Wildfire management has evolved from fire lookout towers and camera networks to AI wildfire detection and operational intelligence. Each generation has improved how organisations detect, understand and respond to wildfire incidents.
Summary
Europe’s 2026 wildfire season highlights a challenge that organisations around the world increasingly face. As wildfire conditions become more complex, the challenge is no longer only detecting fires quickly. It is providing operators with the information they need to verify, understand and respond effectively. This evolution has transformed wildfire management from simple observation to operational intelligence.
Across southern Europe, thousands of people have been forced to evacuate as major wildfires burn through Greece, Spain, France, Italy and other Mediterranean regions.
Successive heatwaves, dry vegetation and strong winds have created conditions where small ignitions can rapidly develop into large, fast-moving wildfires. Similar challenges have also been experienced in Australia, North America and many other fire-prone regions over recent years.
While every wildfire has different causes, one lesson continues to emerge. The challenge of wildfire management is becoming increasingly complex.
For decades, the primary objective of wildfire detection was simple: detect fires as early as possible. Today, early detection remains essential, but successful wildfire management depends on everything that follows.
Modern organisations must also verify detections, assess weather conditions, understand potential fire behaviour, evaluate risks to people and assets, coordinate resources and maintain situational awareness as conditions continue to change.
In other words, wildfire management has evolved from asking a single question to supporting an entire sequence of operational decisions.
Understanding how that evolution occurred helps explain where wildfire management is heading next.
How has wildfire management evolved?
Wildfire management has evolved through several generations of technology. While the methods used to detect fires have changed dramatically, the goal has remained the same: provide decision makers with the information they need to respond as quickly and effectively as possible.
Although modern AI has transformed wildfire detection, the industry’s evolution began long before computers.
Each generation solved one problem while revealing the next.
The first generation: Fire lookout towers
Long before cameras and artificial intelligence, wildfire detection relied on trained observers stationed in fire lookout towers. Their task was simple but critically important. Watch the landscape and identify signs of smoke.
Once smoke was observed, the lookout would estimate the fire’s location, report its direction and support dispatchers by providing as much information as possible.
The fundamental operational question was straightforward.
Is there a fire?
If the answer was yes, the next challenge became determining where it was and how responders could reach it. Although these systems depended entirely on human observation, they established the operational workflow that still exists today.
- Detect.
- Locate.
- Respond.
The second generation: Cameras extend human vision
Fixed and pan-tilt-zoom cameras allowed organisations to monitor larger areas continuously without relying solely on personnel stationed in lookout towers.
As communication networks improved, cameras gradually became an important part of wildfire monitoring. Instead of placing people on remote towers around the clock, operators could monitor multiple camera locations from central control rooms.
This provided several important advantages.
- Larger areas could be monitored.
- Operators could access multiple viewpoints.
- Images could be recorded for later review.
- Cameras could operate continuously during daylight and, with suitable technology, into low-light conditions.
However, human operators still needed to watch camera feeds continuously.
The challenge had shifted. Rather than finding enough lookout observers, organisations now needed people capable of monitoring dozens of cameras simultaneously. Fatigue, distraction and the sheer volume of imagery made detecting every smoke plume increasingly difficult.
The next evolution would address that challenge.
The third generation: Artificial intelligence changes wildfire detection
AI transformed wildfire detection by continuously analysing camera imagery and automatically identifying smoke from a fire, allowing operators to focus on verification and decision making instead of constant observation.
Rather than replacing operators, AI fundamentally changed their role. Instead of spending hours watching live camera feeds, operators could concentrate on investigating detections, confirming incidents and coordinating responses.
Yet even with significant improvements in detection speed, organisations soon recognised another challenge.
Receiving an alert was only the beginning.
Operators still needed to understand what that alert meant.
The fourth generation: Operational intelligence
As wildfire operations became more complex, organisations realised that detecting a fire quickly was no longer enough. Operators also needed the information required to understand an incident and decide what to do next.
Artificial intelligence transformed how quickly and consistently organisations could detect potential fires.
However, detecting a fire is only the beginning of the operational response.
Once an alert is received, operators immediately need answers to a series of additional questions.
- Where exactly is the fire?
- Can the detection be verified?
- What are the current weather conditions?
- How is the fire likely to behave?
- Which communities, infrastructure or natural assets could be affected?
- What is the most appropriate operational response?
Answering these questions requires more than a detection system.
It requires operational intelligence.
What is operational intelligence?
Operational intelligence is the integration of real-time operational information that helps organisations verify incidents, understand changing conditions and make informed operational decisions.
Modern operational environments may combine:
- AI wildfire detection
- Live camera feeds
- Weather information
- GIS mapping
- Environmental layers
- Historical imagery
- Incident information
- Operational tools
Individually, each source provides valuable information.
Together, they create a much clearer understanding of what is happening and support faster, more informed operational decisions.
The future of wildfire management
Every generation of wildfire management has solved the greatest operational challenge of its time. The next generation will focus on helping organisations make faster, better-informed decisions.
