MAESTRO_FLAIR-HUB_base

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⚖️ Model weights <br> ⚙️ Model configuration <br> 📂 Dataset splits <br>

Abstract

MAESTRO is a tailored adaptation of the Masked Autoencoder (MAE) that effectively orchestrates the use of multimodal, multitemporal, and multispectral Earth Observation (EO) data. Evaluated on four EO datasets, MAESTRO sets a new state-of-the-art on tasks that strongly rely on multitemporal dynamics, while remaining competitive on tasks dominated by a single monotemporal modality.

MAESTRO's contributions are as follows:

• Extensive benchmarking of multimodal and multitemporal SSL: Impact evaluation of various fusion strategies for multimodal and multitemporal SSL.
• Patch-group-wise normalization: Novel normalization scheme that normalizes reconstruction targets patch-wise within groups of highly correlated spectral bands.
• MAESTRO: Novel adaptation of the MAE that combines optimized fusion strategies with patch-group-wise normalization.

<div style="position: relative; text-align: center;"> <img src="./media/Maestro_Overview.png" style="width: 100%; display: block; margin: 0 auto;"/> </div>

📃 Paper: https://arxiv.org/abs/2508.10894 <br> 💻 Code repository: https://github.com/IGNF/MAESTRO <br>

Pre-training

This model is pre-trained on FLAIR-HUB.

FLAIR-HUB contains 241,100 tiles of size 102.4 × 102.4 m, covering a total area of 2,528 km² across France.

We retain six distinct modalities:

• Aerial imagery RGB + NIR (0.2 m resolution)
• DEM/DSM imagery (0.2 m resolution)
• SPOT 6–7 imagery
• Sentinel-1 time series in ascending orbit
• Sentinel-1 time series in descending orbit
• Sentinel-2 time series

Below is the reconstruction loss during pre-training on the combined training and validation ensembles, using patch-group-wise normalization and modality-weighted averaging proportional to token counts.

<div style="position: relative; text-align: center;"> <img src="./media/train_rec_loss_maeflair.png" style="width: 100%; display: block; margin: 0 auto;"/> </div> <hr>

Fine-tuning

For optimal fine-tuning results with this model:

• Ensure that patch sizes and channels match between pre-training and fine-tuning for each modality:
  • Modality "aerial":
    • Patch size: 16
    • Channels: NIR, RED, GREEN, BLUE
  • Modality "dem":
    • Patch size: 32
    • Channels: DEM, DSM
  • Modality "spot":
    • Patch size: 16
    • Channels: RED, GREEN, BLUE
  • Modality "s1_asc":
    • Patch size: 2
    • Channels: VV, VH
  • Modality "s1_des":
    • Patch size: 2
    • Channels: VV, VH
  • Modality "s2":
    • Patch size: 2
    • Channels: B02, B03, B04, B05, B06, B07, B08, B8A, B11, B12
• Use fixed cross-dataset grids for positional encodings proportional to ground sampling distance: grid_pos_enc ≈ 1.6 * crop_meters

Note that modality names must match between pre-training and fine-tuning.

Below are cross-dataset evaluation results obtained with these guidelines on TreeSatAI-TS and PASTIS-HD.

<p align="center"> </p>

🚀 Getting started

Prerequisites:

• Clone MAESTRO's code repository
• Fetch Dataset splits and move them to each dataset directory
• Fetch model weights and move them into /path/to/experiments/MAESTRO_FLAIR-HUB_base/checkpoints/
• Fetch model configuration and move it into /path/to/experiments/MAESTRO_FLAIR-HUB_base/.hydra/

The module is setup with Poetry.

