README.md

metadata

license: apache-2.0
pipeline_tag: image-segmentation
tags:
  - pytorch
  - self-supervised
  - transformers
  - multimodal
  - remote sensing
library_name: pytorch
datasets:
  - allenai/s2-naip

We introduce MAESTRO, a tailored adaptation of the Masked Autoencoder (MAE) framework 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 highly competitive on tasks dominated by a single monotemporal modality.

Our 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 our tailored patch-group-wise normalization..

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

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Pre-training Dataset

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🔎 Cross-dataset Evaluation

Benchmark results on 4 datasets :

Model	Pre-training dataset	TreeSatAI-TS	PASTIS-HD	FLAIR#2	FLAIR-HUB
MAESTRO (ours)	FLAIR-HUB	79.6	68.0	-	-
MAESTRO (ours)	S2-NAIP urban	78.8	67.4	62.6	64.6
DINO-v2	LVD-142M	76.7	64.4	64.2	66.0
DINO-v2 sat.	Maxar Vivid2	76.3	64.0	63.5	66.0
DOFA	DOFA MM	76.0	62.9	62.3	65.1
CROMA	SSL4EO	70.5	65.0	39.0	44.3
Prithvi-EO-2.0	HLS	75.6	66.2	41.8	44.9
SatMAE	fMoW RGB+S	76.9	66.6	42.5	45.0

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🚀 Getting Started

First, set up the module with Poetry.

# 1. Change directory
cd MAESTRO

# 2. Install dependencies with Poetry
poetry install

Then, you can start from the following minimal examples.

Intra-dataset MAESTRO on TreeSatAI-TS:

# pre-train, probe and finetune on TreeSatAI-TS
poetry run python main.py \
        model.model=mae model.model_size=medium \
        opt_pretrain.epochs=100 opt_probe.epochs=10 opt_finetune.epochs=50 \
        datasets.name_dataset=treesatai_ts \
        datasets.root_dir=/path/to/dataset/dir datasets.treesatai_ts.rel_dir=TreeSatAI-TS \
        run.exp_dir=/path/to/experiments/dir run.exp_name=mae-m_treesat

Intra-dataset MAESTRO on PASTIS-HD:

# pre-train, probe and finetune on PASTIS-HD
poetry run python main.py \
        model.model=mae model.model_size=medium \
        opt_pretrain.epochs=100 opt_probe.epochs=10 opt_finetune.epochs=50 \
        datasets.name_dataset=pastis_hd \
        datasets.root_dir=/path/to/dataset/dir datasets.pastis_hd.rel_dir=PASTIS-HD \
        run.exp_dir=/path/to/experiments/dir run.exp_name=mae-m_pastis

Intra-dataset MAESTRO on FLAIR-HUB:

# pre-train, probe and finetune on FLAIR-HUB
poetry run python main.py \
        model.model=mae model.model_size=medium \
        opt_pretrain.epochs=100 opt_probe.epochs=15 opt_finetune.epochs=100 \
        datasets.name_dataset=flair \
        datasets.root_dir=/path/to/dataset/dir datasets.flair.rel_dir=FLAIR-HUB \
        run.exp_dir=/path/to/experiments/dir run.exp_name=mae-m_flair

Cross-dataset MAESTRO from S2-NAIP urban to TreeSatAI-TS:

# pre-train on S2-NAIP urban
poetry run python main.py \
        model.model=mae model.model_size=medium \
        opt_pretrain.epochs=15 opt_probe.epochs=0 opt_finetune.epochs=0 \
        datasets.name_dataset=s2_naip \
        datasets.root_dir=/path/to/dataset/dir datasets.s2_naip.rel_dir=s2-naip-urban \
        run.exp_dir=/path/to/experiments/dir run.exp_name=mae-m_s2-naip && \
# probe and finetune on TreeSatAI-TS
poetry run python main.py \
        model.model=mae model.model_size=medium \
        opt_pretrain.epochs=0 opt_probe.epochs=10 opt_finetune.epochs=50 \
        datasets.name_dataset=treesatai_ts \
        datasets.treesatai_ts.aerial.image_size=240 datasets.treesatai_ts.aerial.patch_size.mae=16 \
        datasets.treesatai_ts.s1_asc.name_embed=s1 datasets.treesatai_ts.s1_des.name_embed=s1 \
        datasets.root_dir=/path/to/dataset/dir datasets.treesatai_ts.rel_dir=TreeSatAI-TS \
        run.exp_dir=/path/to/experiments/dir run.load_name=mae-m_s2-naip run.exp_name=mae-m_s2-naip-x-treesat

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Reference

If you use this code, please cite:

@article{labatie2025maestro,
  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},
  journal={arXiv preprint arXiv:2508.10894},
  year={2025}
}

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Acknowledgement

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