GeoAI: Understanding The New Grammar - ARKives

GeoAI: Understanding The New Grammar

Published: 22 July, 2026

11 mins(s) read

Tags:

#Geospatial

#Technology

#AI

Series:

Decoding GeoAI

Posts from "Decoding GeoAI" series:

GeoAI: Understanding The New Grammar

GeoAI: Making Sense of a New Language

Table of contents:

This is the second part of the Decoding GeoAI series, an attempt to make sense of that new AI language. It’s meant to explain some buzzwords in the modern AI era of Geospatial, not a deep dive into the AI concepts, more of a beginner’s glossary.

License: This article is licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

Preface

In the first part of this series, GeoAI: Making Sense of a New Language, I went through some of the new words that have become part of the GeoAI vocabulary, Machine Learning, Computer Vision, Spatial Intelligence, and how each helps machines learn from data, understand imagery, and reason about the world around us. Understanding those terms is a good starting point, but words alone rarely tell the whole story.

A language is more than its vocabulary, and modern AI is more than a collection of models and algorithms. The real shift is in how these ideas connect and build on one another.

To understand why today’s AI feels fundamentally different from the AI many of us have used for years, we need to understand its new grammar.

What Changed?

Machine Learning has been around for decades. Computer Vision isn’t new either. Yet in the last couple of years, AI has suddenly become capable of holding conversations, generating reports, writing code, creating images, and solving problems that felt out of reach not long ago.

The answer doesn’t lie in a single breakthrough. It comes from a handful of ideas that have gradually reshaped modern AI, and the biggest shift among them is the move from task-specific Classical AI to general-purpose Generative AI, made possible by concepts like Foundation Models and Embeddings.

From Classical AI to Generative AI

Today, when someone says AI, the first thing that comes to mind is usually ChatGPT or something like it. We ask it questions, generate reports, write code, create images, brainstorm ideas. That’s introduced a new phrase into the vocabulary: Generative AI.

Which raises an obvious question. Wasn’t the AI we were already using also AI?

Yes.

The AI used for image classification, object detection, route optimisation, recommendation systems, anomaly detection, and countless geospatial workflows is usually called Classical AI (or Traditional AI). These systems are built to do one specific job. Given an input, they learn patterns and produce a prediction, classification, or decision, nothing more, nothing less. Their objective is narrowly defined by the task they were trained to solve.

Let’s continue with the building mapping example from the previous article. A classical AI model might look at an image and answer one specific question, “Is there a building here?” Or maybe, “Draw the footprint of every building in this image.” The output is focused, deterministic, and tied to exactly the task the model was trained for.

Generative AI works differently. Instead of being limited to one predefined question, it can interact with the same data in far more flexible ways. Instead of only identifying or classifying information, it can generate new content based on what it has learned.

Same underlying data, two different ways of working with it.

Using the same building dataset, a Generative AI system could answer questions like:

Summarise the pattern of urban growth in this city.

Describe the differences between these two neighbourhoods.

Generate a report highlighting areas where building density has increased significantly.

Explain which regions may need additional public infrastructure based on recent development.

The underlying geospatial data hasn’t changed. What’s changed is how we interact with it. Instead of just asking a model to recognise patterns, we can now ask it to reason about them, explain them, summarise them, and communicate them in plain language.

At this point, a natural question comes up. Where did all this information actually come from? If a Generative AI system can summarise urban growth or explain changes in a city, someone (or something) still has to identify the buildings, measure the changes, and produce the underlying data first.

In most GeoAI systems, that’s still the job of Classical AI and geospatial analytics. Computer Vision detects the buildings, Machine Learning identifies the patterns, GIS runs the spatial analysis, statistical models quantify the change. Generative AI builds on top of all of that, helping us explore, explain, and communicate the results in natural language.

Generative AI sits on top of Classical AI, not in place of it. One helps us...