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读地球表面的能力。传统模型存在功能孤立的问题，仅针对光学、雷达、高光谱等特定传感器或数据类型及其独特特征进行定制。这种专业化分工阻碍了通过整合异源数据优势实现全面分析的潜力。我们提出的创新方法——动态全能型（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集成到统一框架中。这种动态超网络能够根据波长差异进行实时调整，使单个通用Transformer架构在五种传感器联合训练数据上表现卓越，可完成12项不同地球观测任务（包括预训练阶段未见过的传感器任务）。DOFA的创新设计为更精确、高效、统一的地球观测分析带来突破性进展，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。