Advances in artificial intelligence (AI) and multimodal sensing are driving a paradigm shift in the geospatial sciences, moving from task specific GeoAI models toward general purpose Geospatial Foundation Models (GeoFMs). While these models offer unprecedented opportunities for Earth monitoring, geographic knowledge discovery and addressing societal challenges such as natural disaster management, challenges remain regarding multimodal alignment, spatial reasoning, spatial distribution shifts, and generalizability. This article introduces the first section of a special issue dedicated to advancing the state-of-the-art in GeoFMs and their applications, featuring diverse research on neurosymbolic AI, heterogeneous graph learning, self-supervised learning, spatial retrieval-augmented generation, a genealogical review, and additive compositionality in urban representation learning. Together, these contributions offer new insights toward the next generation of geospatial intelligence.
Gao et al. (Fri,) studied this question.