From Wildfire Detection to Fast Mapping: AIVE AI Systems Expands Access to Geospatial Data

The startup is developing tools that create georeferenced 2D maps from a small number of images, without requiring specialized infrastructure.

INTERGEO brings together established geospatial companies and startups exploring new approaches to mapping. At the 2026 event in Munich, AIVE AI Systems demonstrated how artificial intelligence could simplify complex mapping tasks.

The company’s software creates fast, georeferenced 2D maps from drone imagery. It targets users who need geographic context but may not need a detailed 3D model.

From Wildfire Response to Mapping Software

AIVE grew from work on FLARE-X, a research team led by The University of Texas at Austin. The multi-institution team advanced to the finals of the XPRIZE Wildfire Autonomous Wildfire Response competition.

FLARE-X developed an autonomous drone system for wildfire risk mapping, detection and suppression. Its technology combined drones, infrared sensors and software to locate emerging fires.

“We looked at what we had: we had IP on drones, sensors and software – we decided that software was where we could make the most impact,” said Ryan Gupta, Product Lead at AIVE.

The team saw that the software could support many uses beyond wildfires.

“We wanted to geolocate fires – but the software has a wide horizontal of use cases,” Gupta said. “From infrastructure inspection, public safety, to agriculture or border patrol – all of these people want to understand what’s happening in their environment.”

A Faster Route to a Usable Map

AIVE’s first products are Atlas GEO Cloud and Atlas GEO QGIS. The software creates a georeferenced 2D map using a relatively small number of images.

This approach serves a different need from a detailed 3D model. A full model may support precise measurements or complex site analysis. In other cases, users simply need a fast overhead view tied to real-world coordinates.

Traditional mapping workflows may rely on real-time kinematic positioning, known as RTK, or ground control points. Those tools support precision, but they also require equipment, planning and expertise.

AIVE takes a different approach. Its software searches across broader reference areas until it can place the imagery within a geographic context. According to the company, this automates work that users may otherwise perform manually.

The distinction is important. A simultaneous localization and mapping, or SLAM, system can map an environment relative to the drone. A georeferenced map connects that environment to a known real-world location.

AIVE aims to make this capability more accessible to smaller businesses, farmers and disaster response teams. These users may not have advanced mapping equipment or trained geospatial staff.

“If you want the power to build yourself a high fidelity map and analyze it, we give you that capability without requiring a high level of training, infrastructure or investment,” Gupta said.

The mapping engine could also support automatic change detection and AI-assisted analysis. Users could eventually ask the system to identify changes or locate new objects within a mapped area.

The technology may also address future regulatory needs. The FAA’s proposed Part 108 rule would require drone manufacturers to protect aircraft from intentional electronic interference.

AIVE is working on Atlas LOCATE, to continuously map an area and determine a drone’s position without relying only on GPS. If GPS is jammed or spoofed, the system could help the aircraft continue its mission or return home safely.

For now, AIVE’s central proposition is straightforward: reduce the time and resources needed to turn drone images into useful geographic information. Its presence at INTERGEO reflects the growing demand for mapping tools that extend beyond traditional geospatial specialists.

Read more:

• GeoCue Launches TrueView 550 Aerial LiDAR System at INTERGEO
• The Drone Is No Longer the Point
• Beyond the Drone: What to See at INTERGEO 2026 in Munich