Geospatial Intelligence, Reinvented: Spexi CTO Peter Szymczak on AI’s Next Frontier

Geospatial Intelligence (GEOINT) is emerging as a strategic differentiator in the AI landscape. By combining satellite imagery, sensor feeds, mapping data, and advanced analytics, GEOINT empowers AI systems to move beyond raw processing and understand information within its geographic and temporal context. This fusion transforms raw data into actionable insights about how people, places, and events interact globally, powering innovations in smart cities, logistics, defense, agriculture, and climate resilience.

For CTOs, GEOINT is not a niche capability—it’s a foundational data layer that can drive innovation, strengthen resilience, and build trust across any tech-driven enterprise. Leaders who integrate GEOINT early can differentiate their products, safeguard operations, and demonstrate credibility with regulators, partners, and customers.

But as we embrace the integration of AI in geospatial intelligence, it’s crucial to recognize that this is more than just a technological upgrade. While the opportunities are immense, it is also the responsibility to apply them wisely. Balancing the power of AI with ethical considerations, data accuracy, and the indispensable human element is critical to harnessing its full potential. 

The journey of AI in GEOINT is not just about making intelligence more intelligent; it’s about reshaping our understanding of the world in ways we are just beginning to comprehend.

To gain deeper insights into this transformation, we spoke with Peter Szymczak, Spexi’s CTO. He shared his perspective on Geospatial Intelligence, illuminating the technical challenges, ethical considerations, and future possibilities that shape the evolution of GEOINT.

Q. Thank you for participating with CTO Magazine. It’s great to have you here. Could you start by sharing a bit about your journey as a CTO and what drew you to Spexi?

Szymczak: Thanks for having me!

I joined the Spexi team as a co-founding CTO, bringing a background of over 15 years in web and mobile app development.

What drew me to join the team in co-founding Spexi was the opportunity to take on some of the biggest challenges in aerial imaging— data quality, speed, scale, and environmental impact— and create a new standard to move the industry forward.

At Spexi, we’ve built the world’s largest drone aerial imaging network, helping organizations collect and access the most detailed aerial data to power next-generation use cases.

With a distributed network of drone pilots, we’ve built something that captures imagery 900 times clearer and 200 times faster than traditional satellites and planes.

This data can be used for everything from helping municipalities build smarter cities, monitoring environmental disasters, training real-world 3D games, or pioneering Large Geospatial Models (LGMs), the next frontier of AI.

Geospatial data in advancing AI applications

Q. How critical is geospatial data in advancing AI applications beyond traditional digital datasets?

Szymczak: Geospatial data is critical in powering AI’s next wave of applications.

Traditional data sets like text and images help AI understand the digital world but don’t provide the training data needed to understand the physical world.

Geospatial data provides that missing piece, giving AI a better understanding of where things are, how environments change over time, and how systems interact in the real world.

This data is important for next-gen applications. Advancements in autonomous vehicles, robotics, infrastructure, and disaster response can’t be made relying on internet data or 2D examples. Dynamic physical data is needed to better inform these models.

That’s why, at Spexi, we’re focused on building a network that scales access to this physical data faster and clearer than what incumbents can provide.

Q. How is your organization, Spexi, leveraging geospatial intelligence to improve AI performance?

Szymczak: Spexi feeds geospatial intelligence models by providing consistent, ultra-high-resolution data at the speed and scale AI systems need to improve their performance continuously. Our physical data helps AI models understand real-world environments, make better predictions, and respond more effectively to complex situations.

Q. Can you explain some technical challenges in integrating high-resolution geospatial data into AI pipelines?

Szymczak: One of the biggest challenges of integrating high-resolution geospatial data is keeping it up-to-date and scalable.

Aerial data you might gather from satellites and planes is often low resolution and infrequently updated. AI systems can’t accurately train and rely on the data they’re being fed.

This is challenging, especially because physical systems, like cities, infrastructure, and environmental situations, constantly change. Therefore, you need data that can be updated quickly without compromising quality.

We aim to solve that problem by using drones as a faster and more scalable source of aerial images that can be captured and processed daily. It will continuously feed AI systems at the speed and scale of real-world demands.

Q. How do you see LGMs transforming user experiences compared to current technologies?

Szymczak: With LGMs, user experiences become more intuitive and actionable.

Imagine a construction manager getting real-time site insights instead of static photos, or a city planner who sees up-to-date infrastructure changes without waiting on outdated maps. Instead of relying on generic data, people will get tailored, location-specific information that reflects what’s happening on the ground.

