There’s a phrase that’s been rattling around the industry for years: “deriving insights from imagery.” We’ve seen it in keynotes, in pitch decks, in white papers. For the longest time, it meant that either an expert was looking at images to derive those insights or using machine learning models to derive it for others to digest.

That may finally be changing. A recent announcement from Google, the launch of Aerial and Satellite Insights under its Imagery Insights portfolio, built in partnership with Vexcel and Airbus is one of the clearest signals yet that we’re crossing a genuine inflection point.

Vexcel Studio’s AI-powered semantic search interface lets users query real-world features in plain language. (Source: Vexcel)

I had the chance to sit down with Erik Jorgensen, Chairman and CEO of Vexcel, to dig into what this partnership actually means not just for Vexcel, but for the broader community. Here’s what stood out.

We’ve Been Calling It “Geospatial AI” for Years. This Is Different.

Here’s something Erik said that stuck with me. As an industry, we’ve been throwing around the term “Geospatial AI” for years but if we’re honest, most of what we meant was machine learning and computer vision. Companies would pick a vertical, define a set of real-world attributes (roof material, swimming pools, trampolines), label tens of thousands of examples, and train a model to find those specific things. Powerful, but narrow.

What’s happening now is a different animal entirely. The idea is that instead of pixels, you’re generating something closer to brain cells, a vectorised, semantically-rich representation of the real world that an AI engine can actually reason against. And that changes the fundamental question from “what has the model been trained to find?” to “what do you want to know?”

Think of it as Control+F for the physical world. Using zero-shot and open vocabulary analysis, you can now ask an AI agent to find “all wind turbines in this region” or locate “a river running through a city centre” without any prior training; just a plain-language description. Semantic change detection becomes possible too: distinguishing a long-standing factory from a newer one, or tracking a project from pre-construction through active build phases. We’ve been talking about this kind of capability for decades. It is genuinely starting to exist now.

This isn’t a conceptual idea anymore, by enabling natural language queries against high-resolution imagery, Vexcel has effectively commercialized this “Control+F for the physical world” paradigm for its users (Read more).

A plain-language query for “sites that appear abandoned” returns ranked, confidence-scored results — no training data required. (Source: Vexcel)

Erik gave me a real-world example that I thought was particularly compelling. Traditionally, when a major natural event happens (a hurricane, a flood) teams run computer vision classifiers against build structures to identify damage. Useful, but you’re limited to what the model was trained to see. In today’s world of agentic AI, you can ask “show me all road debris blocking transportation routes” without any prior training at all. Recovery teams get deployed faster. The same logic applies to a utility running an agentic AI across thousands of cooling towers looking for signs of corrosion or rust that used to require expensive on-site surveys.

 A single natural-language query surfaces dozens of matching facilities across locations — the same logic that lets an agent scan thousands of assets at once. (Source: Vexcel Studio)

That’s not an incremental improvement. That’s a different world.

Of course, the underlying data challenge remains a hurdle that requires addressing, and we’ll dive into that exact topic in the following section.

Why the Data Underneath This Matters Enormously

None of this works without reliable ground truth. And this is where I think Vexcel’s positioning is genuinely interesting because they’re not just a data company, they’re a hardware company. They design and manufacture their own sensor systems, which means the imagery they collect in Cape Town or Rio de Janeiro is spectrally and geometrically consistent with what they’re collecting in Berlin or Washington, D.C. That global consistency is what makes it trustworthy fuel for AI reasoning at scale.

There’s also a metadata dimension to this that often gets underplayed. Knowing when the data was collected is not a nice-to-have. An insurance underwriter doing a risk assessment cannot be making decisions based on imagery that’s two years old. Timestamps, provenance, consistency these are the guardrails that prevent an AI from hallucinating against the physical world.

Erik was also clear about something I think is worth spelling out: the remote sensing ecosystem doesn’t have one winner here. Drones are great for small-area, high-frequency monitoring e.g daily progress tracking on a construction site. Satellites dominate where you need planetary-scale coverage like tracking deforestation in the Amazon. Vexcel’s fixed-wing aerial program sits squarely in the middle: the coverage breadth you’d need to map an entire continental road network for autonomous vehicle development, at the resolution you’d actually need to reason against it. Different tools, different jobs.

There’s a Model Garden Now And It Works on Your Data Too

One detail in Google’s announcement that I think deserves more attention: the launch of Aerial and Satellite Models (Experimental) within Google Cloud’s Model Garden, developed through Google Research’s Remote Sensing Foundation effort.

