AI Techniques For Enhancing Community Resilience

Every year, natural disasters hit harder and closer to home. But when city leaders ask, "How will rising heat or wildfire smoke impact my home in 5 years?"—our answers are often vague. Traditional climate models give sweeping predictions, but they fall short at the local level. It's like trying to navigate rush hour using a globe instead of a street map. That’s where generative AI comes in. This year, our team at Google Research built a new genAI method to project climate impacts—taking predictions from the size of a small state to the size of a small city. Our approach provides: - Unprecedented detail – in regional environmental risk assessments at a small fraction of the cost of existing techniques - Higher accuracy – reduced fine-scale errors by over 40% for critical weather variables and reduces error in extreme heat and precipitation projections by over 20% and 10% respectively - Better estimates of complex risks – Demonstrates remarkable skill in capturing complex environmental risks due to regional phenomena, such as wildfire risk from Santa Ana winds, which statistical methods often miss Dynamical-generative downscaling process works in two steps: 1) Physics-based first pass: First, a regional climate model downscales global Earth system data to an intermediate resolution (e.g., 50 km) – much cheaper computationally than going straight to very high resolution. 2) AI adds the fine details: Our AI-based Regional Residual Diffusion-based Downscaling model (“R2D2”) adds realistic, fine-scale details to bring it up to the target high resolution (typically less than 10 km), based on its training on high-resolution weather data. Why does this matter? Governments and utilities need these hyperlocal forecasts to prepare emergency response, invest in infrastructure, and protect vulnerable neighborhoods. And this is just one way AI is turbocharging climate resilience. Our teams at Google are already using AI to forecast floods, detect wildfires in real time, and help the UN respond faster after disasters. The next chapter of climate action means giving every city the tools to see—and shape—their own future. Congratulations Ignacio Lopez Gomez, Tyler Russell MBA, PMP, and teams on this important work! Discover the full details of this breakthrough: https://lnkd.in/g5u_WctW  PNAS Paper: https://lnkd.in/gr7Acz25

another recommendation that didn't make the op-ed: AI-Powered Debris Estimation for Faster, More Accurate Assessments Current Challenge: The existing debris reimbursement model relies on post-disaster damage assessments, which can be slow, bureaucratic, and often lead to disputes over the actual volume and cost of debris removal. AI Solution: FEMA should develop an AI-driven debris estimation tool that uses satellite imagery, LiDAR, historical disaster data, and machine learning models to predict debris volume immediately after an event. The model could be trained on past disaster events and refined with real-time inputs (e.g., wind speed, storm path, structural damage reports) to generate automated, rapid debris cost estimates. This would allow FEMA to pre-authorize funding within days instead of waiting weeks or months for full damage assessments. Upfront Payments to States Instead of Reimbursement Current Challenge: The reimbursement model requires local and state governments to front the costs, which can strain budgets and delay cleanup. Proposed Reform: Based on AI-generated debris estimates, FEMA could provide states with upfront lump-sum payments rather than relying on a reimbursement system tied to cubic yards of debris collected. This would allow states to mobilize debris contractors immediately instead of waiting for reimbursement approvals. A true-up process could follow, where adjustments are made if actual costs exceed or fall short of estimates. Benefits of This Approach ✅ Faster Recovery: Reduces delays caused by slow reimbursement processes, getting debris cleared quickly to restore infrastructure. ✅ Cost Efficiency: AI modeling can improve cost projections, reducing disputes and fraud associated with overestimated cubic yard measurements. ✅ Better Resource Allocation: States won’t have to wait for FEMA assessments before securing contracts and mobilizing cleanup efforts. ✅ Equity in Funding: Helps underfunded local governments that struggle with cash flow for immediate debris removal efforts.

Using Generative AI to Assist Emergency Managers in Data Collection: With this prototype, we will explore how generative AI can be used to assist emergency managers in data collection and entry, and how it can support disaster response efforts. Emergency managers have a huge responsibility when disaster strikes. One of their primary tasks is to collect and analyze data from the disaster area to determine the extent of damage, prioritize rescue efforts, and make informed decisions. However, the process of data collection and entry can be daunting and time-consuming, especially when done manually. This is where generative AI comes in. GeoTalk essential elements of information (EEI) extractor prototype being developed by Kant Consulting Group, LLC. The prototype uses multiple technologies like generative AI (ChatGPT), LangChain to extract EEI data communications, ask follow up questions and enter the data into Esri Esri ArcGIS Online layer. This provides a valuable resource for emergency managers to use when analyzing disaster situations and making informed decisions. Generative AI is a relatively new field of AI technology that involves creating algorithms that can generate content, such as text or images, based on given parameters. While generative AI is often associated with creative writing, it can also be used in data collection and entry. Emergency managers can take advantage of this technology by using it to input field notes, analyze situation reports, and input data into geographic information systems (GIS). One of the main advantages of using generative AI is its ability to analyze and interpret data at scale. Unlike traditional methods that require emergency managers to manually input data into an incident management system, generative AI can automatically analyze and extract data from a variety of sources, which saves time and makes data entry more efficient. This means that emergency managers can focus on other critical tasks related to disaster response. Moreover, generative AI can help identify inconsistencies and errors in collected data. The algorithms can flag inconsistencies and errors, allowing emergency managers to correct and revise the data as needed. This can lead to more accurate and reliable decision-making by emergency managers. Overall, the integration of generative AI in the field of emergency management is an exciting new development that holds tremendous promise. By utilizing AI technology for data collection and entry, emergency managers will be able to operate more efficiently, make higher quality decisions, and ultimately save more lives. While this is only the beginning of what is possible, we can certainly be excited about the future possibilities for AI technology in disaster response efforts. If you are an emergency manager interested in contributing to the development of this technology, don't hesitate to reach out.

When Earth Strikes, Can Technology Heal? Over the July 4th weekend, Central Texas faced a deadly flash flood. The Guadalupe River rose 26 feet in just 45 minutes, overwhelming entire communities. More than 120 lives were lost, with over 170 still missing — including children at summer camps. It was sudden. It was devastating. It was a moment that exposed the fragility of our systems. But it also spotlighted how Geospatial Technology + AI — or GeoAI — can change the way we respond. Here’s how GeoAI is healing what nature has shattered: -Real-time Damage Assessment Using satellite imagery and AI models, agencies are mapping destroyed buildings, washed-out roads, and flooded zones within hours, not weeks. This empowers rescue teams to act with precision and speed. -Smarter Search and Rescue GeoAI analyzes flood paths, terrain, and population density to identify where survivors might be — even in rural or disconnected areas. -Infrastructure Recovery and Risk Mapping By overlaying flood impact with infrastructure data, governments can plan better, restore faster, and prevent future disasters. -Informed Recovery Planning AI-powered change detection helps assess loss, allocate resources, and simulate recovery under future climate scenarios. Agencies like NASA and platforms like Esri are already delivering flood extent maps and pretrained damage classification models — helping responders make better decisions, faster. As disasters become more intense and more frequent, GeoAI isn’t just innovation. It’s intervention. When the Earth strikes, technology must become the healer.