SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing

• URL: http://arxiv.org/abs/2412.19237v2
• Date: Sun, 20 Apr 2025 00:08:58 GMT
• Title: SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing
• Authors: Xuyang Li, Chenyu Li, Gemine Vivone, Danfeng Hong,
• Abstract summary: Remote Sensing (RS) data encapsulates rich multi-dimensional information essential for Earth observation.<n>Existing Visual Foundation Models (VFMs) serve as powerful feature extractors, leveraging extensive RS data for pretraining and subsequent fine-tuning.<n>We introduce SeaMo, a novel VFM that effectively integrates multimodal and multi-seasonal RS information.
• Score: 26.830180880225566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote Sensing (RS) data encapsulates rich multi-dimensional information essential for Earth observation. Its vast volume, diverse sources, and temporal continuity make it particularly well-suited for developing large Visual Foundation Models (VFMs). These models serve as powerful feature extractors, leveraging extensive RS data for pretraining and subsequent fine-tuning in various geoscientific applications. However, existing VFMs in the RS domain often concentrate on specific image characteristics, neglecting the full season-aware potential of RS data. To bridge this gap, we introduce SeaMo, a novel VFM that effectively integrates multimodal and multi-seasonal RS information. SeaMo leverages a masked image modeling framework to fully exploit the spatial, spectral, and seasonal dimensions of RS data. Specifically, we employ unaligned spatial region selection to capture spatial heterogeneity, incorporate multi-source inputs for enhanced multimodal integration, and introduce temporal-multimodal fusion blocks to assimilate seasonal variations effectively. By explicitly modeling the complex, season-dependent attributes of RS data, SeaMo enhances generalization, robustness, and adaptability across geoscientific tasks. Extensive experiments and ablation studies demonstrate its superior performance, underscoring its potential as a foundational model for Earth observation.

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