Benedetto Proietti, Big Data Architect at Janea Systems, recently hosted a podcast conversation with Robert Cheetham, where they explored the evolving role of geospatial technology in business.

Cheetham, a veteran in both landscape architecture and software development, emphasized a fundamental point: mapping the Earth is far from simple. From tectonic drift to satellite distortion, pinpointing real-world coordinates involves more complexity than most people realize​.

Yet, this complexity gives rise to opportunity. Once mapped, geospatial data opens up a world of possibility for urban planners, delivery networks, and retail services, among others. As Cheetham noted, nearly 80% of all business data contains a geospatial component. Smart software engineering can unlock immense value from it​.

To truly grasp the scope of geospatial innovation, it helps to break the field down into three elements:

Geospatial, in Three Dimensions:

• Geospatial data (or geodata) is any information that contains location attributes - like a ZIP code, street address, or latitude/longitude. This data describes both where something is and what it is.
• Geographic Information Systems (GIS) are specialized software systems for collecting, analyzing, and visualizing geospatial data. They allow organizations to solve spatial problems and make smarter, location-based decisions.
• Geospatial mapping is the visual layer. It plots all the data on a map to show meaningful spatial relationships. Whether it’s delivery zones, crop health, or climate models, mapping turns abstract information into real-world insight.

What Is Geospatial AI?

Geospatial AI (or GeoAI) combines traditional geospatial analytics with modern artificial intelligence - especially machine learning and deep learning.

GeoAI systems can learn from complex spatial patterns, make predictions, and even automate decisions that once required manual analysis. This powerful fusion helps organizations:

• Speed up workflows through automation, reducing manual effort and operational costs.
• Predict future trends like shopping behavior, supply chain delays, or urban expansion.
• Improve analytical accuracy while reducing human error.

From retailers optimizing store locations to governments modeling natural disasters, GeoAI is reshaping industries by making geospatial intelligence smarter, faster, and predictive.

Geospatial vs. GeoAI: What's the Difference?

At first glance, traditional geospatial analytics and GeoAI may look similar. Both deal with maps, coordinates, and spatial data. The difference is that GeoAI adds an element of automation and prediction. Geospatial analytics explains the past and present. GeoAI anticipates the future.

Geospatial analytics relies on classic statistics. GeoAI uses machine learning to learn from patterns, deep learning to interpret complex data (like satellite imagery), and real-time processing to evolve with each new input.

Table 1 compares geospatial analytics and geoAI side-by-side:

Aspect	Traditional Geospatial Analytics	GeoAI (Geospatial AI)
Methods	Statistical models, rule-based queries	Machine learning, deep learning
Automation	Mostly manual	Highly automated
Predictive Power	Limited	Strong (forecasting, trend prediction)
Adaptability	Static (batch processing)	Dynamic (real-time updates)
Use Case Example	See where sales happened last month	Predict where sales may happen next month

Table 1: Traditional Geospatial Analytics versus GeoAI

Example: A retailer using geospatial analytics might identify high-traffic areas in the city. With GeoAI, they could go a step further: predict which areas will become high-traffic next quarter, and adjust prices or stock levels accordingly.

The Time Is Now: Market Rushes to Embrace GeoAI

Geospatial AI isn’t a niche anymore. It’s going mainstream fast.

According to a recent study (see Figure 1), the global Geospatial Analytics Artificial Intelligence Market is forecast to skyrocket from $60.4 billion in 2025 to over $470 billion by 2034, growing at an eye-popping 25.7% CAGR over that period. For context, the market already hit $47.7 billion in 2024, showing strong early momentum.

This surge reflects what we’re already seeing: enterprise demand for location-intelligent, AI-driven systems is rapidly growing, from supply chain optimization to climate risk assessment.

The question is no longer if businesses will adopt GeoAI - but how fast they can deploy it to outpace the competition.

The Technology and Trends Behind GeoAI

The power of GeoAI stems from the combination of geospatial technologies and artificial intelligence.

The diagram below (Figure 2) shows this relationship and highlights where these fields connect to drive smarter, location-aware services.

