 | 67.08 | 77.67 |
| plowed land | 33.88 | 50.61 | 50.90 | 50.32 |
| vineyard | 77.53 | 87.35 | 83.85 | 91.14 |
| deciduous | 68.49 | 81.30 | 79.27 | 83.43 |
| coniferous | 55.44 | 71.34 | 79.03 | 65.01 |
| brushwood | 29.47 | 45.52 | 44.80 | 46.27 |
@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