- Fire lookout towers extended human observation.
- Camera networks expanded monitoring across larger landscapes.
- Artificial intelligence automated the detection of potential fires.
- Operational intelligence is transforming what happens next.
Rather than simply identifying potential fires, modern platforms are increasingly helping organisations understand incidents, coordinate responses and maintain situational awareness throughout an event.
The future is unlikely to be defined by a single breakthrough technology.
Instead, it will depend on how effectively organisations integrate detection, environmental information, mapping and human expertise into a single operational workflow.
The organisations that respond most effectively will not necessarily be those with the most technology. They will be those that provide operators with the right information at the right time to make informed decisions.
How should organisations evaluate wildfire management technology?
Organisations should evaluate how well technology supports the entire operational workflow, from early detection through to informed decision making.
As wildfire management continues to evolve, selecting the right technology involves more than comparing detection speed alone.
Important questions include:
- How quickly can a potential fire be detected?
- How easily can operators verify a detection?
- What information is available immediately after an alert?
- Can weather, mapping and live imagery be viewed together?
- Does the system support situational awareness throughout an incident?
- Can operators coordinate an effective response from a single operational environment?
- Will the platform continue to support future operational requirements?
Early detection remains one of the most important capabilities. However, it should be viewed as the beginning of the operational response rather than the final objective.
How exci supports the evolution of wildfire management
Since its founding in 2019, exci has helped organisations detect wildfires within minutes using AI-powered camera systems.
As customer needs have evolved, so too has wildfire technology. Organisations increasingly require more than early detection. They also need the ability to verify incidents, understand changing conditions and make informed operational decisions from a single operational environment.
That evolution has shaped the ongoing development of exci’s technology.
exci combines AI wildfire detection with live camera feeds, weather information, GIS mapping and operational tools that help organisations build situational awareness throughout an incident.
Rather than replacing human expertise, the goal is to provide operators with the information they need to make better decisions when every minute counts.
Image: exciMap showing detection assessment and playback
Conclusion
Europe’s 2026 wildfire season is another reminder that wildfire management continues to evolve.
As wildfire conditions become more challenging and operational demands continue to increase, organisations need more than faster detection alone. They need technology that supports the entire operational workflow, from identifying a potential fire through to making informed operational decisions.
The history of wildfire management tells a clear story.
- Fire lookout towers extended human observation.
- Camera networks expanded monitoring across larger landscapes.
- Artificial intelligence automated the detection of potential fires.
- Operational intelligence is helping organisations understand incidents more quickly and respond with greater confidence.
Each generation has solved the greatest operational challenge of its time.
The next advances in wildfire management will not come from a single technology, but from how effectively information is brought together to support the people responsible for protecting communities, critical infrastructure and natural environments.
Key takeaways
- Wildfire management has evolved through four major generations: fire lookout towers, camera networks, AI wildfire detection and operational intelligence.
- Every generation has addressed the biggest operational challenge of its time.
- Early detection remains essential, but it is only the first step in an effective operational response.
- Operational intelligence combines multiple sources of information to help organisations verify incidents, assess risk and make informed decisions.
- The future of wildfire management depends on integrating technology and human expertise into a single operational workflow.
Frequently asked questions
What is operational intelligence in wildfire management?
Operational intelligence combines AI detection, live camera feeds, weather information, GIS mapping and operational tools to help organisations verify incidents, assess risk and make informed operational decisions.
Why is early wildfire detection important?
Early detection provides valuable time to verify an incident, assess conditions and begin an appropriate response before a fire grows in size and complexity.
How has wildfire management evolved?
Wildfire management has progressed from human observation in fire lookout towers to camera networks, AI-powered wildfire detection and integrated operational intelligence that supports decision making.
Is AI replacing wildfire operators?
No. AI assists by continuously analysing imagery and identifying potential fires. Human operators remain responsible for verification, assessment and operational decision making.
What is the difference between wildfire detection and operational intelligence?
Wildfire detection focuses on identifying potential fires. Operational intelligence builds on that detection by integrating additional information, such as weather, mapping and live imagery, to support operational decisions.
Why is situational awareness important during a wildfire?
Situational awareness helps operators understand current conditions, evaluate potential impacts and determine the most appropriate operational response.
What technologies are used in modern wildfire management?
Modern wildfire management may combine AI-powered camera systems, PTZ cameras, GIS mapping, weather information, communications infrastructure and operational intelligence platforms.
What role does GIS play in wildfire management?
GIS provides geographic context by displaying incidents alongside terrain, infrastructure, environmental layers and other operational information.
Why is integrating information important?
Integrating information reduces the need to switch between multiple systems, helping operators assess incidents more efficiently and make faster, better-informed decisions.
What is the future of wildfire management?
The future lies in combining early detection, environmental information and human expertise into integrated operational environments that support effective decision making.
by Gabrielle Tylor
exci – Early AI Bushfire & Wildfire Detection
28 July 2026