Technologies

Table 2 below explores further how each of these technologies contributes to GeoAI:

Enabling Technologies

Technology	How It Powers GeoAI
Coordinate Systems	Enables standardization of how we locate things on Earth with frameworks like WGS84 and EPSG:3857.
Remote Sensing	Collects Earth observation data from satellites and drones for analysis of terrain, agriculture, infrastructure, and more.
Deep Learning	Extracts patterns from complex geospatial inputs to recognize terrain types, crop health, etc.
Machine Learning	Learns spatial patterns and makes predictions based on satellite imagery, GPS tracks, and geotagged social data.
Natural Language Processing (NLP)	Pulls insights from geotagged data, such as tweets, news, photographs, and more.
Computer Vision	Detects and classifies land features or changes over time via imagery.
Geospatial Foundation Models	Consist of specialized deep learning models (like Prithvi-100M or Segment Anything) pre-trained for geo-specific tasks.

Table 2: Technologies supporting GeoAI

Trends

Emerging consumer and industry trends also converge to support the development and adoption of GeoAI with real-time location data (Table 3):

Trend	Why It Matters
IoT Expansion	Billions of connected devices, such as smartphones, wearables, and vehicles, constantly generate and broadcast location.
Location Services	From delivery apps to social media check-ins, location sharing is now ubiquitous.

Table 3: Trends supporting GeoAI.

GeoAI Across Industries

Geospatial AI isn’t a one-size-fits-all tool. It’s a powerful technology capable of reshaping how different businesses work, plan, and grow.

Every industry can couple GenAI with real-time geospatial data and make reliable predictions based on demographic shifts, customer habits, weather and traffic conditions, and more. Table 4 shows just a few examples:

Industry	GeoAI Use Cases
Retail	Identify ideal new store locations based on spending patterns and population growth.
Transportation & Logistics	Reroute delivery trucks in real time to avoid congestion and delays.
Real Estate	Analyze housing trends and map investments in relation to schools and public transit.
Smart Cities & Urban Planning	Monitor heat islands, optimize traffic flow, and predict how development will affect land use.
Agriculture & Environmental Science	Oversee crop health, forecast yields, and manage water use.
Insurance & Risk Assessment	Detect early fire risks with real-time satellite data, vegetation maps, and wind forecasts.
Energy & Utilities	Manage water and energy resources by analyzing rainfall, groundwater reservoirs, and water flow patterns.

Table 4: GeoAI use cases across industries.

How and Why to Go GeoAI with Janea Systems

GeoAI sits at the crossroads of two demanding disciplines: location intelligence and AI engineering.

But as Robert Cheetham noted in our recent podcast conversation, great developers often outperform geospatial experts when building mapping applications. Why? Because engineers with just enough domain knowledge can deliver exceptional results without needing to become geospatial specialists themselves. The reverse, however, is rarely true.

At Janea Systems, we take that insight seriously. That’s why we hire world-class AI engineers with hands-on experience in both AI and geospatial.

Here’s a look at a few projects that showcase our expertise:

Selected Projects and Use Cases

• Bing Maps
  We helped Microsoft automate geocoding error corrections in Bing Maps and re-engineered their deep learning pipelines. The result? 50× faster AI and 7× faster training​.
• Azure Maps
  When Microsoft needed to extend Azure Maps with customizable indoor navigation, we delivered a fully integrated solution wth custom API calls and a fully functional, user-friendly ​front-end layer.
• AI-Ready Collections Platform
  We designed and implemented a future-proof, ML-enabled data architecture for this fintech client. The solution included a secure Delta Lake with full support for SCD Type 2 tracking - ready for predictive modeling and AI analytics at scale​.
• AI Fact-Checking
  For a social-impact startup, we rapidly built an exploratory prototype of an LLM-powered fact-checking tool. Using a segmented architecture with off-the-shelf components, we helped validate their business concept in three months, at a fraction of the cost of custom AI model development​.

Let Janea Systems Put You on the Map

GeoAI is powerful, but only if it's built right. Janea Systems brings together elite AI engineers and real-world geospatial experience to help you move fast and build smart.

Ready to turn location data into business intelligence? Let’s talk.