Avid fans of Map Happenings may recall an article titled “ChatGPT (et al) for Geospatial. You Ain’t Seen Nothing… Yet.” That was 18 months ago, which is essentially an epoch in AI terms.
Given it’s been so long I decided it was high time to do a little digging and provide an update on where things stand. Has any progress been made?
If you read the article mentioned above you might remember that back in that infantile period ChatGPT not only failed miserably at basic directions, it couldn’t even do basic maths: 1

Today, for mathematics at least, ChatGPT looks like it’s started to pay a little more attention at school:

But what about geography? Has ChatGPT — or any other so called ‘AI’ for that matter — had any schooling?
Lets Start with the Basics
For the following examples I used ChatGPT. I also tried using Claude: it produced similar results.
My first attempt to poke and prod was to determine if ChatGPT could figure out basic information about addresses and coordinates.

Ok, let’s plot that coordinate on a map:

Oh dear, ChatGPT seems to have the location in the middle of the freeway, about 3.3 miles away from the actual address:

If you read my last missive on the topic you may recall the attempt to use ChatGPT for directions: the result was a hilarious hallucination, resulting in the need for catapults. Has it got any better? Well you won’t need catapults anymore, but you’ll still get hopelessly lost:

The Issue with LLMs
Those who have a smidgen of perspicacity will by now have realized that there is a basic flaw in my poking and prodding LLMs. It is like poking Michelin-star chefs and expecting them to produce the winning goal at the World Cup final. Yes, dear reader, we should focus on the meaning of the acronym ‘LLM’ and, in particular, the second ‘L’ . As I think most of you will know, it stands for ‘language’. LLMs are power-hungry beasts that are designed principally to spit out words — and they’re barely capable of doing anything else.
In order for any geographic intelligence to become apparent in your favorite chatbot it has to get plugged into something that understands concepts beyond human language. ChatGPT started down this path in April 2023 with the very concept of ‘plugins’. Plugins didn’t take off, and instead were quickly replaced with the concept of custom GPTs. One of the first companies to jump on the custom GPT bandwagon was Wolfram. Wolfram is best known for developing Mathematica, a powerful computational software, and perhaps more well known for Wolfram|Alpha, a computational knowledge engine. While signed into ChatGPT you can use Wolfram’s GPT to ask natural language questions regarding just about anything that Wolfram|Alpha can answer.
And it does a better job on geographic basics than vanilla ChatGPT. For example, here is Wolfram GPT’s answer to the question, “what is at 51.50104° N, 0.14207° W?” — it correctly deduces that the coordinate is at Buckingham Palace, whereas ChatGPT thinks the coordinate is about a mile away at Wellington Arch:

Ask Wolfram GPT a question even remotely more complex and it quickly falls over, but at least it doesn’t hallucinate:

Perhaps this is unfair to the primarily mathematical Wolfram GPT. Perhaps instead I should use another custom GPT that is more capable of understanding geography and geospatial functions?
Good luck finding one.
The problem with the search function in ChatGPT’s “GPT Store” is that the search results are singularly execrable — to put it mildly: they are an unfilterable quagmire of useless slop.
If you search for geospatial GPTs in ChatGPT’s GPT store it will come back with a seemingly never ending list, but you’ll quickly find that the vast majority of them are nothing more that textual guides to try to help you navigate Geographically Insidious Systems (‘GIS’). It’s impossible to find the nuggets. And asking ChatGPT directly isn’t much help either.
But we shouldn’t overlook another key issue with trying to use LLMs for geospatial …
They fail to provide a map-centric UI.
But then conversely, if you use traditional consumer or professional geospatial apps, you will find that they lack the deliciousness of a natural language interface that LLMs provide.
You can see this with a simple query to Google Maps where I ask for “grocery stores within 2 miles of 311 E Congress St Tucson, AZ 85701 United States that are open after 8pm”:

Google Maps fails to search on “grocery stores within 2 miles” and “that are open after 8pm”. It can only find the address:

I should also note that if you try the same query on Apple Maps you will find it fairs no better.
But Are There Signs of Progress?
It turns out there are a number of areas where AI is beginning to seep in to the world of geospatial, and it’s just starting to get interesting.
First let’s go back to the Google query and use it to poke once again at ChatGPT:

