MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and
  Multispectral Earth Observation Data
Self-supervised learning holds great promise for remote sensing, but standard
self-supervised methods must be adapted to the unique characteristics of Earth
observation data. We take a step in this direction by conducting a
comprehensive benchmark of fusion strategies and reconstruction target
normalization schemes for multimodal, multitemporal, and multispectral Earth
observation data. Based on our findings, we propose MAESTRO, a novel adaptation
of the Masked Autoencoder, featuring optimized fusion strategies and a tailored
target normalization scheme that introduces a spectral prior as a
self-supervisory signal. Evaluated on four Earth observation 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
mono-temporal modality. Code to reproduce all our experiments is available at
https://github.com/ignf/maestro.
68a2def4a4caabb4320e64ad
Antoine Labatie
68a2def4a4caabb4320e64ae
Michael Vaccaro
68a2def4a4caabb4320e64af
Nina Lardiere
68a2def4a4caabb4320e64b0
Anatol Garioud
claimed_verified
2025-08-22T07:24:45.689Z
6537c9477f862897572902f8
https://cdn-avatars.huggingface.co/v1/production/uploads/6537c9477f862897572902f8/QztGliJ4Ozsqm560GLPzw.png
Anatol Garioud
user
AGarioud
68a2def4a4caabb4320e64b1
Nicolas Gonthier
MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and
  Multispectral Earth Observation Data
Self-supervised learning holds great promise for remote sensing, but standard
self-supervised methods must be adapted to the unique characteristics of Earth
observation data. We take a step in this direction by conducting a
comprehensive benchmark of fusion strategies and reconstruction target
normalization schemes for multimodal, multitemporal, and multispectral Earth
observation data. Based on our findings, we propose MAESTRO, a novel adaptation
of the Masked Autoencoder, featuring optimized fusion strategies and a tailored
target normalization scheme that introduces a spectral prior as a
self-supervisory signal. Evaluated on four Earth observation 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
mono-temporal modality. Code to reproduce all our experiments is available at
https://github.com/ignf/maestro.
MAESTRO, an adapted Masked Autoencoder with optimized fusion strategies and spectral prior normalization, achieves state-of-the-art performance on multitemporal Earth observation tasks.
self-supervised learning Masked Autoencoder fusion strategies reconstruction target normalization multimodal multitemporal multispectral Earth observation spectral prior self-supervisory signal