AI In Disaster Response Planning

Today, Nature Communications published our latest research, led by Amit Misra from Microsoft’s AI for Good Lab: a global flood detection model built using 10 years of Synthetic Aperture Radar (SAR) satellite data. It can detect floods through clouds, at night, and in remote areas—filling a critical gap in global disaster data. Already in use in Kenya and Ethiopia, this open-source tool is helping governments respond faster and plan smarter. It’s a powerful example of how AI can drive climate resilience.

☁️ Clouds block satellites. Floods don’t care. Here’s how foundation models are being adapted to see through the storm. During extreme weather events like floods, clouds often block optical satellites from capturing usable data. And that's exactly when timely insight matters most. Originally shared by Heather Couture, PhD, this study tackled this head-on. It adapted the Prithvi foundation model, originally trained on optical imagery, by incorporating Synthetic Aperture Radar (SAR) to detect floods across the UK and Ireland. ✅ SAR can “see” through clouds ✅ Fine-tuning the model with SAR bands boosted flood segmentation accuracy from 0.58 to 0.79 ✅ Even small amounts of local data were enough to adapt the model to new regions This research shows that Earth Observation Foundation Models can be effectively adapted for disaster response, even in data scarce areas and how AI can be useful for real world problems. 🌎 I'm Matt and I talk about modern GIS, AI, and how geospatial is changing. 📬 Want more like this? Join 6k+ others learning from my newsletter → forrest.nyc

AI is now turning decades of "fragmented reports" into a foundation for global resilience. For many climate hazards, the high-fidelity historical data needed to train predictive models simply didn't exist. Today, Google Research is introducing Groundsource to bridge that gap. While we are starting with urban flash floods, the broader opportunity is to create a rigorous scientific baseline for hazards that traditional sensors often miss. By using Google Gemini to synthesise over 25 years of public information in 80 languages, we’ve demonstrated a scalable way to turn unstructured history into actionable intelligence. How this AI-driven methodology scales climate adaptation: 🧩 Solving the Data Gap: It creates a "ground truth" for regions lacking physical infrastructure, ensuring that no community is left behind in the era of AI-driven resilience. 🗺️ A Scalable Blueprint: This framework is a catalyst; while we've mapped 2.6 million flood events, the same methodology can be applied to landslides, heat waves, and other climate-related threats. 🔮 Predictive Power: This research is already powering 24-hour lead times for flash flood alerts on Flood Hub, giving cities a critical head start. By open-sourcing this benchmark, we are inviting the global sustainability community to help turn the records of the past into a more resilient future. https://lnkd.in/eSRvneuE #ClimateResilience #Sustainability #GoogleResearch #FlashFlood #Gemini #Adaptation

Exciting to see the release of Google's WeatherNext 2, which is an advanced AI model for medium-range weather forecasting! In an era of climate change, high-quality weather forecasts can be the difference between preparedness and disaster. Given the complexity of weather modeling, AI has been a game changer in terms of the ability to create timely, accurate, and high resolution forecasts. Here are some of the features of WeatherNext 2: ✨ Accelerating Speed and Resolution: WeatherNext 2 generates forecasts 8 X faster and at a higher resolution (down to 1-hour intervals) than its predecessor. ✨ Superior Accuracy: Overall, WeatherNext 2 surpasses the previous state-of-the-art WeatherNext model on 99.9% of variables and lead times (0–15 days). ✨ Modeling Uncertainty for Resilience: The model introduces a new approach, the Functional Generative Network (FGN), which predicts hundreds of possible weather outcomes. This is crucial for meteorologists who need to plan for a range of scenarios, especially the most important "worst-case" outcomes. ✨ Research to Reality: Google is making the forecast data available in Earth Engine and BigQuery and launching an early access program on Google Cloud’s Vertex AI platform for custom model inference. The technology is now upgrading forecasts in Search, Gemini, Pixel Weather, and Google Maps Platform’s Weather API, with integration coming soon to Google Maps. It's a great example of the way AI can be used to support greater climate resilience for supply chains and communities alike. Read more about WeatherNext 2: https://lnkd.in/dfqGShGp Read paper: https://lnkd.in/dC5yTWEP Earth AI: https://lnkd.in/d33UYtae

Flash flooding is becoming more frequent and less predictable across the U.S. In the Appalachian region, communities often get only a few hours of warning, putting lives, infrastructure, and local economies at risk. Through the #IBMImpactAccelerator, IBM is collaborating with the University of Illinois Urbana-Champaign Center for Secure Water to change that, with the project coordinated by Professor Ana Barros from the Civil and Environmental Engineering at Illinois department. Pairing Illinois’ hydrology and precipitation modeling with IBM technologies like watsonx.ai, IBM Cloud for Government, and Cloud Pak for Data, the team is improving rainfall prediction and flood forecasting in complex mountainous terrain. Two key innovations are emerging: 💡Enhanced Precipitation Forecasting, which uses AI to correct errors in leading weather models 💡Flood View, a tool that integrates this enhanced rainfall data with hydrology models, delivering earlier flash flood warnings through an interactive map, alerts, and local watershed insights Flood View is already supporting the U.S. National Park Service (NPS). NPS is using Flood View to strengthen disaster preparedness by planning road and park closures in advance and monitoring specific points of interest across the parks. With more reliable forecasts, extending lead time from roughly six hours to up to 48 hours, communities gain critical time to prepare, protect infrastructure and stay safe. Watch the full video to learn how AI, research, and public-sector collaboration are strengthening climate resilience in the U.S.: https://lnkd.in/eSCVq_VW

