The development of foundation models has revolutionized our ability to interpret the Earth's surface using satellite observational data. Traditional models have been siloed, tailored to specific sensors or data types like optical, radar, and hyperspectral, each with its own unique characteristics. This specialization hinders the potential for a holistic analysis that could benefit from the combined strengths of these diverse data sources. Our novel approach introduces the Dynamic One-For-All (DOFA) model, leveraging the concept of neural plasticity in brain science to integrate various data modalities into a single framework adaptively. This dynamic hypernetwork, adjusting to different wavelengths, enables a single versatile Transformer jointly trained on data from five sensors to excel across 12 distinct Earth observation tasks, including sensors never seen during pretraining. DOFA's innovative design offers a promising leap towards more accurate, efficient, and unified Earth observation analysis, showcasing remarkable adaptability and performance in harnessing the potential of multimodal Earth observation data.

翻译：基础模型的发展彻底改变了我们利用卫星观测数据解译地球表面的能力。传统模型各自为政，针对特定传感器或数据类型（如光学、雷达和高光谱）进行定制，每种类型都具有独特特征。这种专业化阻碍了通过整合多种数据源的综合优势实现整体分析的潜力。我们提出的创新方法——动态全能（DOFA）模型——借鉴脑科学中的神经可塑性概念，将多种数据模态自适应地整合到统一框架中。这种动态超网络可根据不同波长进行调整，使单一通用Transformer能够联合训练来自五个传感器的数据，并在12项不同的地球观测任务（包括预训练中从未见过的传感器）中表现出色。DOFA的创新设计为迈向更精准、高效且统一的地球观测分析提供了突破性进展，在挖掘多模态地球观测数据潜力方面展现出卓越的适应性与性能表现。