# 1. Change directory
cd MAESTRO

# 2. Install dependencies with Poetry
poetry install

Pre-training on FLAIR-HUB is performed using:

# batch size 9 on 4 nodes with 4 GPUs per node
poetry run python main.py \
  model.model=mae model.model_size=medium \
  model.fusion_mode=group model.inter_depth=3 \
  opt_pretrain.epochs=100 opt_probe.epochs=0 opt_finetune.epochs=0 \
  opt_pretrain.batch_size=9 trainer.num_nodes=4 \
  datasets.name_dataset=flair \
  datasets.flair.filter_inputs=[aerial,dem,spot,s2,s1_asc,s1_des] \
  datasets.flair.crop_meters=102.4 datasets.flair.grid_pos_enc=160 \
  datasets.flair.aerial.image_size=512 datasets.flair.aerial.patch_size.mae=16 \
  datasets.flair.dem.image_size=512 datasets.flair.dem.patch_size.mae=32 \
  datasets.flair.spot.image_size=128 datasets.flair.spot.patch_size.mae=16 datasets.flair.spot.bands=3 \
  datasets.flair.s2.image_size=10 datasets.flair.s2.patch_size.mae=2 \
  datasets.flair.s1_asc.image_size=10 datasets.flair.s1_asc.patch_size.mae=2 \
  datasets.flair.s1_des.image_size=10 datasets.flair.s1_des.patch_size.mae=2 \
  datasets.root_dir=/path/to/dataset/dir datasets.flair.csv_dir=/path/to/dataset/dir/FLAIR-HUB datasets.flair.rel_dir=FLAIR-HUB \
  run.exp_dir=/path/to/experiments/dir run.exp_name=MAESTRO_FLAIR-HUB_base \

Fine-tuning on TreeSatAI-TS:

# batch size 24 on 1 node with 4 GPUs per node
# load pre-trained model "MAESTRO_FLAIR-HUB_base"
poetry run python main.py \
  model.model=mae model.model_size=medium \
  model.fusion_mode=group model.inter_depth=3 \
  opt_pretrain.epochs=0 opt_probe.epochs=10 opt_finetune.epochs=50 \
  opt_probe.batch_size=24 opt_finetune.batch_size=24 trainer.num_nodes=1 \
  opt_finetune.monitor=treesat_mlc_thresh/weighted_f1_val \
  datasets.name_dataset=treesatai_ts \
  datasets.treesatai_ts.filter_inputs=[aerial,s2,s1_asc,s1_des] \
  datasets.treesatai_ts.crop_meters=60 datasets.treesatai_ts.grid_pos_enc=96 \
  datasets.treesatai_ts.aerial.image_size=240 datasets.treesatai_ts.aerial.patch_size.mae=16 \
  datasets.treesatai_ts.s2.image_size=6 datasets.treesatai_ts.s2.patch_size.mae=2 \
  datasets.treesatai_ts.s1_asc.image_size=6 datasets.treesatai_ts.s1_asc.patch_size.mae=2 \
  datasets.treesatai_ts.s1_des.image_size=6 datasets.treesatai_ts.s1_des.patch_size.mae=2 \
  datasets.root_dir=/path/to/dataset/dir datasets.treesatai_ts.rel_dir=TreeSatAI-TS \
  run.exp_dir=/path/to/experiments/dir run.exp_name=MAESTRO_FLAIR-HUB-x-TSAI-TS_base \
  run.load_name=MAESTRO_FLAIR-HUB_base

Fine-tuning on PASTIS-HD:

# batch size 12 on 1 node with 4 GPUs per node
# load pre-trained model "MAESTRO_FLAIR-HUB_base"
poetry run python main.py \
  model.model=mae model.model_size=medium \
  model.fusion_mode=group model.inter_depth=3 \
  opt_pretrain.epochs=0 opt_probe.epochs=10 opt_finetune.epochs=50 \
  opt_probe.batch_size=12 opt_finetune.batch_size=12 trainer.num_nodes=1 \
  opt_finetune.monitor=pastis_seg/average_iou_val \
  datasets.name_dataset=pastis_hd \
  datasets.pastis_hd.filter_inputs=[spot,s2,s1_asc,s1_des] \
  datasets.pastis_hd.crop_meters=160 datasets.pastis_hd.grid_pos_enc=256 datasets.pastis_hd.repeats=8 \
  datasets.pastis_hd.spot.image_size=160 datasets.pastis_hd.spot.patch_size.mae=16 \
  datasets.pastis_hd.s2.image_size=16 datasets.pastis_hd.s2.patch_size.mae=2 \
  datasets.pastis_hd.s1_asc.image_size=16 datasets.pastis_hd.s1_asc.patch_size.mae=2 \
  datasets.pastis_hd.s1_des.image_size=16 datasets.pastis_hd.s1_des.patch_size.mae=2 \
  datasets.root_dir=/path/to/dataset/dir datasets.pastis_hd.rel_dir=PASTIS-HD \
  run.exp_dir=/path/to/experiments/dir run.exp_name=MAESTRO_FLAIR-HUB-x-PASTIS-HD_base \
  run.load_name=MAESTRO_FLAIR-HUB_base

