How do we prepare our data for a world of AI?

Artificial intelligence is now part of everyday life. It is being used in countless situations at work and at home, with the promise of helping people and organisations work more efficiently.

As AI becomes more embedded in geospatial workflows, organisations are beginning to expose their data to AI models. This creates significant opportunities, but it also brings risks that should not be underestimated.

The key question is no longer whether geospatial data should be exposed to AI. It is how this can be done responsibly, securely and effectively.

Is our geospatial data AI-ready?

The old phrase “rubbish in, rubbish out” is especially relevant when exposing geospatial data to AI. Issues such as incomplete, inconsistent, or poorly described data do not disappear. In many cases, they are magnified. AI will not automatically correct poor data. It can scale its impact.

This matters because geospatial data is highly dependent on context. It can represent an infinite number of locations, different spatial extents, coordinate reference systems, and points in time. Without that context, the meaning and usefulness of the data can quickly become unclear.

AI systems are very good at identifying patterns, but they do not have true semantic understanding. For example, they may not understand the nuances of a road classification system, or the difference between current and historical features. Spatial relationships between features are also difficult to define without clear semantic meaning.

The world around us is varied and complex, which makes it difficult to describe and document. Yet this is exactly what training data needs to do within an AI system. There is no substitute for high-quality, representative training data if AI is to be used effectively.

We also need to describe the provenance and lineage of geospatial data in a precise way. If we know where data came from, how it was created and how it has been processed, we can provide the context needed to build trust.

How to improve your data

What can be done to improve data quality, better describe context, minimise bias, and strengthen trust?

The evidence is clear: AI depends on high-quality, structured data. This is particularly true for geospatial data.

This is where geospatial data standards have an important role to play. The ISO 19100 series of standards is well established in describing metadata, providing models for data quality and defining semantic meaning.

These standards help ensure that geospatial data is consistent, reproducible, and auditable. In turn, this supports trust in the data and in the outputs produced from it.

There has also been growing interest in metadata frameworks, such as ISO 19115, for capturing provenance and trust information. As AI technologies have evolved, metadata standards have also developed, increasing their ability to serve both people and machines.

Recent work on geospatial data standards has also focused on improving the consistency and reliability of training data used in AI development. This is an important step. If training data is inconsistent, incomplete or poorly documented, the AI systems that depend on it will inherit those weaknesses.

Exposing geospatial data to AI is not inherently risky. The risk comes from doing so without adequate preparation, particularly where data quality, context, bias and trust are not actively managed.

The takeaway is, if we want AI to support better decisions, we need to prepare the data first. That means investing in quality, structure, provenance, standards and governance.

For geospatial data, this is not just a technical exercise. It is how we make sure AI can use location data responsibly and effectively. Get the foundations right, and AI can help unlock new insight. Get them wrong, and the outputs may be faster, but not necessarily better.

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