AI for crisis resilience: how Google's multi-hazard forecasting and humanitarian tooling empower builders

Google and the United Nations have released a joint framework on using AI to enhance multi-hazard early warning systems. The initiative spans forecasting, real-time alerting, and post-disaster damage assessment, with tools already deployed during the 2025 hurricane season and in flood-prone regions across Africa. For AI builders, the key takeaway is a set of open datasets, interoperable alerting protocols, and hybrid forecasting models that can be integrated into crisis response workflows.

What happened

The UN report "Leveraging AI to enhance multi-hazard early warning systems" details how Google's AI breakthroughs are being used by governments and humanitarian organizations. During the 2025 hurricane season, the U.S. National Hurricane Center used Google's WeatherNext model, which predicted Hurricane Melissa's historic Jamaican landfall five days in advance. In Nigeria's Adamawa state, UN OCHA launched a Floods Anticipatory Action Programme using Google's river flood forecasts to trigger early interventions like shelter preparation. The NGO GiveDirectly used similar forecasts in Kogi State to deliver cash transfers before flooding.

Google's Flood Hub now covers 2 billion people across more than 150 countries. A pilot with the World Meteorological Organization and national hydrological agencies in Czechia, Nigeria, Uruguay, and Vietnam found that incorporating local streamflow data into global AI models significantly improves forecasts in ungauged areas. Google also open-sourced its Groundsource dataset for urban flash floods and its hydrology modeling framework.

For wildfires, Google developed the FireSat satellite constellation with the Earth Fire Alliance and Muon Space. Three new FireSat satellites launched from Vandenberg Space Force Base. On the alerting side, CAP-based Public Alerts surface warnings from authorities in over 90 countries across Search, Maps, and Android notifications.

Post-disaster, the DISHA damage assessment workflow has been deployed 11 times with UNOSAT. After Hurricane Melissa, it assigned preliminary damage scores to over 385,000 buildings. Following February 2026 floods in Colombia, UNOSAT cross-referenced AI-derived building maps with radar imagery to inform response planning.

Why AI builders should care

For teams building crisis response or geospatial AI products, this initiative demonstrates several patterns worth adopting. First, hybrid forecasting models that blend global AI with local data can improve accuracy in data-sparse regions. The WMO pilot provides a blueprint for how to integrate national hydrological data into global models without losing local control.

Second, the open-sourcing of Groundsource and the hydrology framework means developers can build on Google's work while retaining full ownership of their own data. The Czech Hydrometeorological Institute already built an adapter to use the model in standard workflows.

Third, the Common Alerting Protocol (CAP) integration shows how AI-generated warnings can reach billions through existing distribution channels. Any builder creating a public safety app can tap into these CAP feeds or replicate the pattern for other alert types.

Practical implications

Developers can explore several concrete integrations:

• Data pipelines: Combine satellite imagery, Open Buildings datasets, and local hydrological data to build risk assessment tools. The DISHA workflow shows how to scale building damage analysis from weeks to hours.
• Alerting APIs: CAP feeds are standardized and already used by 90+ countries. Your app can surface these alerts via Search, Maps, or push notifications without building custom ingestion for each authority.
• Open models: The hydrology framework and Groundsource dataset are available for experimentation. If you work with urban flood modeling, these can accelerate your research while keeping local data private.
• Hybrid forecasting: If you operate in ungauged regions, consider combining global AI forecasts with sparse local measurements. The WMO pilot results (to be published soon) will offer more detail on the accuracy gains.

Caveats

The plans and deployments reflect ongoing collaborations and pilots; results may evolve as programs expand. Some outcomes, such as the precise effectiveness of forecasts in all ungauged regions, depend on data quality, local governance, and partner engagement. Not all regions have CAP feeds or wide mobile reach, which can limit alert dissemination in certain contexts. Builders should also note that the FireSat constellation is still in early deployment, and the full wildfire detection capabilities will take time to materialize.