How to evaluate geospatial AI for city planning

Two AI systems can produce equally readable recommendations about a city while using very different evidence. One may calculate an indicator from a suitable dataset. Another may describe a plausible pattern without checking whether it exists.

A geospatial benchmark needs to distinguish those behaviors. LGM’s Geospatial & City Planning Benchmark offering is designed around evaluating the complete analytical path, not just the fluency of the final answer.

Begin with the decisions users actually make

A useful task set reflects a defined audience and territory. A planning team might compare service coverage, inspect land-use constraints or prioritize areas for infrastructure assessment. An agent platform might need to verify that its model can select and call spatial tools correctly.

Each task needs a clear success condition. “Discuss accessibility” is difficult to score consistently. “Identify facilities inside a supplied boundary and report the source date” has inspectable requirements.

Include cases where the correct outcome is to acknowledge insufficient evidence. A system that always produces a confident recommendation is not necessarily more capable.

Test several layers of correctness

Evaluation should distinguish:

1. Task interpretation: did the system use the requested territory, units and constraints?
2. Evidence selection: were the sources relevant, sufficiently current and appropriate in scale?
3. Tool execution: were the operations and parameters valid, and were errors handled?
4. Spatial output: were the geometry, attributes and calculations correct for the task?
5. Explanation: did the narrative match the actual results and disclose limitations?

This separation matters because different failures need different remedies. Better wording will not repair a wrong boundary. A stronger model cannot recover local information that the available sources never contained.

Build references that can be inspected

Some tasks can be checked against a known geometry set or independently calculated quantity. Others involve planning judgment and need an expert-reviewed rubric rather than a single supposedly perfect answer.

For those tasks, evaluate whether the system recognizes relevant constraints, presents defensible alternatives and distinguishes assumptions from observations. Document where reviewers disagree.

Regional evaluation should also include realistic place names, languages and administrative conventions. Understanding a translated question does not establish that a system understands the local planning context.

Compare systems under declared conditions

A model with access to detailed local data is not directly comparable to one answering from text alone. Record the model version, tool access, dataset versions, prompts and resource limits for each run.

If production workflows vary between runs, repeat representative tasks and report the variation. Measure latency and resource use alongside quality, while specifying what the measurement includes.

Keep development examples separate from the held-out tasks used to assess improvement. Otherwise, a better score may reflect familiarity with the test rather than a capability that transfers to new questions.

Turn failures into an improvement roadmap

The output of an evaluation should explain what to change. Missing regional evidence suggests a data problem. Repeated parameter errors suggest work on tool definitions, routing or the harness. Unsupported conclusions may require stronger verification and clearer stopping behavior.

Fine-tuning is one possible research direction, not the default answer to every failure. Improvements should be tested against the same baseline and checked for regressions elsewhere.

LGM’s benchmark offering is a scoped R&D engagement with a report, reusable evaluation assets and recommendations. It does not imply an already published leaderboard or proven superiority over particular models. Its purpose is to create evidence for the next product and engineering decision.