--- license: apache-2.0 pipeline_tag: image-segmentation tags: - pytorch - pytorch lightning - self-supervised learning - masked autoencoders - transformers - remote sensing - earth observation - multimodal - multitemporal library_name: pytorch datasets: - allenai/s2-naip --- ## Download ⚖️ [**Model weights**](./MAESTRO_S2-NAIP-urban_base/checkpoints/pretrain-epoch=14.ckpt)
⚙️ [**Model configuration**](./MAESTRO_S2-NAIP-urban_base/.hydra/config_resolved.yaml)
📂 [**Dataset splits**](https://huggingface.co/IGNF/MAESTRO_S2-NAIP-urban_base/tree/main/dataset_splits)
## 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.
📃 **Paper:** https://arxiv.org/abs/2508.10894
💻 **Code repository:** https://github.com/IGNF/MAESTRO
## Pre-training This model is pre-trained on S2-NAIP urban, an urban subset of [S2-NAIP](https://huggingface.co/datasets/allenai/s2-naip) derived by intersecting the S2-NAIP footprints with the urban set defined in [Zooming-in zooming-out](https://github.com/allenai/satlas-super-resolution). The resulting subset contains 167,397 tiles of size 640 m × 640 m, covering a total area of 68,565 km2 across the continental United States. We retain three distinct modalities: - Aerial NAIP imagery RGB + NIR (1.25 m) - Sentinel-1 time series (mixed ascending and descending orbits) - Sentinel-2 time series During pre-training, we generate surrogate modalities for aerial and SPOT imagery via resampling of NAIP imagery. Below is the reconstruction loss during pre-training on the combined training, validation, and test ensembles, using patch-group-wise normalization and modality-weighted averaging proportional to token counts.
## 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 "spot": - Patch size: 16 - Channels: RED, GREEN, BLUE - Modality "s1": - 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` - Retain separate Sentinel-1 modalities by orbit (if available on the fine-tuning dataset), but use a shared embedding layer initialized from the pre-trained Sentinel-1 layer 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, PASTIS-HD, FLAIR#2, and FLAIR-HUB.

| Model | Pre-training dataset | TreeSatAI-TS | PASTIS-HD | FLAIR#2 | FLAIR-HUB | |--------------------|-----------------------|--------------|-----------|---------|-----------| | 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 |

## 🚀 Getting started Prerequisites: - Clone MAESTRO's [code repository](https://github.com/ignf/maestro) - Fetch [Dataset splits](https://huggingface.co/IGNF/MAESTRO_S2-NAIP-urban_base/tree/main/dataset_splits) and move them to each dataset directory - Fetch [model weights](MAESTRO_S2-NAIP-urban_base/checkpoints/pretrain-epoch=14.ckpt) and move them into `/path/to/experiments/MAESTRO_S2-NAIP-urban_base/checkpoints/` - Fetch [model configuration](MAESTRO_S2-NAIP-urban_base/.hydra/config_resolved.yaml) and move it into `/path/to/experiments/MAESTRO_S2-NAIP-urban_base/.hydra/` The module is setup with [Poetry](https://python-poetry.org/). ```bash # 1. Change directory cd MAESTRO # 2. Install dependencies with Poetry poetry install ``` Pre-training on S2-NAIP urban is performed using: ```bash # batch size 16 on 8 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=15 opt_probe.epochs=0 opt_finetune.epochs=0 \ opt_pretrain.base_lr=1e-5 \ opt_pretrain.batch_size=16 trainer.num_nodes=8 \ datasets.name_dataset=s2_naip \ datasets.s2_naip.filter_inputs=[aerial,spot,s2,s1] \ datasets.s2_naip.crop_meters=120 datasets.s2_naip.grid_pos_enc=192 datasets.s2_naip.repeats=5 \ datasets.s2_naip.aerial.image_size=384 datasets.s2_naip.aerial.patch_size.mae=16 \ datasets.s2_naip.spot.image_size=128 datasets.s2_naip.spot.patch_size.mae=16 \ datasets.s2_naip.s2.image_size=12 datasets.s2_naip.s2.patch_size.mae=2 \ datasets.s2_naip.s1.image_size=12 datasets.s2_naip.s1.patch_size.mae=2 \ 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=MAESTRO_S2-NAIP-urban_base ``` Fine-tuning on TreeSatAI-TS: ```bash # batch size 24 on 1 node with 4 GPUs per node # re-use embeddings' weights with "name_embed" argument # re-use encoder's weights with "name_group" argument # load pre-trained model "MAESTRO_S2-NAIP-urban_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.treesatai_ts.s1_asc.name_embed=s1 datasets.treesatai_ts.s1_des.name_embed=s1 \ datasets.treesatai_ts.s1_asc.name_group=s1 datasets.treesatai_ts.s1_des.name_group=s1 \ 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_S2-NAIP-urban-x-TSAI-TS_base \ run.load_name=MAESTRO_S2-NAIP-urban_base ``` Fine-tuning on PASTIS-HD: ```bash # batch size 12 on 1 node with 4 GPUs per node # re-use embeddings' weights with "name_embed" argument # re-use encoder's weights with "name_group" argument # load pre-trained model "MAESTRO_S2-NAIP-urban_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.pastis_hd.s1_asc.name_embed=s1 datasets.pastis_hd.s1_des.name_embed=s1 \ datasets.pastis_hd.s1_asc.name_group=s1 datasets.pastis_hd.s1_des.name_group=s1 \ 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_S2-NAIP-urban-x-PASTIS-HD_base \ run.load_name=MAESTRO_S2-NAIP-urban_base ``` Fine-tuning on FLAIR#2: ```bash # batch size 6 on 2 nodes with 4 GPUs per node # load pre-trained model "MAESTRO_S2-NAIP-urban_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,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.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_S2-NAIP-urban-x-FLAIR2_base \ run.load_name=MAESTRO_S2-NAIP-urban_base ``` Fine-tuning on FLAIR-HUB: ```bash # batch size 6 on 4 nodes with 4 GPUs per node # re-use embeddings' weights with "name_embed" argument # re-use encoder's weights with "name_group" argument # load pre-trained model "MAESTRO_S2-NAIP-urban_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,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.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.flair.s1_asc.name_embed=s1 datasets.flair.s1_des.name_embed=s1 \ datasets.flair.s1_asc.name_group=s1 datasets.flair.s1_des.name_group=s1 \ 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_S2-NAIP-urban-x-FLAIR-HUB_base \ run.load_name=MAESTRO_S2-NAIP-urban_base ```
## Reference If you use this model, please cite: ```bibtex @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} } ```
## Acknowledgement The experiments in the paper were conducted using HPC/AI resources from GENCI-IDRIS (allocations A0181013803, A0161013803, AD010114597R1, and AD011014690R1).