Today, I’m thrilled to share some new groundbreaking research on Google’s AI-powered flood forecasting abilities, featured in scientific journal Nature! 📣 ⏱ This breakthrough is 7 years in the making. The Google Research team has worked tirelessly to develop an AI model that can forecast floods at scale, the most common type of natural disaster. 🌊 Now, our AI model can accurately predict floods 7 days in advance. It performs comparably to state-of-the-art global modelling systems, with a 0-day lead time. ❗ This is game-changer because this model could provide more targeted warnings of flood risks, bringing invaluable data to places that need it, including locations where reliable flood-related data is scarce, enabling flood forecasting at global scale. 🇬🇧 Here in the UK, the results can be just as impactful. Our Economic Impact Report states that AI-powered flood forecasting could prevent £165 million in damages every year! 👉 A huge congratulations to Yossi Matias and the Google Research team for making this possible! It's truly exciting to see the impact that AI research can have on pressing global issues such as natural disasters. To discover more about our AI model, read Nature’s research paper/our blog: https://lnkd.in/e2pxXEHz #TechforGood #Sustainability #AI

Precipitation is one of the most challenging variables to accurately simulate in global climate models as it depends on small-scale physical processes. In our latest research published in 𝘚𝘤𝘪𝘦𝘯𝘤𝘦 𝘈𝘥𝘷𝘢𝘯𝘤𝘦𝘴, we describe an advancement in our hybrid atmospheric model, NeuralGCM, which now leverages AI trained directly on NASA satellite observations to improve global precipitation simulations. Key results of this work: 👉 Physics-AI Integration: The model combines a traditional fluid dynamics solver for large-scale processes with AI neural networks that learn to account for the effects of small-scale physics, specifically precipitation. 👉 Improved Extremes: NeuralGCM demonstrates significant improvements in capturing the intensity of the top 0.1% of extreme rainfall events, better representing heavy precipitation than many traditional models. 👉 Long-Term Accuracy: In multi-year simulations, the model achieved a 40% average error reduction over land compared to leading atmospheric models used in the latest Intergovernmental Panel on Climate Change (IPCC) report. 👉 Daily Patterns: It more accurately reproduces the timing of peak daily precipitation, which is critical for hydrology and agricultural planning. We are already seeing the value of this approach in the field. A partnership between the University of Chicago and the Indian Ministry of Agriculture recently used NeuralGCM in a pilot program to help predict the onset of the monsoon season. NeuralGCM is part of our Earth AI program to better understand the physical earth in ways that benefit society. We have made the code and model checkpoints openly available to the community. Read the full details on the Google Research blog by Janni Yuval: goo.gle/4qH63sU Paper: https://lnkd.in/d7E4US4W

A #flood started forming. The AI detected the risk before the city was underwater. Not from social media. Not from emergency calls. From satellite imagery + #Spatial #RAG + #GeoAI. — #Disaster #management today is still mostly reactive. Floods. Landslides. Wildfires. Cyclones. We respond after damage happens. But what if cities and governments could monitor disasters continuously? — This is where Spatial RAG becomes extremely powerful. Imagine asking: “Which regions show highest flood risk in next 12 hours?” Or: “Show settlements affected by river expansion in the last 3 days.” And getting answers instantly. — Spatial RAG Architecture for Disaster Management 1️⃣ Multi-source monitoring The system continuously ingests: • Satellite imagery • Weather data • Drone feeds • River & terrain models • Historical disaster records • IoT sensor streams Everything becomes: geo-referenced + time indexed — 2️⃣ AI-based disaster detection Computer vision models identify: • Flood spread • Landslide zones • Wildfire hotspots • Damaged infrastructure • Water level anomalies Each event becomes a geo-tagged intelligence layer. — 3️⃣ Temporal risk analysis The system compares changes continuously: What changed Where it changed How fast it is spreading Now authorities don’t just see maps. They see: real-time risk intelligence. — 4️⃣ Spatial RAG reasoning layer AI retrieves: • Historical disasters • Terrain data • Population density • Evacuation routes • Critical infrastructure layers Now users can ask: “Which hospitals are at flood risk?” “Which villages may lose road connectivity?” “Which zones need evacuation priority?” — Why this matters For governments and disaster agencies: • Faster response time • Early warning intelligence • Better resource deployment • Reduced human risk • Real-time situational awareness This changes disaster management from: Reactive response → Predictive intelligence — The bigger shift: Spatial RAG is evolving into a real-time reasoning engine for the physical world. Cities. Forests. Infrastructure. And now disasters. — Next I’ll show something even more fascinating: How Spatial RAG can monitor Oil and Gas Pipelines to detect defects and inspect it automatically. Comment "ONG" if you want that architecture. — #GeoAI #SpatialRAG #DisasterManagement #ArtificialIntelligence #RemoteSensing #ClimateTech #SatelliteImagery #ComputerVision #SmartCities #GIS

