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README.md
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library_name: pytorch
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We introduce MAESTRO
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Our contributions are as follows:
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MAESTRO: Novel adaptation of the MAE that combines optimized fusion strategies with our tailored patch-group-wise normalization.
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<div style="position: relative; text-align: center;">
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<img src="./media/Maestro_Overview.png" alt="Classes distribution." style="width: 100%; display: block; margin: 0 auto;"/>
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library_name: pytorch
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datasets:
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- IGNF/FLAIR-HUB
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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.
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Our contributions are as follows:
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- **Extensive benchmarking of multimodal and multitemporal SSL:** Impact evaluation of various fusion strategies for multimodal and multitemporal SSL.
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- **Patch-group-wise normalization:** Novel normalization scheme that normalizes reconstruction targets patch-wise within groups of highly correlated spectral bands.
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- **MAESTRO:** Novel adaptation of the MAE that combines optimized fusion strategies with our tailored patch-group-wise normalization..
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<div style="position: relative; text-align: center;">
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<img src="./media/Maestro_Overview.png" alt="Classes distribution." style="width: 100%; display: block; margin: 0 auto;"/>
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</div>
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