Fine-tuning on FLAIR#2:

# batch size 6 on 2 nodes with 4 GPUs per node
# load pre-trained model "MAESTRO_FLAIR-HUB_base"
poetry run python main.py \
  model.model=mae model.model_size=medium \
  model.fusion_mode=group model.inter_depth=3 \
  opt_pretrain.epochs=0 opt_probe.epochs=15 opt_finetune.epochs=100 \
  opt_probe.batch_size=6 opt_finetune.batch_size=6 trainer.num_nodes=2 \
  opt_finetune.monitor=cosia/average_iou_val \
  datasets.name_dataset=flair \
  datasets.flair.version=flair2 \
  datasets.flair.filter_inputs=[aerial,dem,s2] \
  datasets.flair.crop_meters=102.4 datasets.flair.grid_pos_enc=160 \
  datasets.flair.aerial.image_size=512 datasets.flair.aerial.patch_size.mae=16 \
  datasets.flair.dem.image_size=512 datasets.flair.dem.patch_size.mae=32 \
  datasets.flair.s2.image_size=10 datasets.flair.s2.patch_size.mae=2 \
  datasets.root_dir=/path/to/dataset/dir datasets.flair.csv_dir=/path/to/dataset/dir/FLAIR-HUB datasets.flair.rel_dir=FLAIR-HUB \
  run.exp_dir=/path/to/experiments/dir run.exp_name=MAESTRO_FLAIR-HUB-x-FLAIR2_base \
  run.load_name=MAESTRO_FLAIR-HUB_base

Fine-tuning on FLAIR-HUB:

# batch size 6 on 4 nodes with 4 GPUs per node
# load pre-trained model "MAESTRO_FLAIR-HUB_base"
poetry run python main.py \
  model.model=mae model.model_size=medium \
  model.fusion_mode=group model.inter_depth=3 \
  opt_pretrain.epochs=0 opt_probe.epochs=15 opt_finetune.epochs=100 \
  opt_probe.batch_size=6 opt_finetune.batch_size=6 trainer.num_nodes=4 \
  opt_finetune.monitor=cosia/average_iou_val \
  datasets.name_dataset=flair \
  datasets.flair.filter_inputs=[aerial,dem,s2,s1_asc,s1_des] \
  datasets.flair.crop_meters=102.4 datasets.flair.grid_pos_enc=160 \
  datasets.flair.aerial.image_size=512 datasets.flair.aerial.patch_size.mae=16 \
  datasets.flair.dem.image_size=512 datasets.flair.dem.patch_size.mae=32 \
  datasets.flair.s2.image_size=10 datasets.flair.s2.patch_size.mae=2 \
  datasets.flair.s1_asc.image_size=10 datasets.flair.s1_asc.patch_size.mae=2 \
  datasets.flair.s1_des.image_size=10 datasets.flair.s1_des.patch_size.mae=2 \
  datasets.root_dir=/path/to/dataset/dir datasets.flair.csv_dir=/path/to/dataset/dir/FLAIR-HUB datasets.flair.rel_dir=FLAIR-HUB \
  run.exp_dir=/path/to/experiments/dir run.exp_name=MAESTRO_FLAIR-HUB-x-FLAIR-HUB_base \
  run.load_name=MAESTRO_FLAIR-HUB_base

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Reference

If you use this model, please cite:

@inproceedings{labatie2026maestro,
  title={MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data},
  author={Labatie, Antoine and Vaccaro, Michael and Lardiere, Nina and Garioud, Anatol and Gonthier, Nicolas},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2026}
}

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Acknowledgement

The experiments in the paper were conducted using HPC/AI resources from GENCI-IDRIS (allocations A0181013803, A0161013803, AD010114597R1, and AD011014690R1).