Main Conference Agenda 2025

California faces over 7,000 wildfires each year, with enormous costs to lives, communities, and ecosystems. Responding faster requires distributed sensing and intelligence that can act in the field where traditional satellites and watchtowers fall short. http://Wywa.ai First Responder is an open-science initiative led with researchers from MIT and CMU, together with industry leaders and policy experts, to design and deploy a scalable wildfire early-warning network. The system combines ultra-low-cost LoRa-enabled chemical sensors with edge AI and vision-language models. These distributed “artificial noses” continuously monitor air for smoke and combustion signatures. When risk thresholds are detected, the sensors activate nearby edge vision systems that confirm wildfire presence and generate real-time alerts for first responders and civic authorities. We will present results from early deployments, highlight the LoRa network architecture and AI model training that make such systems deployable at scale, and discuss how open collaboration across academia, industry, and government can accelerate resilience. The session will include a live demonstration of how edge intelligence can empower communities to act in the earliest, most critical moments of wildfire response.