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