At first glance: the results look amazing — especially compared to the non-answers from Google Maps and Apple Maps. But dig a little deeper and you’ll find the results aren’t so rosy:
- The first result is acceptable, it is roughly within two miles.
- The second result just does not exist: it’s an empty lot.
- The third result may well be open 24 hours, but it’s over three miles away.
- More importantly perhaps: the results missed an amazing local store called Time Market, which is just one mile away and is open until 10pm.
So lovely, friendly answers, but entirely untrustworthy. For the query above ChatGPT claimed to talk to Bing as well as a bunch of yellow page and grocery store sites. Yellow page sites? Are you surprised it didn’t work?!
Now my tinkering is just that. I am not even scratching the surface. However, it turns out there is a highly respected institution that’s been doing some much more serious digging into the question of geospatial AI. That organization is the Alan Turing Institute who was commissioned to write an independent report on Geospatial AI for Land Use, specifically to “to explore how land use decision making can be supported by geospatial data, including earth observation data, and artificial intelligence”.
In the report they include a section on the value of LLMs:

The point therefore is to make the data more accessible, enabling a non-geospatial analyst to get the answers they’re looking for by simply asking natural language questions . The Turing Institute explored how a map-centric UI with a built in specialized LLM chatbot could help broaden the audience, in their case with an app called ‘Demoland’:

Here is typical conversation with the chatbot in Demoland:
Question: In areas of high deprivation has access to jobs improved?
Answer: In approximately 15.4% of the areas with high levels of deprivation, job accessibility has improved. This suggests that while some progress has been made, there is still a significant proportion of high deprivation areas where job accessibility has not improved. It’s important to continue efforts to improve job accessibility in these areas, as this can play a key role in reducing deprivation.
Question: How has air quality changed around schools?
Answer: Sure, let’s take a look at the changes in air quality around some schools:
- St Joseph’s Roman Catholic Aided Primary School: The air pollution index has increased by 0.1074 units. This school is located in Gateshead.
- Newcastle Bridges School: The air pollution index has increased slightly by 0.0479 units.
- Alternative Education Service – The Beacon Centre: There has been no change in the air pollution index at this location in South Tyneside.
- Yamaha Music School: Similarly, there has been no change in the air pollution index at this location in North Tyneside.
- New Silksworth Academy Junior and Infant: Both these schools, located in Sunderland, have also seen no change in the air pollution index.
Please note that these changes are relative to the current air pollution index at each location.
This certainly seems to be very promising. One of the key recommendations coming out of the report is to develop a set of Geospatial AI Toolkits for LLMs:

Clearly this is precisely what we’re missing from geospatial apps — it’s evidenced not only by my tinkering but by the institutes performing much more serious study. But when those geospatial AI toolkits are integrated into LLMs the results are very promising.
I’m sure there are many organizations, large and small, focused on such integration. One startup I’ve come across that is doing precisely this is Aino. Their natural language interface is used for fairly atomic queries as this point, but it still significantly lowers the barrier to entry relative to a traditional Geographically Insidious System.
Here’s a demo of Aino’s platform being used for site selection (click to play):
But what about the larger organizations? What are they doing?
One notable and recent example of employing AI and natural language interfaces to query geospatial data has been the collaboration between Microsoft and NASA for something Microsoft is calling “Earth Copilot”.
If you follow Microsoft then you’ll know that “Copilot” is the brand they use to indicate integration of AI into their products. You can think of it like their version of “Apple Intelligence”. You might think, therefore, that Earth Copilot is Microsoft’s attempt to turn the whole planet into a computer to answer the Ultimate Question of Life, The Universe, and Everything.
Alas, it’s not quite that bold. But it is designed to help non-geospatial analysts quickly answer questions about the planet from the petabytes of earth observation data that NASA has collected. Here’s Microsoft’s CEO, Satya Nadella, announcing Earth Copilot at Microsoft Ignite 2024 just a few days ago:
Now we all know Microsoft has a tendency to pre-announce something as a product well before it’s generally available, and Earth Copilot is no doubt one of those cases. Nonetheless, the announcement paints a clear vision of what’s (hopefully) coming to all of us.
An American might summarize all this by saying: “We want to democratize access to geospatial data and geospatial systems”.
However, a Brit might simply say: “Let’s make stodgy and dodgy mapping technology work for everyone!”2
Beyond Natural Language Interfaces
When contemplating using AI for geography we shouldn’t be myopic and limit our thinking to the use of LLMs and natural language interfaces. Indeed there are two other important use cases for AI that are worth exploring:
- Use of AI for classification of objects on the ground
- Use of AI for positioning — i.e. locating oneself
Let’s dig into this a little further…
Object Classification
The concept of using AI for object classification is not new. Indeed Google Photos started giving users the ability to search for objects inside photos back in 2015. That’s almost ten years ago. So back then you could attempt to search your photo library for “cats” or “cars” and the app would miraculously list the photos with cats or cars. In 2024 this has now advanced significantly and with the Ask Photos feature you can ask questions like “show me the best photo from each national park I visited”.
Now this approach also works in the geospatial world for identifying objects on the ground. In fact organizations like NASA and the US Department of Defense were experimenting with rudimentary approaches to automatically identify like forests and urban areas back in the 1980s. However, it wasn’t until recently, with the advent of machine learning, neural networks and sheer GPU horsepower that the capability has really taken off.
Obviously there’s a great deal of work going on in this space that we don’t know about. For example, it has tremendous applicability to military applications. So no doubt the likes of Palantir are focused on this problem full bore.
For the rest of us though there’s another company that enabled some enormous progress. And that company is …
Meta.
Meta’s research labs have developed and released something they call “SAM”, otherwise known as the “Segment Anything Model”. By ‘segmenting’ Meta means classifying things or objects that are present in a photographic image. And Meta’s lofty goal is to live up to its tag line: they want to be able to recognize any object or thing in any image.
You can learn a bit more about SAM by visiting the Segment Anything website. Here’s a demo from the site (you may need to tap/click to play):
Why is SAM relevant to geospatial? Well it’s been used by geospatial organizations to interpret aerial and satellite imagery very effectively. One such organization that is successfully employing SAM is that age-old enterprise mapping technology juggernaut: Esri.
Esri’s R&D arm has published something they call “Text SAM” that combines the power of Meta’s SAM with another open source model called “Grounding DINO” which can find objects given a text prompt.3 The results are pretty impressive:



