Update README.md

6741c0e verified 6 months ago

26.1 kB

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
          - name: IoU swimming pool
            type: IoU
            value: 0
          - 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 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		✓	✓		✓	✓	✓

🔍 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

- 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

- 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{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},
}

APA:

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