Google deploys AI to battle extreme weather

Google is turning Search and Maps into a real-time climate radar.

At its Sustainable with Google event, the tech giant detailed a series of AI-driven updates designed to tackle floods, wildfires, and urban heat waves. The goal is straightforward: take complex environmental predictive models and put actionable safety alerts directly on people's phones.

Forecasting river floods and tracking wildfires

Floods remain the world's most frequent natural disaster. Google has been developing its Flood Hub platform since 2018, using AI and geospatial data to forecast riverine floods up to seven days ahead.

Earlier this year, Google expanded Flood Hub to 80 countries, reaching 460 million people across Africa, the Asia-Pacific region, Europe, and Latin America. In August, local agencies in Chile used these forecasts to issue emergency evacuation orders. Now, the service is expanding to the U.S. and Canada, covering over 800 river locations and 12 million residents.

For wildfires, Google's boundary tracker pairs AI with satellite imagery to map fire lines nearly in real time, refreshing data every 15 minutes across fire-prone areas in the U.S., Canada, Mexico, and Australia. In 2023 alone, Google's SOS alerts reached over 30 million people across 120 wildfire events.

Google is also teaming up with the U.S. Forest Service to deliver the biggest update in 50 years to the agency's fire spread model, using machine learning to better predict how blazes shift and grow.

Mapping shade and cool roofs from the sky

Google is also focusing on extreme heat and the urban heat island effect, where concrete and asphalt trap scorching temperatures.

Its Tree Canopy tool uses aerial photography and AI to map city shade, giving municipal planners a clear look at where trees are missing. The feature is expanding to more than 2,000 cities globally. In the U.S., a partnership with American Forests brings this shade data to its Tree Equity Score, covering 260 million people.

Meanwhile, a new Cool Roofs tool analyzes aerial imagery to measure solar reflectivity on rooftops, helping cities pinpoint where reflective white roofs could lower ambient heat. Currently live in four pilot cities, it will roll out to 11 more in the coming weeks, including New York, Nashville, and Melbourne.

Why it matters

Climate modeling is usually trapped inside academic research labs or expensive enterprise software. Google's real strength here is distribution: baking flood alerts and fire perimeters straight into Google Search and Maps, apps that billions already open every day.

For vulnerable communities lacking local physical hydrology sensors, a seven-day AI river forecast can quite literally mean the difference between timely evacuation and catastrophe. For city planners on tight budgets, free high-resolution canopy and roof reflectivity data eliminates years of tedious manual surveys.

That said, high-stakes predictive AI carries obvious operational risks. If a machine learning model miscalculates a flash flood crest or underestimates a fire line's speed due to flawed satellite inputs, the consequences on the ground could be devastating. Algorithms can pinpoint exactly where to plant a tree or coat a roof, but translating aerial heat maps into physical urban cooling still requires municipal budgets that AI cannot write.