Physical AI for Forests: How Dryad is Building the Intelligence Layer for Climate Infrastructure
- Carsten Brinkschulte
- 3 days ago
- 7 min read
The artificial intelligence revolution has been largely invisible: algorithms trained on digital data, generating digital outputs. But the next frontier is physical: AI systems that sense, reason, and act directly in the material world. This is Physical AI, and while most conversations focus on humanoid robots in factories or autonomous vehicles on highways, some of the most critical applications are unfolding in silence, deep in the world's forests.
Physical AI isn't just robots in warehouses. It's AI systems that perceive environmental conditions through sensors, analyze patterns through cloud intelligence, and trigger autonomous responses in real-time. That's exactly the layer Dryad Networks is building for forests, and it's already protecting forests across four continents with 50 active customers and 30,000 sensors deployed.
Beyond the Sensor Label
For years, Dryad has been categorized as a sensor company. The label is understandable but incomplete. Our solar-powered gas sensors, now deployed in over 50 installations across 20 countries, represent merely the sensing layer of something far more ambitious, an industrial IoT network and software platform that transforms forests into intelligent infrastructure.
Each sensor node, equipped with a Bosch gas sensor chip, measures temperature, humidity, air pressure, and crucially, detects the gases emitted by smoldering fires before flames even appear. This data is analyzed by machine learning algorithms (ML) executing on the sensor itself (edge computing). If the sensor AI detects a fire, it streams the gas scan data through our LoRaWAN mesh network to the cloud, where sophisticated AI models trained on vast environmental datasets perform anomaly detection and pattern recognition.
The system doesn't just collect information; it understands context, distinguishes between a campfire and a dangerous ignition, and determines precise locations across vast territories.
The value isn't in the hardware: it's in the intelligence layer. Landowners and utilities receive automated decision-support: risk assessments, early warnings, and actionable insights that transform forest management from reactive firefighting to predictive stewardship. With 30,000 sensors already shipped and major deployments including 10,000 units in France and potential expansion in Europe and Asia, the infrastructure is scaling rapidly.
Proving the Model Across Continents
The transition from pilot to production demonstrates how Physical AI for forests moves from concept to operational reality. Dryad's four foundational partnerships exemplify the breadth of deployment contexts and stakeholder types.
In June 2024, Dryad partnered with Kinéis, a French satellite IoT operator, integrating direct-to-satellite connectivity into Silvanet sensors. This breakthrough addresses the core infrastructure limitation: many of the world's most fire-prone forests lack cellular coverage, making traditional LoRaWAN deployments impossible. The hybrid architecture enables seamless switching between terrestrial mesh networks and satellite uplinks, without sacrificing power efficiency. For utilities protecting transmission corridors through remote terrain and governments defending unmonitored forests, this capability transforms deployment economics. Within three years, Dryad and Kinéis aim to deploy hundreds of thousands of these devices globally, ensuring wildfire detection reaches even the most isolated regions.
In August 2024, Terraformation deployed Silvanet across its 45-acre Pacific Flight restoration site on Hawaii Island's leeward coast. Unlike mature forests, young restoration sites are fire-vulnerable during establishment, a single ignition could destroy years of carbon sequestration work. Terraformation installed 25 sensors monitoring air quality, humidity, and temperature across the plantation, enabling rapid response to combustion detection. This deployment demonstrated that Silvanet functions as ecosystem recovery infrastructure, protecting restoration sites that are foundational to carbon credit permanence.
On December 11, 2023, Mada's 91-sensor deployment in central Lebanon detected an illegal fire within 30 minutes. A farmer burning dry grapevines triggered combustion signatures: hydrogen, carbon monoxide, and volatile organic compounds; that Silvanet's AI identified with 70% confidence at 10:33 AM. Response teams arrived and extinguished the fire before it spread into nearby forest. This wasn't a theoretical exercise: it proved the system works in real operational conditions, delivering precisely the early detection that enables intervention.
In September 2023, the UK's National Trust deployed 50 Silvanet sensors on Marsden Moor in West Yorkshire, protecting a designated Site of Special Scientific Interest and habitat for endangered species including merlin and golden plover. The compact, solar-powered sensors required minimal infrastructure impact, attaching to existing fence posts rather than requiring new installations. This made the system viable for conservation contexts where traditional fire detection infrastructure would be rejected. The Marsden Moor deployment serves as a proof point for landscape-scale conservation partners, with the National Trust indicating potential estate-wide expansion.
These four reference deployments, satellite connectivity solving infrastructure gaps, ecosystem recovery protection, real-world fire intervention, and biodiversity safeguarding demonstrate that ultra-early detection serves diverse stakeholders across geography and organizational mission. Each successful detection feeds new patterns into the AI. Each partnership extends Physical AI infrastructure into different contexts.
The Autonomous Response Layer
Early detection is revolutionary, but true Physical AI requires closing the loop between sensing and action. That's where Silvaguard, our autonomous drone system, enters the scene.
In November 2025, at a demonstration in Eberswalde, Germany, attended by XPRIZE Wildfire judges and wildfire response experts, Dryad unveiled its advanced end-to-end autonomous wildfire detection and suppression system. The demonstration proved a groundbreaking milestone: Silvanet sensors detected a fire within three minutes from ignition, a Silvaguard observation drone autonomously launched from its solar-powered hangar to locate the fire using optical and infrared imaging, and a Silvaguard suppression drone launched automatically and extinguished the flames, all in under 12 minutes with zero human intervention.
This capability collapses response times from hours to minutes. In wildfire prevention, those minutes don't just save property, they save ecosystems, watersheds, and communities. The system's success depends entirely on ultra-early detection: drones can only suppress fires while they remain small enough for water payloads to be effective. That's why Silvanet's ability to detect fires in the smoldering phase, within minutes from ignition, is foundational to the autonomous response architecture.
