Google's recent collaboration with the UN and governments shows something important: AI isn't just the future, it's a practical tool to save lives. From forecasts that buy you days to satellite analysis that speeds up aid, technology is changing how we prepare for and respond to disasters.
Forecasting and preparedness: buying time before the worst
Having hours or days of warning can be the difference between an orderly evacuation and a tragedy. Google's WeatherNext model predicted the historic impact of Hurricane Melissa on Jamaica five days in advance, which allowed authorities to alert people and take protective measures.
In Nigeria, programs like the UN's Anticipatory Action in Adamawa use flood forecasts to trigger early measures. NGOs such as GiveDirectly used those same forecasts in Kogi State to deliver cash transfers before the floods, letting families buy sandbags or evacuate in time.
Google's forecasts are available in Flood Hub and cover 2 billion people across more than 150 countries. There are also pilots with the World Meteorological Organization and local agencies in Czechia, Nigeria, Uruguay and Vietnam showing that adding local streamflow data noticeably improves accuracy in basins without gauging stations.
Google has also released the Groundsource dataset for urban flooding and its hydrologic modeling framework. Institutions like the Institute of Hydrometeorology of Czechia adapted the framework to their workflows, a sign that open collaboration can accelerate local solutions.
For wildfires, Google uses satellite imagery in Search and Maps and participates in the FireSat constellation with Earth Fire Alliance and Muon Space. Three new FireSat satellites were launched from Vandenberg, aiming to detect fires faster and in more places.
Fast alerts: getting the right information to the right person
In a crisis, wrong or late information is dangerous. Google distributes Public Alerts using CAP (Common Alerting Protocol), a standard that lets authorities publish alerts that appear in Search, Maps and Android notifications.
These alerts already include data from more than 90 countries and connect people with critical information. In 2025, Google connected users with crisis information on average 10 million times per day.
A clear example: when earthquakes recently affected Venezuela, the seismic alert system on Android—which uses a network of phones as seismometers—warned millions outside the epicenter and gave them valuable seconds to take cover.
Satellite imagery and AI to speed post-disaster response
After a disaster, the priority is to deliver help quickly and accurately. The DISHA tool, developed with Google and used by UNOSAT, combines models like Open Buildings and Building Damage Assessment to analyze satellite imagery. This solution has already been deployed 11 times and saves specialists weeks of work.
After Hurricane Melissa, the system assigned preliminary damage assessments to more than 385,000 buildings to guide recovery. And in the floods in Colombia in February 2026, teams combined AI-derived building maps with flood radar to prioritize responses by humanitarian agencies and government.
Google also integrates climate and geospatial models in the Google Earth AI collection, offering actionable data for response, monitoring and planning.
What governments and organizations should take away
Combining global models with local data works better than either alone. Do you have local data? It can significantly boost accuracy.
Publishing alerts in CAP is a simple step that extends the reach of official warnings. If you're in charge of public communication, this should be on your checklist.
Opening datasets and frameworks makes it easier for countries and agencies to keep control of their data while benefiting from AI advances.
Technology doesn't replace governance or human coordination. It requires investment, clear protocols and partnerships between agencies, academia and the private sector.
Does this mean we're prepared for everything? No. But it points to a practical path: better forecasts, faster alerts and more efficient assessments bring response closer to the people who need it.
The bet is clear: combine data, models and international cooperation so no one is caught off guard by a disaster. That's the resilience we can build today.
