Carsten Brinkschulte, CEO of Dryad Networks, is revolutionizing wildfire detection by combining IoT technology with artificial intelligence to catch fires at their earliest stages. Drawing on 25 years of experience scaling telecommunications startups, Brinkschulte cofounded Dryad Networks to address the growing wildfire crisis and its impact on climate change.
The company’s innovative approach centers on “digital nose” sensors that detect fires through smell rather than vision. Unlike satellite and camera systems that only identify fires once they’ve grown large, Dryad’s Silvanet wildfire sensors use gas detection technology sensitive to hydrogen, carbon monoxide, and volatile organic compounds. The breakthrough lies in the built-in AI that analyzes fire patterns using distributed edge computing, enabling early detection during the smoldering stage before flames appear.
Developing this technology required overcoming significant technical challenges. While building the physical sensors happened relatively quickly, training the AI took years. Each forest and tree species produces different chemical signatures when burning, requiring Dryad to gather data from customers worldwide and conduct controlled burns of tree samples in their laboratory to build comprehensive training datasets.
Power management presented another critical hurdle. Replacing batteries in millions of forest-deployed sensors every two years isn’t practical, so the team engineered devices with 10-15 year battery life, supplemented by solar panels and supercapacitors instead of lithium-ion batteries to eliminate fire risk.
Communication infrastructure posed the final challenge. Forests typically lack mobile network coverage, so Dryad developed solar-powered mesh gateways that connect sensors via IoT networks and relay information to cloud platforms. The comprehensive technology stack—encompassing electronics, embedded software, cloud infrastructure, and AI—required a team of seven cofounders and took three years to develop, plus another 18 months to prepare for scaling.
Funding green tech hardware proved challenging, but Dryad found success with impact investors by quantifying their environmental impact. The company calculated that wildfires produce 5-8 billion tons of CO2 annually and projected how their sensors could prevent significant portions of these emissions. This data-driven approach demonstrated both financial viability and measurable environmental impact.
Looking forward, Dryad plans to expand beyond fire detection, potentially adding sensors for environmental risk assessment, tree and soil health monitoring, and prevention of poaching and illegal logging.
Key Quotes
We built a ‘digital nose,’ a gas sensor sensitive to hydrogen, carbon monoxide, and volatile organic compounds, with built-in AI to detect fire patterns in a distributed, edge-computing manner.
Carsten Brinkschulte explains Dryad Networks’ core innovation—using AI-powered chemical sensors rather than visual detection to catch wildfires at the smoldering stage before they become uncontrollable.
Building these sensors happened relatively quickly — but training the AI to work reliably took years. Every forest and tree species smells different when they’re burning, so we’ve gathered data from customers around the world and even burned tree samples in our lab.
Brinkschulte reveals the time-intensive nature of developing reliable AI models for environmental applications, highlighting the complexity of training algorithms to recognize diverse fire signatures across different ecosystems.
When working toward our fundraising goal, we took months to calculate how many global CO2 emissions come from wildfires — a staggering 5 billion to 8 billion tons a year — and then project how much of that could be prevented with our sensors.
The CEO describes how Dryad quantified their environmental impact to attract impact investors, demonstrating that green tech companies must provide measurable data on both financial returns and climate benefits.
Our Take
Dryad Networks exemplifies how edge AI is becoming essential for environmental protection. The decision to process fire detection algorithms locally on sensors rather than in the cloud demonstrates sophisticated understanding of deployment constraints—forests lack connectivity, making edge computing not just preferable but necessary.
What’s particularly noteworthy is the multi-year AI training timeline. While consumer AI applications can often launch with imperfect accuracy, environmental monitoring systems require near-perfect reliability since false negatives could mean catastrophic fires and false positives waste emergency resources. This case study should temper expectations about rapid AI deployment in critical infrastructure.
The broader implication is that AI for climate tech represents a massive untapped market. As Brinkschulte suggests, the sensor network infrastructure could support multiple applications beyond fire detection, creating a platform for comprehensive forest management. This positions AI not as a standalone solution but as foundational technology enabling ecosystem-scale environmental monitoring.
Why This Matters
This story represents a crucial intersection of AI technology and climate crisis mitigation. Wildfires contribute 5-8 billion tons of CO2 emissions annually, making early detection systems not just environmentally beneficial but economically critical. The application of edge AI for environmental monitoring demonstrates how artificial intelligence can move beyond consumer applications to address existential threats.
The multi-year AI training process highlights an often-overlooked challenge in deploying machine learning for real-world environmental applications: the need for diverse, comprehensive datasets that account for natural variation. This case study provides valuable insights for other companies developing AI-powered environmental monitoring systems.
The success in securing impact investment also signals a maturing green tech funding landscape where investors recognize that hardware-intensive AI solutions require different capital structures than software-only ventures. As climate change intensifies wildfire risks globally, AI-driven early detection systems could become critical infrastructure, potentially spawning an entire industry of environmental AI applications that protect natural resources while generating sustainable returns.
Recommended Reading
For those interested in learning more about artificial intelligence, machine learning, and effective AI communication, here are some excellent resources: