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San Jose partners highlight satellite, AI and modeling tools for real‑time wildfire risk mapping
Summary
San Jose State University, CAL FIRE and nonprofit partners described modeling, remote sensing, FIRIS aircraft data and emerging AI tools that city staff say they will integrate to better target vegetation work and enforcement.
Presenters at a San Jose study session described an expanding toolkit of technologies — from high‑performance fire models and RAWS weather stations to infrared aircraft systems and private satellite and AI vendors — that can help the city detect fire conditions and prioritize prevention work.
Dr. Greg Clements, director of the Wildfire Interdisciplinary Research Center at San Jose State University, described numerical fire‑modeling capability hosted at SJSU and explained that "fire weather is defined as hot, dry, and windy, and we get those days all through the summer." He said the university runs an operational, high‑resolution fire model that can simulate smoke concentrations and fire spread, but cautioned the most complex coupled models require substantial computing time and are not a minute‑by‑minute tool for first responders.
Chief Sapien and Deputy Chief James Dobson highlighted operational systems already in use. The city operates a Remote Automatic Weather Station (RAWS) in Almirac Park that transmits temperature, humidity, wind and fuel‑moisture data. The Fire Integrated Real‑time Intelligence System (FIRIS) was described as a fixed‑wing aircraft capability that maps perimeters, detects hotspots via infrared and transmits data to a ground fusion center to distribute to incident commanders.
The Santa Clara County Fire Safe Council said it has piloted satellite and AI vendors and worked with San Jose Water on watershed mapping. CEO Seth Shalott told the council the nonprofit’s role is to help partners "separate the wheat from the chaff" in evaluating commercial products and to provide evidence‑based analysis for grant proposals.
Mayor Mahan and council members asked whether parcel‑level ownership data can be integrated into a digital twin to identify responsible owners for enforcement or targeted outreach. Staff said parcel shapefiles and ownership data exist and that the city can build tile layers overlaying parcels, topography and FHSZ designations, though it will take time to operationalize those layers for enforcement and prioritization.
Presenters urged a combined approach: rapid, “quick and dirty” tools for initial attack and private‑sector products for near‑real‑time fuel‑loading estimates, supplemented by high‑performance coupled models for planning and smoke forecasting.
