亟需能够处理各类数据源、其模态以及不同空间与时间分辨率的策略与工具。过去几十年间，深度学习的兴起与计算能力的增长，已彻底改变了遥感（EO）数据的处理方式，其应用涵盖地球系统科学、城市计算、地理空间语义学和遥感等领域。

Strategies and tools are needed that can handle the various data sources, their modalities, and the different spatial and temporal resolutions. The rise of deep learning and the growth of computational power in the past few decades have been game changers in the processing of EO data, with applications in domains such as Earth system science, urban computing, geospatial semantics and remote sensing.