The Global Race Towards Wildfire Detection
Jul 23, 2025
A Global Push, but a National Pause
As climate change accelerates, wildfires are becoming more intense, frequent, and unpredictable. Countries across Europe are racing to integrate advanced technologies to mitigate this growing threat. In Spain, AI-powered satellite systems are being tested to detect heat signatures. Italy is deploying drone surveillance over fire-prone regions. Germany is investing heavily in forest sensor networks that enable early detection and rapid response. These innovations are backed by both private startups and public agencies aiming to safeguard forests, lives, and ecosystems.
In contrast, Canada, the nation home to some of the largest boreal forests in the world and increasingly devastating wildfire, is lagging in technological adoption. While Canadian wildfire response systems rely on traditional surveillance methods like lookout towers, aerial patrols, and weather data modeling, the use of real-time IoT sensors and AI-driven wildfire prediction remains sparse. This gap signals an urgent opportunity for technological intervention.
About BlazeAI
BlazeAI aims to fill that gap, and to do so effectively, it’s essential to examine what global innovators are already doing. One of the most compelling examples is Dryad Networks, a European startup revolutionizing wildfire monitoring through a mesh sensor network. This case study explores how Dryad’s solution works, its real-world impact, and what BlazeAI can learn from its approach.
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Overview of Dryad Networks
Founded in Germany in 2020, Dryad Networks is a climate-tech startup focused on ultra-early wildfire detection using environmental IoT technology. Its vision is clear: detect wildfires before they spread. The core product, Silvanet, is a solar-powered, wireless sensor network designed to detect wildfires at the smoldering stage, which is usually done within 30 minutes of ignition.
Silvanet includes three key components:
Sensors: Devices that monitor temperature, humidity, air pressure, and gas composition (e.g., hydrogen, carbon monoxide) to detect early signs of combustion.
Mesh Gateways: These create a communication network using LoRa (Long Range) wireless protocols that don’t rely on cellular networks, making them ideal for remote forested areas.
Cloud Platform: Data from sensors is transmitted to a cloud dashboard where AI-based analytics and alerting mechanisms are used to notify relevant stakeholders.
The system can cover vast areas while maintaining low energy consumption, and its solar-powered design makes it sustainable and cost-effective over time.
How Dryad Detects Wildfires
Traditional wildfire detection can take hours, often only starting once smoke is visible via satellite or from the ground. By then, wildfires may have already spread significantly. Dryad’s system is designed to spot wildfires in their infancy, particularly at the smoldering phase — the slow burn stage that precedes open flames and visible smoke.
Here’s how the detection pipeline works:
Continuous Monitoring: Sensors continuously sample air quality and temperature at the forest floor level.
Gas Detection: A critical factor in early detection is monitoring for gases such as hydrogen and carbon monoxide, which are emitted during the smoldering stage of a fire.
AI Pattern Recognition: The cloud system uses trained models to distinguish between natural environmental changes (e.g., a rise in temperature) and combustion indicators.
Immediate Alerts: Once a fire signature is detected, alerts are sent via email, text, or connected emergency systems, giving fire response teams a head start.
In field tests, Dryad has demonstrated wildfire detection times as low as 20 minutes — potentially reducing fire damage by 80–90% when paired with rapid response.
Dryad in Action
Dryad’s Silvanet has been deployed in various forests across Europe, the U.S., and parts of the Middle East. Some notable implementations include:
Germany: Pilot projects in Brandenburg’s fire-prone pine forests have successfully detected test burns within minutes, validating the system’s effectiveness.
Greece: In collaboration with local authorities, sensors have been deployed in national parks where high fire risk due to dry, hot summers has led to previous disasters.
California, USA: Partnerships with fire departments and universities have tested the sensors in wildfire-prone regions to support evacuation and containment efforts.
In all these scenarios, the technology has proven reliable and scalable, with some deployments covering tens of thousands of hectares.
Dryad’s approach is not just about detection — it also contributes to forest management by collecting real-time environmental data, which can be used to study microclimates, track soil moisture levels, and optimize reforestation.
Lessons for BlazeAI
As BlazeAI looks to develop a wildfire monitoring system tailored for Canada, Dryad’s model offers several valuable insights:
Local Networks Beat Satellites for Speed: While satellite data is helpful, it lacks the real-time, ground-level insight that sensors provide. Dryad’s mesh network offers a blueprint for how to create a responsive, decentralized system.
LoRa Technology Is Key: Dryad’s use of LoRaWAN allows long-range communication with minimal energy use, making it perfect for remote Canadian forests with poor cellular connectivity.
Smoldering Detection Is Critical: The earlier the system can detect a fire, the more damage can be prevented. BlazeAI should focus on low-level combustion detection, not just visible fire or heat spikes.
Public-Private Collaboration: Dryad partners with governments, emergency services, and researchers. BlazeAI should explore similar collaborations in Canada to improve adoption and funding.
Incorporating these principles could help BlazeAI become a first mover in the Canadian wildfire detection space.
Bridging the Gap in Canada
Canada’s wildfire crisis is intensifying. The 2023 wildfire season was the worst on record, with over 18 million hectares burned. Yet technological adoption remains limited. Unlike Europe, Canada lacks a nationwide strategy for integrating IoT sensors or AI-driven models in wildfire prevention. This is a critical gap BlazeAI is positioned to fill.
With learnings from Dryad Networks, BlazeAI can build a Canada-specific solution that adapts to the unique environmental and infrastructure conditions of this country’s vast landscapes. The first step is to launch small-scale pilots in high-risk regions — British Columbia, Alberta, and parts of Ontario — and then expand through partnerships with local municipalities and environmental agencies.