Dryad is developing Silvaguard with the support of the European Union and is receiving €3.8 million from the European Regional Development Fund. The company is among 15 semifinalists in the XPRIZE Wildfire Competition, which challenges teams to autonomously detect and suppress fires within 10 minutes across 1,000 square kilometers. Our March demonstration came close to that ambitious goal, proving the technical feasibility of end-to-end autonomous wildfire response.
The vision: Silvaguard drones permanently stationed in solar-powered hangars across high-risk forests, automatically deployed the moment Silvanet detects ignition. No waiting for human crews to mobilize. No delays for aircraft to reach remote locations. Autonomous detection, confirmation, and suppression, all within the critical first minutes when fires remain controllable.
Building Core Climate Infrastructure
The next generation of climate technology is rewriting the base layer of infrastructure across energy, mobility, and materials. Forests require the same fundamental upgrade. Without intelligent sensing and response networks, they remain vulnerable points in a warming world: liabilities rather than assets.
Our complete stack: sensors, connectivity, cloud intelligence, autonomous response, positions wildfire resilience not as an afterthought or insurance policy, but as integrated climate infrastructure. This is Physical AI's promise: transforming distributed physical systems into coherent, intelligent networks that can be monitored, optimized, and eventually underwritten in entirely new ways.
Silvanet extends far beyond fire detection. The continuous stream of environmental data: temperature, humidity, air pressure, volatile organic compounds, carbon monoxide, hydrogen; feeds machine learning models that predict fire spread patterns and forecast fire risk days in advance. By analyzing historical fire behavior alongside real-time sensor readings, the system learns how fires propagate under different wind conditions, vegetation density, and moisture profiles. This fire forecast capability enables preemptive resource positioning: authorities can station crews, equipment, and drones in high-probability zones before ignition occurs, rather than reacting after flames appear.
The economic case is compelling. Deploying around 10 million sensors across California's 7.3 million acres of Wildland-Urban Interface, would represent a fraction of the state's $3.6 billion annual emergency fire suppression costs. But the calculation extends beyond suppression economics: with fire spread forecasting, utilities reduce catastrophic liability from transmission-caused fires. Insurance companies price risk with real-time environmental data rather than historical loss ratios. Governments optimize allocation of firefighting budgets by predicting where fires are most likely to occur, escape initial response, or spread into populated zones.
Private forestry operations optimize harvest timing, replanting strategies, and fire breaks based on continuous microclimate monitoring, extending asset lifespan and improving carbon permanence. Reforestation companies like Terraformation use Silvanet not just for fire prevention, but to monitor restoration success and ensure that carbon sequestration objectives are met over decades.
The forest becomes an active participant in its own preservation, generating continuous data that informs detection, response, forecasting, and long-term stewardship simultaneously.
The Physical AI Architecture
Physical AI systems share a common architecture whether monitoring coral reefs, agricultural zones, or urban heat islands: distributed sensing, cloud intelligence, autonomous action. At Dryad, we're proving this model where it matters most: where the next megafire would otherwise begin.
The sensing layer operates under forest canopies, detecting molecular changes at the smoldering phase when intervention remains feasible. Unlike satellite or camera-based systems that detect fires hours after flames appear, gas sensors identify hydrogen, carbon monoxide, and volatile organic compounds at parts-per-million accuracy within minutes of ignition.
The connectivity layer, our patent-pending LoRaWAN mesh network augmented by direct-to-satellite capabilities through partnership with Kinéis, ensures data reaches the cloud even in terrain where mobile operators can't justify infrastructure investment. The distributed mesh architecture extends far beyond the typical 12-kilometer LoRaWAN limit, enabling coverage across vast remote forests.
The intelligence layer applies machine learning models trained in lab settings using forest floor samples from around the world. The AI learns to distinguish genuine threats from false positives, adapting to each forest's unique microclimate. Over-the-air updates mean every sensor benefits from lessons learned across the entire 50-customer network.
The action layer deploys autonomous systems, currently Silvaguard drones, potentially expanding to ground-based robots or automated suppression systems, that inspect, suppress, or coordinate human crews based on real-time threat assessment.
The Road Ahead
This architecture doesn't replace human expertise, it amplifies it. Firefighters receive precise coordinates, aerial reconnaissance, and threat assessments before entering dangerous terrain. Forest managers gain continuous visibility into conditions that previously required manual surveys. Utility operators monitor infrastructure with sensor density that makes vegetation-caused ignitions detectable within minutes rather than hours.
Physical AI for forests represents a template for climate adaptation across domains. The infrastructure layer we're building: sensing, intelligence, autonomous response, applies wherever environmental threats require detection and intervention measured in minutes, not hours. At Dryad, we're proving this model works, protecting forests from California to Spain, from reforestation sites in Hawaii to railway corridors in Canada.
The artificial dryads of Greek mythology lived in symbiotic relationship with their host trees. Our artificial Dryads do the same: monitoring, analyzing, and protecting forests using cutting-edge Physical AI. As climate change accelerates and wildfire seasons intensify globally, that symbiosis isn't aspirational. With 50 installations operational, 30,000 sensors deployed, and autonomous drone suppression demonstrated successfully, it's becoming the new baseline for forest infrastructure in a warming world.
-
Dryad Networks is a Berlin-based climate technology company deploying ultra-early wildfire detection and autonomous response systems across four continents. The company has raised €22 million in venture funding and received the GLOMO award for Best Mobile Innovation for Climate Action at Mobile World Congress 2025.