The key point here is that these models are not restricted to Google’s own imagery catalog. Enterprises can bring their own proprietary aerial or satellite data and apply Google’s advanced AI to extract insights from imagery they already own then there is no need to build or maintain complex models from scratch. For any organisation sitting on years of accumulated aerial surveys, that’s a significant unlock that most people haven’t quite processed yet.

Solar Insights: This Is What Problem-First Geospatial Looks Like

One of the announcements in Google’s blog that caught my attention immediately was Solar Insights (Experimental): a high-resolution, per-building solar potential dataset covering over 90% of buildings in the US and Europe. It integrates roof statistics, existing solar array data, and weather models to predict rooftop solar contributions at the building level.

This is exactly the kind of thing we started Geoawesome to talk about. The Milken Institute’s Community Infrastructure Center is already using it to assess rooftop solar potential for community resilience centers e.g. libraries, schools, clinics and layering it with climate risk and demographic data to identify where investments matter most in underserved communities. Director Rachel Halfaker described it as being able to evaluate solar potential for any community anchor facility in minutes. That’s geospatial technology solving a real problem for real people.

LiDAR Street Insights

Google is also adding LiDAR data to Street View Insights, enabling precise measurements — utility pole heights, overhead line clearances, road sign dimensions through natural language queries, all without leaving your desk.

I mentioned to Erik that I’d recently spoken with a German utility company still flying helicopters with complicated sensor payloads to monitor their infrastructure. The regulatory environment for drones hasn’t caught up yet, which I get. But the combination of tools like Street View Insights with LiDAR, alongside the kind of consistent aerial data Vexcel provides, is rapidly making the helicopter approach look like something from another era. The question is whether those companies know it yet.

Where Things Are Heading: AI Marketplaces and the End of Ortho-Only

When I asked Erik where the next two to three years take us, he flagged two things.

First: the push toward 360-degree vantage points. Most historical analytics have been built on ortho mosaics because they were simply the easiest format to work with. But AI can now reason against multi-vantage-point data; oblique views, vertical views, all of it vectorised and processed simultaneously. You’re not just seeing the AC units on the rooftop anymore; you see the whole building from every angle. Sensor innovation will follow that capability.

Second: the explosion of AI marketplaces. The model is shifting from “download data, build a pipeline” to agent-to-agent interfaces where an enterprise gets a direct answer to a complex question without ever touching a pixel. That’s a fundamental shift in who the customer is and what they’re buying and it drives a move toward token-based or value-based pricing rather than data-by-the-square-kilometre. Vexcel is leaning into this with active engagement in the Overture Maps Foundation and integration with BigQuery and Earth Engine to make their data accessible across enterprise platforms.

The Awareness Problem Is Still the Hardest Problem

Here’s the part of the conversation that I find most grounding. Vexcel just launched in India and Mexico, bringing their footprint to 47 countries. There are still regulatory challenges in some markets; certain governments restrict aerial imagery collection in ways that end up isolating them from the benefits of global AI platforms.

Erik made a point at the Geospatial World Forum in Amsterdam recently that I thought was well put: you can have sovereignty over your destiny without requiring proprietary data that locks out global participants. Countries that restrict access end up being only as capable as their own domestic geospatial industry which may be exactly the opposite of what they want.

But honestly, the biggest friction isn’t regulatory. It’s awareness. Having previously worked for a public sector transportation company, I know firsthand how difficult it is to change established business logic, even when a new approach could save millions. Many organisations still don’t know that a consistent, high-resolution aerial imagery library exists off the shelf. They assume it’s too expensive, can’t find it, or just keep doing what they’ve always done because the process works well enough and nobody has ever shown them there’s a better way (one that actually works for their problem).

Erik’s example of being able to instantly map spare railroad ties along a rail corridor — something the railways apparently don’t even have as a clean vector dataset today — and how easy and cheap that now is with agentic AI? That landed for me. That’s not science fiction. That’s today. And most of the potential customers don’t know it yet.

That’s also why partnerships like the one with Google matter so much strategically. Google has the reach and the distribution to put these capabilities in front of enterprise customers who would never go looking for a specialised aerial imagery provider on their own. As Erik put it in Google’s announcement:

“Google brings world-class expertise in geospatial technology and AI that is also opening the door to a broader set of non-technical, non-geospatial users.”

That’s the whole game.

And the awareness problem? At Geoawesome, helping close that awareness gap is exactly what we’re here to do. Let’s do it together!