Benchmark and test-set design
Build representative GIS tasks with clear instructions, required inputs, constraints, and expected outputs.
AI evaluation
Plausible-looking GIS output can still be spatially wrong.
Brooks Geospatial designs evaluations that distinguish a convincing answer from an accurate, reproducible, and defensible spatial result.
What evaluation examines
Test real GIS work, not just whether an answer sounds right.
A system can return a polished map, a reasonable place name, or a confident explanation while still using the wrong coordinate system, applying an invalid join, missing records, or producing geometry that does not support the conclusion.
Evaluation makes those conditions visible with realistic tasks, known inputs, expert reference outputs, measurable scoring criteria, and documented failure evidence.
Evaluation practice
Evidence that product, research, and QA teams can inspect.
Expert output review
Review outputs against GIS practice instead of relying on a plausible-looking map or a passing narrative.
Objective rubrics
Define inspectable criteria for correctness, spatial tolerance, schema, provenance, cartography, and instruction following.
Failure taxonomy
Classify error patterns so teams can distinguish geometry, CRS, join, spatial-analysis, and workflow failures.
Regression testing
Turn observed failures into a regression pack that can be rerun after product or model changes.
Deliverables
A complete evaluation package can include:
- 01
Task library
- 02
Inputs
- 03
Reference outputs
- 04
Scoring rubric
- 05
Automated checks where appropriate
- 06
Failure report
- 07
Executive summary
- 08
Regression pack
Start with an evaluation pilot.
A focused task set can surface how a system handles real spatial work before an evaluation expands.