You could argue that Esri has not actually invented anything here. They are merely integrating capabilities developed by other organizations into an Esri workflow. Indeed this may be true, but their work still illustrates great potential.
Imagine if you were to combine this object recognition capability with the power of a natural language interface:
- “Produce a map of all the buildings under construction in the suburbs of Chicago together with an accompanying chart that shows total area under construction by ZIP Code”
- “Compare the tree cover for the years 2016 to 2024 and produce a chart showing changes by county over the years”
Positioning
The challenge of figuring out your location is not new. It’s also a problem that has by no means been solved. In fact with GPS jamming it’s getting worse. BTW: if you’re interested in the history of positioning technology then I recommend you read “12 Map Happenings that Rocked our World: Part 8 — Oh Brother, Where Art Thou?“
I’m sure you’ve all experienced the frustration of trying to locate yourself.
One of the most confounding issues facing anybody who has used a navigation app is “which way do I go first?”. For example, this is particularly perplexing when exiting a subway station: should I start by going left or by going right?
In an effort to solve this problem organizations like Google and Apple have started to provide augmented reality guided solutions to help people get off on the right foot. Basically you are told to hold up your phone and wave it around the buildings in front of you. It will then magically recognize exactly where you are and promptly encourage you to go in a specific direction.
Apple calls this “Visual Localization”, but more recently the industry has been calling this “VPS” which stands for “Visual Positioning System”.
Now it turns out providing VPS requires collecting a tremendous amount of imagery data, and so this feature is generally only available in a few cities around the world.
But there is an organization that is about to change all that.
Their name is Niantic, and they’re probably most famous for building the incredibly popular game: Pokémon GO.
Now what you might not know is that the super smart guys who formed Niantic also invented Google Earth and later made Google Maps what it is today. Niantic started as a separate organization within the greater Google empire back in 2010 and was later spun off as an independent company in 2015.
In 2016 Niantic launched Pokéman GO. This game quickly became the most popular — and most profitable — augmented reality app of all time.
But just a few weeks ago, on November 12, Niantic announced they were pioneering something well beyond a popular game:
They announced that they were pioneering something they call a “Large Geospatial Model”.
In other words Niantic has set out to replace the ‘L’ (for ‘Language’) in LLMs with a ‘G’ (for ‘Geospatial’) to create some called an ‘LGM’.
But what, exactly, does this all mean?
Well to quote from their blog:
At Niantic, we are pioneering the concept of a Large Geospatial Model that will use large-scale machine learning to understand a scene and connect it to millions of other scenes globally.
When you look at a familiar type of structure – whether it’s a church, a statue, or a town square – it’s fairly easy to imagine what it might look like from other angles, even if you haven’t seen it from all sides. As humans, we have “spatial understanding” that means we can fill in these details based on countless similar scenes we’ve encountered before. But for machines, this task is extraordinarily difficult. Even the most advanced AI models today struggle to visualize and infer missing parts of a scene, or to imagine a place from a new angle. This is about to change: Spatial intelligence is the next frontier of AI models.
Even with all the scans that Niantic collects — they have 10 million scanned locations around the world and are receiving about 1 million fresh scans each week, each containing hundreds of discrete images — it’s a drop in the bucket. There is simply not enough data to make VPS pervasive, even with Niantic’s scale.
But that’s where Niantic’s LGM will come in: it will essentially fill in the blanks:
Imagine yourself standing behind a church. Let us assume the closest local model has seen only the front entrance of that church, and thus, it will not be able to tell you where you are. The model has never seen the back of that building. But on a global scale, we have seen a lot of churches, thousands of them, all captured by their respective local models at other places worldwide. No church is the same, but many share common characteristics. An LGM is a way to access that distributed knowledge.
An LGM distills common information in a global large-scale model that enables communication and data sharing across local models. An LGM would be able to internalize the concept of a church, and, furthermore, how these buildings are commonly structured. Even if, for a specific location, we have only mapped the entrance of a church, an LGM would be able to make an intelligent guess about what the back of the building looks like, based on thousands of churches it has seen before. Therefore, the LGM allows for unprecedented robustness in positioning, even from viewpoints and angles that the VPS has never seen.
The global model implements a centralized understanding of the world, entirely derived from geospatial and visual data.
With Niantic LGM the goal is to be able to locate you to within a few centimeters — just about anywhere on the planet.
Essentially it will be your ultimate hack for GeoGuessr … 😱
So What Does The Future Hold?
As is usual for all things AI, things are moving fast. Here are just a few predictions as to where we’ll end up…
Converse With Any Geospatial App
Gone will be the days of brain dead search boxes. You will now expect any mapping app, consumer or professional, to be not only capable of understanding what you type into a text box, but you’ll also expect to be able to have a conversation with it. So answers to questions like the one I tried asking Google Maps above will be answered flawlessly and with the ability to maintain context.
This capability will obviously be of tremendous benefit to the billions of people that use consumer mapping apps every day, but where it’s really going to make a huge difference is in professional apps, in many cases eliminating the need for professionally trained “GIS analysts”.
Now the department or individual that normally has to rely on the geospatial geeks in the basement will be able to get the answers they’re looking for themselves. You only have to look at the queries used in the “Demoland” app that the Alan Turing Institute highlighted to understand where it’s all going.
Gone will be the days of contingent values, complex edges, feature barriers and topological unfathomables. You’ll just ask questions.

