Wildfire detection startup Dryad Networks netted roughly $6.1 million in convertible financing this week, which it plans to use to bump up international deployment of its sensor system fueled by artificial intelligence.
New and old investors included TELUS Pollinator Fund for Good, STIHL Ventures, Brandenburg Kapital GmbH, and eCAPITAL Entrepreneurial Partners.
- The top line: The Berlin-based startup’s latest funding round represents a preview of its Series B funding round, which is expected to net between $16 million and $22 million later this year. The $6.1 million influx will enable more rapid and widespread deployment of Dryad’s sensor network, which embeds AI into solar-powered sensors that measure various gasses, temperature, humidity, and air pressure to detect fledgling fires.
- The current take: Matt Casey, managing director of Latitude Intelligence, said that while these remote monitoring systems are picking up speed beyond and within the energy sector, the time is particularly ripe for utility interest in AI-enabled wildfire detection systems. “With climate change increasing wildfire risk, and growing energy demand compounded with aging infrastructure further straining the grid, the demand and market for these solutions within utilities is growing rapidly,” he said.
Unlike most other early wildfire detection platforms, Dryad doesn’t rely on visual cues to find a fire. Instead, the ultra-low-power sensors sniff them out.
Dryad uses a large-scale network of wireless sensors to monitor and analyze forest conditions in real time. They constantly track levels of hydrogen, carbon dioxide, carbon monoxide, and other volatile organic compounds at a parts-per-million level.
Accompanying AI analyzes the readings and compares them with the microclimate to accurately detect forest fires and avoid false positives. Information from the network of more than 30,000 sensors across Europe, North America, and Asia is then transmitted to a cloud platform that runs on long-range radio networks, where it is then analyzed and monitored to alert users to budding wildfires.
The sensors work by translating communication from many devices into a common “language” so that they can share data across a network. The tech runs on a low-power, open-standard network protocol to connect the company’s gas sensors through distributed gateways. In practice, this means that Dryad is brand- and sensor-agnostic, enabling large-scale deployment in forests around the world that can be shared regardless of how those sensors collect data.
Dryad’s AI is continuously trained in a lab setting using forest floor samples collected from around the world; the company said it expects that within the first two years of operation, the AI will no longer need to be trained specifically for each new deployment.
Aside from the ultra-early fire detection that enables more prompt response to fires, Dryad says its platform can be used in broader forest management and restoration efforts. The sensors — which attach directly onto trees and can last 10 to 15 years before needing maintenance — collect other environmental data about the forest’s microclimate that can be used by forest owners to optimize forest health over time.
The spread of the firetech market
Wildfires are larger, quicker, and more common than ever, and the firetech industry is growing — and increasingly relying on AI — in an attempt to keep pace.
Earlier this year, the AI-powered asset management startup AiDash raised $50 million; the company is beginning to expand beyond its vegetation management roots, and is attracting utility interest for its asset inspection capabilities. These tools allow AiDash, as well as other major players like the startup Overstory, to use visual inspections to keep track of changes in the landscape — and reducing wildfires is an added bonus of the tech’s potential.
Dryad’s gas-monitoring tech, though, is specifically wildfire-focused.
“There are going to be some cases where a utility is looking for a more all-in-one system that has monitoring and broader asset management capabilities in addition to wildfire detection,” Casey said. “Still, for some utilities like PG&E that know first-hand the risk of wildfires, there’s also a large upside to having a more specialized solution like Dryad. With most startups in the space relying on image-based data and systems to power their solutions, Dryad’s sensors can provide utilities an added layer of assurance with multiple layers of redundancy.”
The San Francisco-based startup Pano AI also offers wildfire detection-specific tech, but relies on high-resolution cameras and AI data analytics to detect fires before humans report them. The company — as well as a California state-facilitated detection program — relays that information to emergency response centers, and mobilizes firefighting resources, to ultimately prevent small fires from mushrooming into infernos.
Still, visual systems relying on satellite imagery or infrared mapping can take hours or even days to spot fires. Dryad’s sensors can smell fires as early as the smoldering stage of a wildfire, or the first 60 minutes.
This, said Casey, is a potentially valuable add-on to the systems that utilities already rely on.
“While Dryad’s technology can operate as a standalone solution, the complementary benefits of its sensor-based system and open platform make it an intriguing option to also incorporate with existing vegetation and asset management systems, even if they already incorporate wildfire detection capabilities,” he said.
Latitude Intelligence is soon to publish its first report on the use of AI by utilities. This joint research program with Indigo Advisory Group is a first-of-its-kind study of the pathways to adoption of AI-based solutions in the power sector. Through multiple interviews with utilities across the US, from investor-owned utilities to public power, this research uncovers how deployment strategies, existing applications, and targeted benefits are evolving. Sign up here to be notified when the report is released.
