MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks

• URL: http://arxiv.org/abs/2505.14951v1
• Date: Tue, 20 May 2025 22:24:36 GMT
• Title: MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks
• Authors: Jose Sosa, Danila Rukhovich, Anis Kacem, Djamila Aouada,
• Abstract summary: This paper explores a more flexible multi-modal, multi-task pre-training strategy for Earth Observation (EO) data.<n>Specifically, we adopt a Multi-modal Multi-task Masked Autoencoder (MultiMAE) that we pre-train by reconstructing diverse input modalities.<n>Our approach exhibits significant flexibility, handling diverse input configurations without requiring modality-specific pre-trained models.
• Score: 11.359741665798195
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-modal data in Earth Observation (EO) presents a huge opportunity for improving transfer learning capabilities when pre-training deep learning models. Unlike prior work that often overlooks multi-modal EO data, recent methods have started to include it, resulting in more effective pre-training strategies. However, existing approaches commonly face challenges in effectively transferring learning to downstream tasks where the structure of available data differs from that used during pre-training. This paper addresses this limitation by exploring a more flexible multi-modal, multi-task pre-training strategy for EO data. Specifically, we adopt a Multi-modal Multi-task Masked Autoencoder (MultiMAE) that we pre-train by reconstructing diverse input modalities, including spectral, elevation, and segmentation data. The pre-trained model demonstrates robust transfer learning capabilities, outperforming state-of-the-art methods on various EO datasets for classification and segmentation tasks. Our approach exhibits significant flexibility, handling diverse input configurations without requiring modality-specific pre-trained models. Code will be available at: https://github.com/josesosajs/multimae-meets-eo.

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