🌍One map can save thousands of lives. 🌍 Every flood leaves a footprint. But what if we could predict, visualize, and act before disaster strikes? Using ArcGIS, Google Earth Engine, and Python, I built a flood risk model that transforms raw satellite data into actionable insights. ✅ Methodology: Remote sensing + GeoAI + advanced spatial analysis ✅ Real-World Impact: Helps governments, NGOs, and communities plan, respond, and save lives ✅ Big Picture: Turning data into climate resilience The message is clear: 📢 Data is powerful, but only if it reaches decision-makers in time. This is why geospatial science isn’t just about maps — it’s about solutions that protect people and ecosystems. 💡 I’d love to hear your thoughts: 👉 How else can GeoAI & GIS be used to tackle the world’s toughest environmental challenges? 🔁 If you believe geospatial data can change the world, share this post so more people see the power of location intelligence. #GIS #RemoteSensing #FloodMapping #GeoAI #ClimateAction #Sustainability

I spent 20 hours analyzing 5 breakthrough Earth Disasters AI Agents from Stanford, MIT, and NASA's Jet Propulsion Lab. Here's the life-saving architecture that's changing disaster response forever ⬇️ Most AI systems clean up after disasters. 》𝗧𝗵𝗲 𝗕𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵: 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗚𝗲𝗼-𝗔𝗴𝗲𝗻𝘁𝘀 These research teams built geo-agents that triangulate risk by combining three weak signals most systems ignore. Individually these signals mean nothing. Combined, they predicted the 2023 Turkey earthquake 72 hours early in simulations. 》𝗛𝗼𝘄 𝗧𝗵𝗲𝘆 𝗕𝘂𝗶𝗹𝘁 𝗧𝗵𝗶𝘀: 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 ✸ Data Sources & Agent System: ☆ Seismic Agent: Monitors ground movement from LSTM + Transformer models ☆ Satellite Agent: Processes visual changes using computer vision ☆ Weather Agent: Tracks rainfall & temperature via APIs ☆ Historical Pattern Agent: Analyzes past disaster data ☆ Prediction Agent: Combines conflicting signals for ensemble prediction ✸ The Key Insight: ☆ When satellite shows dry land BUT weather predicts heavy rain AND historical data flags flood season = 72-hour warning ☆ Weak signal detection through contradiction analysis ☆ Multi-agent orchestration beats single-model approaches ✸ Tech Stack: ☆ Reasoning LLMs for causal analysis ☆ Groq for real-time processing ☆ LangGraph for agent orchestration ☆ ChromaDB for geospatial embeddings 》𝟱 𝗚𝗲𝗼 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗣𝗮𝗽𝗲𝗿𝘀 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 ✸ 1. GeoChat: Grounded Large Vision-Language Model for Remote Sensing ☆ Key Feature: Conversational querying for geospatial data ☆ Benefit: Non-experts extract insights with natural language prompts ✸ 2. GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks ☆ Key Feature: Standardized benchmarking for geospatial VLMs ☆ Benefit: Robust model evaluation with consistent metrics ✸ 3. RS5M: A Large-Scale Vision-Language Dataset for Remote Sensing ☆ Key Feature: Massive dataset of image-text pairs ☆ Benefit: Fine-tunes models for disaster monitoring tasks ✸ 4. VHM: Versatile and Honest Vision Language Model for Remote Sensing ☆ Key Feature: High interpretability for sensitive applications ☆ Benefit: Builds trust in AI for disaster response and policymaking ✸ 5. EarthGPT: Universal Multi-modal LLM for Multi-sensor Image Comprehension ☆ Key Feature: Multimodal analysis combining multisensor data ☆ Benefit: Integrates diverse datasets for richer insights ≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣ ⫸ꆛ Join My 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝟱-𝗶𝗻-𝟭 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 trusted by 1,500+ worldwide! ➠ Build Geo, Audio, Video & Vision Agents ➠ Master 5 Modules: 𝗠𝗖𝗣 · LangGraph · PydanticAI · CrewAI · OpenAI Swarm ➠ Deploy for Healthcare, Finance, Smart Cities & More 👉 𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗢𝗪 (𝟱𝟲% 𝗢𝗙𝗙): https://lnkd.in/eGuWr4CH