For example, I can imagine how a map-centric site selection app might work in this world. No buttons. No pull-downs. No ribbons. Just an interactive map, a query text box and an icon to invoke the microphone:
- “show me the areas in west London that have a median income greater than £65,000 and have a higher density of Millenials”…
- “now show me all available retail locations less than 200 square meters that are within a five minute walk of these areas”…
- “now eliminate all potential locations that are within a three minute walk of a Lululemon store”…
- “ok. now give me a list of those locations together with the contact details of the leasing agent”
Get ready. It’s going to happen.
Locate Me and Orient Me — Immediately, Anywhere
When they work, satellite positioning systems are great. But they don’t work in concrete canyons. And they don’t work indoors. They don’t work underground. They get spoofed. Unless you invest in very expensive equipment, they’re mostly accurate to only few meters. And, when you start your journey, they’re not good at telling you which way to turn.
VPS is beginning to change that. But it relies on pre-scanning the area (outside or inside) to make it all work. And to do that globally, everywhere is plainly not achievable — especially for areas which are not drivable. We need to add some intelligence to fill in the blanks. And it’s organizations like Niantic that are showing the way.
In this new world HD location will be a pervasive entitlement. Not being able to locate yourself precisely, accurately, anywhere — particularly indoors — will be expected. Just like Wi-Fi, it’ll be a new layer in Maslow’s hierarchy of needs.
A World of Geospatial Agents
Now I haven’t mentioned AI agents thus far. But there’s a lot of buzz about them across many companies and industries.4 As you might expect these creatures will be lurking in the background, monitoring, checking, acting on your behalf. You can imagine their use in consumer apps for keeping an eye on on prices, availability, and taking actions when certain specified conditions are met — think reservations for that table at a popular restaurant, or perhaps that amazing Airbnb villa in Tuscany that you’ve always wanted to stay at.
In the professional world it will enable you to be productive in other ways while the geospatial AI agents are doing grunt work on your behalf. In a site selection app, keeping an eye out for properties that meet your requirements. Or in a public safety app, automatically alerting you and others should dangers arise.
Agentive AI for geospatial is going to be huge. And we haven’t even started.
Footnotes
- BTW, with the exception of American spelling this article is written in English, so it’s “maths”, not “math”. Q: did you sadly only study a singular “mathematic” at school? ↩︎
- I can envisage a new marketing campaign for an innovative new mapping startup: “Not stodgy and dodgy — easy peasy, lemon squeezy”. Who says I don’t have a second career as a CMO? ↩︎
- ChatGPT’s comparison of SAM vs. Grounding DINO. ↩︎
- And more only yesterday: Google’s AI enters its ‘agentic era’ ↩︎

































































































































































