AI-Generated GIS Code Can Run Without Errors Yet Deliver Wrong Spatial Results

AI coding agents can generate geospatial code that executes cleanly but produces methodologically incorrect results, a problem distinct from simple API errors. A common example is buffer operations run on datasets using geographic coordinate systems like EPSG:4326, where units are degrees rather than metres, yielding geometries that silently misrepresent real-world distances. Similarly, standard random train-test splits used in spatial machine learning ignore spatial autocorrelation, inflating model accuracy scores that collapse when applied to new regions. Map rendering adds another layer of risk, as visually convincing outputs can mask misregistered rasters, misleading projections, or dropped geometries. Experts argue that defensible GIS workflows require explicit coordinate reference system checks, spatially blocked validation, and domain-aware guardrails that go beyond what general-purpose AI agents currently apply by default.