Spatiotemporal Model Cards Enabling Future-Proof GeoAI Systems

Source abstract

Spatiotemporal machine learning models are increasingly central to geographic information science. Still, despite their growing impact, existing model documentation practices remain insufficient for capturing the spatial, temporal, and contextual dependencies that critically affect model validity, transferability, and responsible use. In this vision paper, we propose Spatiotemporal Model Cards (STMCs), a domain-agnostic extension of the Model Cards paradigm tailored to the unique characteristics of spatiotemporal models. The STMC framework integrates geographic and temporal metadata, autocorrelation-aware evaluation protocols, performance variability across space and time, transferability considerations, and ethical and sustainability aspects. Beyond model documentation, we outline how STMCs can serve as actionable interfaces within future-proof GeoAI systems, supporting model discovery, assessment, and reuse in both catalog-based and agentic (LLM-driven) settings. Using illustrative examples from the mobility domain, we demonstrate how STMCs help surface uncertainty, bias, and contextual limitations that are often obscured by aggregate performance metrics. We conclude by identifying key research challenges and directions for the GIScience community. STMCs provide a foundation for more transparent, reproducible, and responsible GeoAI practices.