Early Wildfire Detection Using AI: The Dryad Networks Case

Wildfires are becoming more frequent and harder to contain as climate change intensifies. This article shows how AI-powered sensors and data analysis enable ultra-early wildfire detection by identifying smouldering fires before smoke or flames appear, helping responders act faster and prevent large-scale damage.

Introduction

Dryad Networks is a German technology company focused on preventing wildfires through ultra-early detection. Its approach is based on the belief that prevention is more effective than response, and that identifying the earliest signs of ignition can significantly reduce damage to forests, ecosystems, and nearby communities. Instead of relying on visible indicators such as smoke or flames, Dryad’s system detects changes at the forest floor, where fires often begin at the smouldering stage.

As climate change intensifies droughts and heat waves, wildfires are becoming more frequent and harder to contain. Traditional detection methods such as towers, cameras, and satellites typically identify fires only after they are already established. Dryad’s solar-powered sensors detect subtle changes in gas composition when organic material first starts to burn, shortening response times and improving containment. This case examines how field testing, machine-learning development, and collaboration with Omdena are contributing to the advancement of ultra-early wildfire detection.

Scaling Ultra-Early Wildfire Detection

Moving from early prototypes to reliable, real-world wildfire prevention requires more than a working sensor. Ultra-early detection depends on dense deployment, continuous monitoring, and the ability to operate across varied forest conditions. Scaling Dryad’s approach meant turning a validated concept into a system that could be manufactured, deployed, and maintained at scale while preserving detection accuracy.

The recent Series A investment enabled this transition by supporting large-scale sensor production, expanded field deployments, and parallel development of hardware and machine-learning systems. Dryad’s progress reflects a broader movement in wildfire prevention, where startups are increasingly applying AI, sensors, and satellite data to detect fires earlier and reduce large-scale damage. This allowed Dryad to move beyond isolated trials and begin building a detection network capable of operating across regions with different climates, terrains, and fire behaviors. In practical terms, scaling made it possible to detect smouldering fires earlier, respond faster, and prevent small ignition events from becoming large-scale wildfires.

Testing and Deploying Ultra-Early Wildfire Detection Technology

Dryad is validating its ultra-early wildfire detection system through ten proof-of-concept trials across southern Europe, the United States, and Asia. These trials expose sensors to a wide range of climates and terrains, including dry Mediterranean forests, windy regions, damp Pacific Northwest landscapes, and monsoon-affected areas. Testing across such varied conditions ensures the system performs reliably in real-world environments rather than only under controlled assumptions.

As Dryad moves beyond pilots toward broader deployment, medium-scale rollouts are planned for later this year. Manufacturing partners are producing sensors for the Silvanet network at scale, enabling dense coverage of forest floors where smouldering fires often begin. Large-scale production, combined with strict quality control, allows Dryad to focus on refining detection performance while ensuring long-term reliability in harsh outdoor conditions.

Controlled Burn Testing and Model Training

Sensor validation during controlled burn testing.

Alongside deployment, Dryad conducts controlled burns across Europe to train and validate its sensors and machine-learning models. During these tests, devices are exposed to heat, smoke, and lingering gases from dying embers. Engineers analyse how gas composition changes during early combustion and compare it with normal environmental variation, improving detection accuracy and reducing false alarms.

After a controlled burn in Nuremberg, sensors were left in place to monitor residual gases as embers slowly died out. This post-burn data proved critical for understanding how smouldering fires behave over time and for refining the system’s ability to distinguish harmless environmental shifts from genuine ignition events.

Real-World Validation Through an Unplanned Detection

Smoke drifting through the forest from a hidden smouldering fire

A key validation moment occurred during an exhibition at INTERFORST in Munich. During the event, Dryad’s system triggered an unexpected alert. Initially suspected to be a false alarm, the team contacted local fire services as a precaution. The alert proved accurate: a smouldering fire had survived a prescribed burn and reignited due to shifting winds.

These so-called zombie fires can burn underground for extended periods without visible flames or obvious smoke, making them difficult to detect through traditional methods. By sensing changes in gas composition at the forest floor, the system identified the fire before it became visually apparent, giving responders valuable time to act.

Early Detection Supporting Human Response

Firefighters deploy pumps and gear to suppress the reignited embers

When firefighters arrived, they confirmed that embers hidden beneath organic debris had reignited and quickly extinguished the site with water. The incident was contained before it could spread, preventing further damage and unnecessary air pollution.

This moment highlights the role of ultra-early detection as a support tool rather than a replacement for human expertise. Sensors and algorithms provide early warnings, but successful outcomes depend on rapid communication and the skill of fire services on the ground. Together, early sensing and human response shift wildfire management from reaction to prevention.

Making Ultra-Early Detection Reliable at Scale

Ultra-early wildfire detection depends not only on sensing ignition events, but on correctly interpreting environmental data that changes constantly with weather, soil conditions, and vegetation. Each sensor captures gas readings alongside temperature and humidity, all of which fluctuate naturally. The challenge is separating early signs of combustion from normal environmental variation so that alerts are both timely and trustworthy, especially when systems are deployed across large and diverse forest areas.

To accelerate this capability, Omdena collaborated with Dryad on developing machine-learning models that learn from real field data, including controlled burns, normal forest conditions, and a confirmed smouldering fire incident. The resulting system processes sensor data in real time and classifies signals as early fire activity or normal background conditions, enabling reliable alerts at scale. Continuous updates using new field data allow detection accuracy to improve over time, ensuring the system remains effective as environmental conditions evolve.

Key Outcomes

• Scaling enabled for early detection: €10.5 million in Series A funding supported the transition from pilot deployments to large-scale production and global rollout of ultra-early wildfire detection systems.
• Validation across diverse environments: Ten proof-of-concept trials were conducted across Europe, the United States, and Asia, demonstrating reliable performance in varied climates, terrains, and forest conditions.
• Field-driven system improvement: Data from controlled burns and post-fire monitoring provided critical insight into smouldering fire behaviour, directly informing sensor calibration, firmware updates, and algorithm refinement.
• First confirmed real-world detection: Silvanet sensors identified a smouldering fire during a prescribed burn, enabling firefighters to intervene early and prevent escalation.
• Reliable signal classification at scale: Machine-learning models developed through collaboration with Omdena enabled accurate classification of early fire signals versus normal environmental conditions, supporting real-time alerts across large sensor networks.
• Continuous performance optimisation: Ongoing integration of new field data allows detection accuracy to improve over time, ensuring the system adapts as environmental and climate conditions change.

Conclusion 

Dryad Networks’ journey illustrates how innovation, collaboration, and sustained investment can advance early wildfire detection in meaningful ways. As climate change intensifies droughts and heat waves, the need for prevention-focused solutions is becoming increasingly urgent. By combining field-tested sensors with data-driven analysis, Dryad has shown how detecting changes at the forest floor can identify fires long before smoke or flames are visible, enabling faster and more effective response.

Ultra-early detection shifts wildfire management from reaction to prevention. Catching smouldering fires early reduces suppression costs, protects biodiversity, limits air pollution, and helps keep nearby communities safe. It also provides responders with valuable time to mobilize resources and contain incidents while they remain manageable. Through continued field testing, large-scale deployment, and collaboration with technical partners such as Omdena, Dryad demonstrates how applied AI and sensor technology can support climate resilience and environmental protection at scale.

Ultra-early wildfire detection is one example of how technology-led organizations are reshaping disaster preparedness, alongside innovations in emergency response, risk mitigation, and climate resilience. For readers interested in applying similar approaches within their own organizations, the following 45-page guide outlines practical considerations for responsible AI implementation.