Toward Foundation Models for Mobility Enriched Geospatially Embedded Objects

Area of Science:

• Geospatial Artificial Intelligence (GeoAI)
• Foundation Models (FMs)
• Machine Learning

Background:

• Foundation models excel in language, vision, and audio but lag in GeoAI.
• GeoAI requires joint reasoning over geospatial objects and human mobility for real-world understanding.
• Current GeoAI lacks unified, transferable representations for geospatially embedded objects (GEOs).

Purpose of the Study:

• To address the bottleneck in GeoAI by developing general-purpose, transferable representations for GEOs.
• To formalize challenges in modeling GEOs, distinct from language modeling.
• To outline research directions for building effective GeoAI foundation models.

Main Methods:

• Analyzing challenges in modeling GEOs: spatial continuity, scale, temporal dynamics, and data sparsity.
• Comparing GEO representation to language tokens, highlighting fundamental differences.
• Identifying representational gaps for behavior-informed GEOs.

Main Results:

• Identified key representational gaps in current GeoAI research.
• Formalized unique challenges in modeling GEOs compared to language data.
• Proposed a research agenda for transferable GEO representations.

Conclusions:

• Unified, transferable representations for GEOs are critical for advancing GeoAI.
• Future GeoAI foundation models should leverage large-scale human mobility and static contextual data.
• Overcoming challenges in spatial continuity, scale, and temporal dynamics is key for generalization.