It enables AI to become more descriptive and proactive, where people and businesses can interact with the world in smarter, more immediate ways.

LGMs (Large Geospatial Models)

Q. How do large geospatial models (LGMs) differ from traditional AI models or LLMs regarding design and capability?

Szymczak: LLMs are designed primarily to understand and generate digital data. They don’t understand the physical world.

LGMs can bridge the gap between the digital and physical worlds. They can process location information, changes over time, and provide insights if they can access consistent, high-quality data.

Think of LGMs as Anthropic’s Claude of the physical world, able to answer questions like “What shingles are missing from this roof after yesterday’s storm, and which ones are most important to replace?”

They are a bit more complex and grounded in the real world than LLMs.

Q. Can you explain how LGMs could enhance AR/VR experiences or metaverse interactions?

Szymczak: Instead of entirely designing virtual worlds from scratch or using synthetic data, LGMs can transform real-world environments into digital experiences with high fidelity and up-to-date detail.

In VR/AR gaming, LGMs can create dynamic environments that respond to actual changes in the player’s surroundings, making experiences more immersive and interactive.

LGMs make digital environments more reflective of their real-world counterpoints, enabling a richer, more meaningful experience for users.

Q. Are there examples where AI “street smarts” significantly outperformed traditional AI models?

Szymczak: When AI accesses real-world data, it can make decisions and provide insights that traditional models simply can’t.

Researchers at the British Columbia Institute of Technology and Northeastern University trained a computer vision algorithm on Spexi’s data. They used it to detect extreme fire risks from hidden fuel loads in vulnerable forests and near critical infrastructure like highways and railroads.

The model picked out damaged trees, fallen logs and debris, and dead trees primed to burn to help produce user-friendly, color-coded maps that allowed forest managers and transportation officials to target high-risk areas.

We’ve also partnered with the Canadian government to monitor areas for wildfire hotspots and with other local government bodies to watch for other environmental disasters.

Challenges and ethics

Q. What are the biggest technical hurdles in scaling LGMs for real-world applications?

Szymczak: The most significant technical hurdle is collecting the necessary data to power these models — not just the volume but also the type.

LGMs can’t be scaled with only the data found on the Internet. They need continuous real-world, high-fidelity data streams to understand and interact with physical environments. It is much harder to capture, standardize, and scale.

At Spexi, we’re tackling this problem by building the largest distributed aerial imaging network. LGMs can be grounded in data from the physical world quickly and easily.

Q. How do you ensure privacy and security when AI interacts with geospatial or real-world data?

Szymczak: We take privacy and security seriously from the point of capture.

Once imagery is verified, we use blockchain to record it in a tamper-proof, distributed ledger. This gives buyers and AI systems confidence that the data is authentic and unchanged.

At the same time, we apply strict safeguards to ensure sensitive information is protected.

Future outlook

Q. How do you see LGMs evolving over the next 3–5 years? What could be their long-term impact on AI and spatial computing?

Szymczak: Over the next few years, LGMs will become more practical and widely used, moving from research projects to tools that help people and businesses interact with the real world. They’ll enable faster, more accurate insights for disaster response, infrastructure monitoring, and urban planning.

LGMs are poised to transform the next wave of AI by creating dynamic, real-world models that AI can understand.

We’re going to see applications we couldn’t have imagined before across every industry that relies on physical data. These will enable a future where AI can help shape the world instead of just training on it.

Q. What advice would you give other CTOs or tech leaders exploring AI that bridges digital and physical realities?

Szymczak: My advice is to really focus on the data you’re collecting and using in your models. That’s going to be key in helping to shape AI that bridges the digital and physical worlds. High-quality, timely, and relevant data form the foundation for sophisticated systems that deliver meaningful insights.

Key takeaways for CTOs and other business leaders

Location-aware insights are a strategic advantage

GEOINT adds the “where” and “when” to AI data, enabling more context-driven decisions and competitive differentiation.

Data quality and integration are critical

Success depends on high-resolution and seamless integration of internal and external datasets.

Ethics, privacy, and governance matter

Tech leaders must ensure responsible usage, compliance with data privacy laws, and robust governance frameworks for AI intelligence.

Cross-industry applications open new opportunities

GEOINT is not limited to defense or mapping—industries like retail, insurance, climate tech, and urban planning can all benefit.

Human expertise remains indispensable

While AI accelerates insights, human judgment is critical for interpretation, validation, and ethical oversight.