How Accurate Is AI-Based Feature Extraction?

When is AI-generated geospatial feature extraction accurate enough to become part of an authoritative government GIS?

A model reports F1 = 0.92 and a GIS director hears "92% correct". What the model actually reported is performance on a held-out slice of the same imagery, annotated by the same people, using the same class definitions, at a chosen probability threshold. Change any of those and the number moves; change the county and it can move a lot. The evidence supports a sharp distinction: AI extraction is strong at detection and aggregate measurement, and weak at the two things an authoritative GIS exists for — precise geometry tied to a datum, and stable classification of things whose definition is legal or administrative rather than visual.

This review works through what the four extraction task types actually produce, why benchmark F1, IoU and mAP cannot be converted into an ASPRS positional accuracy class, what independent evidence shows by data source and by feature type, why performance degrades when a model crosses jurisdictions, sensors or seasons, and where human review remains non-negotiable. It closes with a seven-gate framework for admitting an extracted layer into an authoritative GIS, and fourteen procurement questions with the two contract clauses that matter most.