One for All: Toward Unified Foundation Models for Earth Vision

• URL: http://arxiv.org/abs/2401.07527v2
• Date: Tue, 28 May 2024 11:47:35 GMT
• Title: One for All: Toward Unified Foundation Models for Earth Vision
• Authors: Zhitong Xiong, Yi Wang, Fahong Zhang, Xiao Xiang Zhu,
• Abstract summary: Current remote sensing foundation models specialize in a single modality or a specific spatial resolution range. We introduce OFA-Net: employing a single, shared Transformer backbone for multiple data modalities with different spatial resolutions. The proposed method is evaluated on 12 distinct downstream tasks and demonstrates promising performance.
• Score: 24.358013737755822
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models characterized by extensive parameters and trained on large-scale datasets have demonstrated remarkable efficacy across various downstream tasks for remote sensing data. Current remote sensing foundation models typically specialize in a single modality or a specific spatial resolution range, limiting their versatility for downstream datasets. While there have been attempts to develop multi-modal remote sensing foundation models, they typically employ separate vision encoders for each modality or spatial resolution, necessitating a switch in backbones contingent upon the input data. To address this issue, we introduce a simple yet effective method, termed OFA-Net (One-For-All Network): employing a single, shared Transformer backbone for multiple data modalities with different spatial resolutions. Using the masked image modeling mechanism, we pre-train a single Transformer backbone on a curated multi-modal dataset with this simple design. Then the backbone model can be used in different downstream tasks, thus forging a path towards a unified foundation backbone model in Earth vision. The proposed method is evaluated on 12 distinct downstream tasks and demonstrates promising performance.

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