这些约束条件为训练与验证 GeoAI 模型提供了独特机会，从而提升其地理可迁移性。上述要素共同凸显了 GeoAI 的独特性——它不仅是一种人工智能应用，更是一个专门应对地球科学中时空复杂性的学科领域。地球科学领域一项开创性的 GeoAI 建模工作是 Prithvi-EO 的开发；这是一种新型地理空间 AI 基础模型，基于具有国家及全球覆盖范围的时间序列遥感（EO）数据进行训练，旨在提取独特的语义与光谱特征。

These constraints offer a unique opportunity to train and validate GeoAI models, improving their geographical transferability. Together, these elements highlight the uniqueness of GeoAI, not just as an application of AI but as a specialized field that addresses spatial and temporal complexities in Earth science. A pioneering GeoAI modeling effort in the Earth science domain is the development of Prithvi-EO, a new geospatial AI foundation model trained on time-series EO data with both national and global coverage to distill unique semantic and spectral ch