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Authoritative knowledge infrastructure for GeoAI: Implications for scientific spatial data providers

Samantha T. Arundel·Lilit Veghiazarian·Torsten Hahmann·Bryan B Campbell
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Abstract

Geospatial Artificial Intelligence (GeoAI) increasingly relies on automated analysis of authoritative geospatial data, exposing limitations in data infrastructures originally designed for simple dissemination rather than for knowledge-driven use. From the perspective of scientific spatial data providers, the challenge is enabling GeoAI responsibly while retaining semantic clarity, provenance, reproducibility, and governance. This short paper (i) examines how GeoAI exposed limitations within existing geospatial data infrastructures, and (ii) identifies provider-side requirements for an authoritative knowledge infrastructure. Drawing on GIScience, scientific workflow, and operational AI literature, the paper outlines implications for national mapping organizations that seek to support advanced analytics without jeopardizing scientific rigor or stewardship responsibilities.

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Samantha T. Arundel, Lilit Veghiazarian, Torsten Hahmann, Bryan B Campbell. 2026-10-07. Authoritative knowledge infrastructure for GeoAI: Implications for scientific spatial data providers. https://doi.org/10.5281/zenodo.23215427

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