Smarter Bushfire Detection: Finding the Right Tool for the Right Job

Fire Foresight camera on patrol

Bushfires are among the most destructive natural disasters worldwide, and their frequency and severity are escalating due to climate change and increasingly extreme weather events. The impact is profound socially, economically, and environmentally.

In Australia, the 2019–2020 “Black Summer” bushfire season was catastrophic, with more than 24 million hectares burned, 33 direct fatalities, and an estimated 450 additional deaths linked to smoke inhalation 1. Globally, wildfires are equally devastating. In early 2025, the Palisades and Eaton wildfires in Los Angeles destroyed over 12,000 structures and claimed at least 27 lives *2. These tragedies highlight the urgent need for more effective fire detection and prevention technologies.

The Cost of Delay 

Early detection is critical in managing and suppressing bushfires before they spiral out of control. The longer a fire burns undetected, the more difficult, and costly, it becomes to contain. 

Beyond the human toll, bushfires cause long-lasting damage:

Biodiversity loss: During the Black Summer, over one billion animals were killed and more than three billion impacted overall, the largest wildlife loss ever recorded in a single event *3.

Health risks: In January 2020, Canberra recorded the worst air quality index of any major city in the world, with bushfire smoke contributing to severe respiratory and cardiovascular conditions *3.

Economic fallout: Nearly AUD $1 billion in insurance losses were reported from the 2019–2020 fires, with farming and tourism sectors heavily affected. By 2030, the damage-related property value loss from climate hazards across Australia is projected to reach AUD $571 billion *3.

The message is clear: time is the most valuable resource in bushfire management, and detection technology must evolve to keep pace with growing risks.

How to Utilise Technology  

Traditional approaches involved putting people in watch towers with the sole purpose of spotting smoke/ fires. Other methods have included deploying many individual ground-based sensors throughout forests. Sensors provide valuable on-the-ground data and are most effective when integrated with broader monitoring systems, helping to build a more complete picture of developing fire conditions.

For situational awareness, AI-powered cameras like those in the Fire Foresight system offer wide-area coverage, continuous monitoring, and intelligent analysis in real time.

Fire Foresight camera on patrol

Leveraging advances in remote sensing and deep learning, AI cameras can:

• Reduce false positives by distinguishing between actual fires and fire-like conditions such as sunlight reflections or dust.

• Adapt to diverse environments using hybrid deep learning models that combine local and global image analysis.

  
  

This is a significant leap forward compared to traditional ‘eyes on the ground’, making fire detection more scalable, accurate, and cost-effective.

The Way Forward  

Emerging research continues to refine AI-based fire detection. New hybrid models achieve 98.9% accuracy in real-time detection with minimal computing resources *3. Future developments promise even greater resilience, including multi-class detection (distinguishing fire, smoke, and other hazards) and integration with thermal imagery for enhanced precision.

The lesson from Black Summer and recent global disasters is that bushfire risk will only continue to rise. Investing in intelligent, scalable solutions like AI-powered cameras gives us the best chance of detecting fires early and preventing devastation before it begins.

Sources *1. Bukhari, S. M. S., Dahmani, N., Gyawali, S., Zafar, M. H., Sanfilippo, F., & Raja, K. (2023). Optimizing fire detection in remote sensing imagery for edge devices: A quantization-enhanced hybrid deep learning model.

*2. Chan, C. C., Alvi, S. A., Zhou, X., Durrani, S., Wilson, N., & Yebra, M. (2022). A survey on IoT ground sensing systems for early wildfire detection: Technologies, challenges, and opportunities.

*3. Qadir, Z., Le, K. N., Bao, V. N. Q., & Tam, V. W. Y. (2024). Deep Learning-Based Intelligent Post-Bushfire Detection Using UAVs. IEEE Geoscience and Remote Sensing Letters, 21, 1–5. https://doi.org/10.1109/LGRS.2023.3329509